2025 Summer Undergraduate Research Fellowship Project Descriptions

Faculty Mentor: Matthew Crane | Applied Mathematics and Statistics

Project Abstract: 

Nanomaterials synthesized in liquid have remarkable properties that can’t be achieved with other methods, including outstanding properties for quantum networks. However, incorporating nanomaterials from solution into devices is challenging. Materials need to be placed with < 100 nm accuracy and precision for devices to minimize error in quantum information applications. We have recently developed a new optical printer that uses light to print materials from solution and onto a surface.

In this SURF, students will work with the optical printer to establish its accuracy and precision limits. Students will investigate how laser power, substrate, liquid, and polarization influence printing. Based on these results, students will incorporate nanomaterials into devies and evaluate their efficiencies. Successful students will also learn how to synthesize nanomaterials relevant for not only quantum networks but also solar cells and photocatalysts. Students will work closely with both a graduate student and professor throughout the summer.

Student’s role and learning objectives: 

In addition to the activities above, students will attend weekly group meetings to learn about other activities in the group and weekly one-on-one meetings with the professor and a graduate student. Students will work closely with a graduate student to learn all skills. Successful students will also be an author on the resulting paper.

Faculty Mentor: Nathan Lenssen | Applied Mathematics and Statistics

Project Abstract: 

There are various metrics for quantifying if a region is experiencing drought. At least two physical processes need to be accounted for: the amount of rain and the amount of evaporation due to heat. This project will investigate existing metrics of drought and update the information on these metrics on a highly-visited climate data encyclopedia. The project will have two major components: (1) A literature review on existing metrics of drought and aridity (2) a data project in which these metrics are applied globally and compared. The literature review will be included as part of the NSF National Center for Atmospheric Research (NCAR) Climate Data Guide. The data project will involve writing code in python to implement statistical and machine learning methods on large climate datasets. The project may also require that analyses are run on the NCAR supercomputer.

Note: this project will be based out of the NCAR Mesa Lab in Boulder. There is a bus that runs from Golden to Boulder and a shuttle that goes to the Mesa Lab (~1 hr commute from Mines to NCAR by public transportation).

Student’s role and learning objectives: 

Through this project, you will learn how to:
– Conduct a literature review by reading scientific literature, writing summaries of papers, and synthesizing multiple papers into a single review.
– Load, analyze, and visualize climate data in python, using the xarray package to analyze data in NetCDF format.
– Communicate findings in writing and in oral presentations
– Collaborate with a group of interdisciplinary scientists and software developers

Faculty Mentor: Scott Strong | Applied Mathematics and Statistics

Project Abstract: 

This research project investigates the phenomenon of synchronization in coupled pendulums and metronomes, integrating experimental data collection, computer vision, sparse regression, and dynamical systems analysis. First observed by Christiaan Huygens in 1665, synchronization occurs when multiple pendulums or metronomes on a movable platform spontaneously align their motions—swinging either in perfect unison (in-phase) or in perfect opposition (antiphase). While several mathematical models have been proposed to explain this behavior, it remains an open question which model best captures the dominant physical mechanisms driving synchronization.

To address this, we employ computer vision techniques to track and extract motion data from experimental video recordings of pendulums and metronomes. This extracted trajectory data is then processed using a modified version of the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, a sparse regression method that reconstructs governing differential equations directly from measurements. SINDy operates by selecting a minimal set of relevant terms from a large library of candidate functions while avoiding overfitting, yielding an interpretable and generalizable set of equations that describe the system’s dynamics.

This project aims to develop a general framework for identifying governing equations of weakly coupled oscillators by combining computer vision, data-driven modeling, and nonlinear dynamics. The insights gained extend beyond pendulums and metronomes, with applications in fields such as:

– Neuroscience (synchronized neural oscillations),
– Biological rhythms (circadian clocks and heart pacemakers),
– Mechanical and structural engineering (vibration control in coupled mechanical systems),
– Chemical reaction networks (biochemical oscillators and feedback regulation),
– Social and behavioral synchronization (coordinated rhythmic behaviors in human crowds),
– Power grid stability (phase synchronization in electrical networks).

This interdisciplinary approach bridges classical mechanics, nonlinear dynamics, and computational modeling, contributing to an interesting undergraduate entry point to the topic of synchronization in complex systems.

Student’s role and learning objectives: 

## Student Roles

– Design and execute experiments with coupled pendulums/metronomes on movable platforms
– Implement computer vision algorithms to extract trajectory data from video recordings
– Clean and process experimental data for optimal analysis
– Apply and modify SINDy to discover interpretable governing equations
– Compare discovered equations with theoretical models from the literature
– Test the generalizability of discovered models across different experimental conditions
– Create visualizations that illustrate the dynamics and synchronization patterns
– Document findings and prepare research presentations

## Student Learning Objectives

1. Gain an understanding of how to read through academic literature efficiently
2. Learn to apply reflective techniques and record-keeping to support academic research practice
3. Design and execute controlled physical experiments for nonlinear dynamical systems
4. Implement computer vision techniques for scientific measurement applications
5. Understand the mathematical foundations of coupled nonlinear oscillators
6. Evaluate competing mathematical models and analyze their boundaries and limitations
7. Effectively communicate technical research through data visualizations and presentations

## Mentoring Activities

The principal investigator will mentor the undergraduate researcher through weekly meetings that emphasize reporting, reflection, and planning. These sessions will focus on practicing technical and administrative communication while providing guidance on literature reviews, experimental designs, and data analysis approaches. The mentoring plan is designed to help the student become more independent by keeping records to report progress, difficulties, and prospecting efficiently.

Faculty Mentor: Scott Strong | Applied Mathematics and Statistics

Project Abstract: 

Many physical systems are described by robust, localized structures in a field that persist due to constraints imposed by topology. In fluid dynamics and geometric mechanics, such defects often manifest as vortex filaments, whose evolution is governed by singular integral equations. This project will focus on the numerical evaluation of the Biot-Savart integral and adjacent properties, which describes the induced velocity field generated by a vortex filament and how this flow dynamically deforms a space curve over time.

The core goal of this work is to numerically approximate singular integrals and use these computations to track and visualize the evolution of space curves under the influence of their self-induced flow. Specifically, we will:

1. Compute the Biot-Savart integral numerically for discretized space curves, ensuring stable and accurate handling of singularities.
2. Evolve the space curve dynamically using the computed velocity field, updating the curve’s position over time.
3. Develop scientific visualizations to analyze and interpret the geometric evolution of the curve.

This work involves three key components:

– Programming & Numerical Methods: Implementing efficient quadrature schemes for singular integral evaluation, likely requiring adaptive integration or regularization techniques to handle near-singular behavior.
– Geometric Flow Simulation: Using numerical methods (such as Runge-Kutta integration) to evolve the space curve under the Biot-Savart-induced flow.
– Scientific Visualization: Rendering and analyzing the time-dependent geometry of the curve in three-dimensional space, possibly using libraries such as Matplotlib (Python), VTK, or Blender for rendering.

Applications and Broader Impact

Understanding the dynamics of vortex filaments and biological filaments through numerical simulation is important in fields such as:

– Fluid Mechanics: Studying vortex stretching and reconnection in turbulent flows.
– Biophysics: Modeling flagellar wave propagation and self-locomotion of microorganisms.
– Plasma Physics: Simulating magnetic field line dynamics in tokamaks.
– Mathematical Biology: Understanding cilia synchronization and metachronal waves.
– Geometric Mechanics: Investigating the evolution of space curves under self-induced flows.

This project will require computational programming of numerical techniques and visualization environments. It will provide a hands-on approach to applying computational mathematics to a fundamental problem in geometric mechanics and fluid dynamics.

Student’s role and learning objectives: 

## Student Roles
– Implement numerical methods to approximate singular integrals, particularly the Biot-Savart integral for space curves.
– Develop algorithms for stable and efficient quadrature to handle near-singular behavior in integral evaluations.
– Simulate the geometric evolution of space curves under their self-induced flow.
– Compare different numerical approaches to improve accuracy.
– Implement scientific visualization techniques to illustrate curve evolution dynamically in three-space.
– Analyze the stability and convergence of numerical schemes used for singular integral approximations.
– Document findings, benchmark results, and prepare research presentations.

## Student Learning Objectives
1. Develop an understanding of conserved quantities in vortex filament dynamics and how singular integral approximations affect them.
2. Gain experience in designing, implementing, and analyzing numerical algorithms for singular integrals.
3. Learn to critically evaluate the accuracy and stability of numerical methods in computational fluid dynamics and geometric mechanics.
4. Strengthen skills in scientific programming and computational mathematics using tools like MATLAB, Python (SciPy, NumPy), or Julia.
5. Develop expertise in scientific visualization for representing space curve evolution dynamically.
6. Understand the connections between numerical methods, differential geometry, and physical applications.
7. Improve technical communication skills through structured record-keeping, benchmarking, and research documentation.

## Mentoring Activities
The principal investigator will mentor the student through weekly meetings focused on numerical analysis, computational techniques, and theoretical insights. These sessions will emphasize:
– Structured Reporting & Reflection – Encouraging the student to document progress, computational challenges, and numerical benchmarks.
– Technical Guidance – Reviewing code implementations, discussing alternative numerical approaches, and refining problem-solving techniques.
– Theoretical Foundations – Providing context on the relationship between integral approximations, fluid dynamics, and space curve evolution.
– Scientific Communication – Assisting in writing research reports, preparing visualizations, and structuring presentations.

The mentoring approach is designed to balance technical guidance with independent problem-solving, gradually transitioning the student toward self-directed research.

Faculty Mentor: Kevin Cash | Chemical and Biological Engineering

Project Abstract: 

This research project is aimed at improving nanosensor lifetime and function in vivo. Pharmaceutical companies are one of the key contributors to physiological imaging and biomedical monitoring tools. They work primarily with vertebrate research models, so there are fewer customers to support the development of tools for the invertebrate monitoring market. Our long-term goal is to develop tools for invertebrate research similar to those available for vertebrate monitoring, opening up research areas for smaller schools and universities like Mines. The 3-D Printed Small Animal Imager research project is working on the development of a smaller, cheaper, multifunction fluorescent imaging system to study biological processes in small animals. The development of this new-generation imaging technique will facilitate the use of imaging in biomedical research. One of the goals of this project is to make this accessible to other researchers, especially those at smaller institutions where they may not have access to commercial in vivo imaging systems.

Student’s role and learning objectives: 

The student will perform advanced experiments using the In Vivo Imaging System (IVIS), including applying nanosensors, conducting DNA gel electrophoresis, and testing live plants and tissues within the imaging box system. Their work will also involve developing and characterizing Short Wave Infrared (SWIR) nanosensors. Furthermore, the student will contribute to chemical imaging research to improve drug detection methods and produce detailed technical documentation of their findings to contribute to peer-reviewed publications. Students will be mentored by the PI and a graduate student through group and individual meetings as necessary.

Faculty Mentor: Anuj Chauhan | Chemical and Biological Engineering

Project Abstract: 

Sustained drug delivery by patches is of interest for treating a number of diseases. Our lab is primarily interested in ophthalmic and transdermal drug delivery. In both cases it is useful to deliver drugs at rates that can be increased on demand. Our goal is to design contact lenses and transdermal patches loaded with drugs of interest and gold nanoparticles to provide triggered drug delivery. The purpose of gold nanoparticles is to allow heating of the patches or lenses by exposure to green light, which is absorbed by the particles and converted to heat. The increase in temperature of the lenses or patches would increase the rate of drug delivery, and possibly also increase the temperature of the skin which would increase the rate of drug permeation into the body.

Student’s role and learning objectives: 

1. Designing patches and lenses loaded with gold nanoparticles
2. Measure heating transients after exposure to light
3. Measure drug release by the lenses and patches in vitro and ex vivo
4. Develop a model for heating and drug transport

Faculty Mentor: Melissa Krebs | Chemical and Biological Engineering

Project Abstract: 

The growth plate is a complex cartilage structure located at the end of long bones which mediates growth in children. When injured, fibrous bony repair tissue can form, instead of healthy growth plate cartilage, which interrupts the normal pattern of growth and can result in angular deformities or growth arrest. Current solutions for growth plate injuries do not adequately prevent the formation of bony repair tissue and do not regenerate the healthy cartilage, making this a significant clinical issue. The objective of this work is to design injectable alginate/chitosan hydrogels that may aid in the prevention of bony tissue formation. Specifically, this study aims to design a hydrogel system with modification of anti-angiogenic peptides designed to target endothelial cells and mesenchymal stem cells to prevent osteogenic differentiation. We hypothesize that alginate/chitosan hydrogels can be developed and tuned to provide sustained presentation of anti-angiogenic peptides and that its therapeutic effect can be quantified in vitro using metabolite and gene expression assays, such as qPCR. We are looking for a student interesting in helping to fabricate and characterize the in vitro effect of our peptide modified biomaterials.

Student’s role and learning objectives: 

The undergraduate student will help fabricate the biomaterial systems in the lab, and study their interaction with endothelial and mesenchymal stem cells towards impeding angiogenesis and osteogenesis. The undergraduate student will work in direct supervision with a graduate (PhD) student and will also have regular research meetings with the PI to go over project updates. The student will learn techniques in polymer modification, characterization, and cell culture.

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Polymer-nucleic acid complexes (polyplexes) have emerged as promising genetic therapeutic alternatives to viral vectors due to their low cost and versatility. While much research has focused on how the chemical properties of polymers shape their biological performance, as soon as polyplexes enter the body, proteins are adsorbed to the surface. The protein corona reshapes the biological identity of polyplexes, modulates cellular uptake, and dictates in vivo fate (biodistribution, immune responses, and circulation time). This project will utilize a creative suite of analytical techniques to characterize the protein corona formed around polyplexes formed from amphoteric polymers. By creating a library of amphoteric polymers incorporating cationic, anionic, and moderately hydrophobic monomers, rational engineering of protein corona composition can be done. Polyplex will be characterized via AF4, proteomics, and light scattering. Initial studies will use human serum from blood samples as the model biofluid although this approach can be extended to other biofluids (lung fluids, mucus, fetal bovine serum, etc.) as needed.

Student’s role and learning objectives: 

Skills gained: polymer synthesis, air-free technique, Schlenk line, semi-batch polymerization, characterization using nuclear magnetic resonance spectroscopy, dynamic light scattering, static light scattering, size exclusion chromatography, asymmetric flow field flow fractionation. The student will gain unique insights into applying to and succeeding in graduate school as an independent researcher.

Mentoring: The student will work closely with a graduate student to learn basic laboratory skills and safety. Then, the student will be allowed to work more independently using the training they have received.

Qualifications: None required. Some chemistry, molecular biology, and coding may be helpful.

Hours: 8-12 hours per week

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Genetic therapies offer previously unattainable cures for genetic diseases like cystic fibrosis, muscular dystrophy, and sickle cell disease. However, severe intracellular delivery challenges limit their accessibility and affordability. To make gene therapy accessible to broad populations, we need to design safe, effective, and affordable carriers from economical materials. Synthetic polymers are promising materials because of their versatility and scalability, but experimentally determining optimal polymer properties among thousands of potential combinations is time and cost prohibitive. Machine learning can accelerate this discovery process, but its success depends on the rapid generation of large, reproducible, and chemically diverse polymer libraries, something traditional batch synthesis struggles to provide. To address this, we will build and program an automated plug flow reactor capable of high-throughput polymer synthesis. The reactor, built with 3D-printed and machined components, will contain nitrogen separated reaction droplets to synthesize dozens of different polymers within hours. A custom liquid handler will inject reactants into a heated tubing loop embedded in an aluminum block, and at the end of the loop, an automated fraction collector will isolate polymer samples in a carousel. We will use LabVIEW to control the whole system for automatic synthesis. By rapidly producing chemically diverse and reproducible polymers, this continuous flow reactor will enable the iterative design of safe and effective polymeric carriers for gene therapies.

Student’s role and learning objectives: 

Roles and Learning Objectives: The undergraduate student will work closely with a PhD student mentor to design, fabricate, assemble, and test a tubular flow polymerization reactor. The undergraduate student’s primary focus will be on fabrication and assembly, and they will use 3D printing, machining, and other fabrication techniques to build reactor components. The student should have prior experience with CAD software because they will design custom parts and assist in troubleshooting the designs. Throughout this project, the student will gain experience in reactor engineering and polymerization chemistry by learning how reactor design choices influence reaction kinetics. They will also become familiar with LabVIEW programming as they work with the PhD student to automate the system.

Mentoring Activities: The graduate student will meet as needed with the undergraduate student to provide structured guidance and explain tasks. Both students will work together to refine designs, troubleshoot issues, and plan next steps. The PhD student, a chemical engineer, will mentor the undergraduate in reactor design and engineering as they learn how reactor design decisions impact polymerization kinetics. The graduate student also has some experience with LabVIEW programming and will work alongside the undergraduate student to automate the reactor. However, the graduate student has no background in CAD or fabrication techniques, so the undergraduate student will take the lead on these aspects as they work to build the reactor together.

Faculty Mentor: Alex Pak | Chemical and Biological Engineering

Project Abstract: 

In photosynthetic organisms, carbon dioxide (CO2) uptake can be enhanced through so-called carbon concentrating mechanisms (CCM) that increase the activity of the CO2-fixing enzyme Rubisco through co-localization. The pyrenoid is one such CCM observed in some algae and plants where Rubisco condenses into a dense matrix as a membrane-less organelle. The formation of pyrenoids is induced by an intrinsically disordered linker and believed to follow liquid-liquid phase separation principles, although the exact mechanisms are unclear. This SURF project aims to use computational techniques, particularly molecular dynamics simulations and machine learning classification, to design linker sequences that control the assembly, morphology, and ultimately, the efficacy of pyrenoids for enhanced CO2 fixation. Insights from this study will translate into hypotheses for CCM engineering in crops and algae to improve CO2 uptake and increase growth.

Student’s role and learning objectives: 

The student will learn how to:
1) Parse literature, interpret data in figures, and critically assess scientific claims
2) Execute molecular dynamics simulations using high-performance computing resources
3) Analyze molecular dynamics trajectories using Linux- and Python-based programming
4) Propose and test protein mutations for pyrenoid-based functional outcomes
5) Communicate their scientific findings during weekly meetings and a final oral presentation

Faculty Mentor: Carolyn Koh | Chemical and Biological Engineering

Project Abstract: 

Interfacial properties of clathate hydrates are important in carbon capture applications, including carbon dioxide, transportation, storage, and carbon sequestration. Furthermore, the interfacial techniques (interfacial tension, wettability, emulsion stability) are important in a wide range of research areas. The goals of this project are to measure and analyze the interfacial properties of clathrate hydrate systems for carbon capture applications.

Student’s role and learning objectives: 

Weekly meetings with Prof. C. Koh and a graduate student in the hydrate center. C. Koh will advise the student with the formulation, design, methods, analysis of the research in the project.

Faculty Mentor: Colin Wolden | Chemical and Biological Engineering

Project Abstract: 

We develop membranes and catalytic membrane reactors for the production and purification of hydrogen. These composite membranes consist of a thin palladium layer deposited on a porous ceramic or metal support. The support plays a critical role in performance. In this project we engineer asymmetry to low cost supports using a variety of chemo-mechanical techniques at both the micro- and nanoscale. This project involve developing these techniques and characterizing membrane performance through a variety of methods including optical and electron microscopy, porometry, and X-ray diffraction

Student’s role and learning objectives: 

The UG would be primarily mentored on a day to day basis by a PhD student who would teach the various techniques. Students would participate in weekly group meeting where they would discuss their accomplishments and goals for the following week. Student would work with PI to develop dissemination materials in terms of posters or publications.

Faculty Mentor: Melissa Krebs | Chemical and Biological Engineering

Project Abstract: 

Women’s healthcare has long been underserved, with significant gaps in medical device innovation and drug delivery. This project aims to develop an ergonomic tampon-based drug delivery system to address menstrual pain. By integrating bioengineered materials and optimizing drug release mechanisms, this research seeks to enhance comfort, safety, and efficacy in women’s health products. The project will explore material biocompatibility, absorption properties, and potential regulatory pathways to ensure feasibility for future commercialization.

Student’s role and learning objectives: 

The student will actively contribute to the research and development of the novel drug-delivery system by:
Conducting two literature reviews.
The first one includes biomaterials, drug delivery mechanisms, and regulatory considerations.
The second one includes ergonomics, human factors in medical device design, and user experience considerations.
Evaluating different hydrogel and cryogel materials for controlled drug release and biocompatibility.
Assisting in laboratory testing of material properties, including absorption, drug release rates, degradation, and mechanical strength.
Exploring FDA regulations and market analysis for feasibility assessment.
Collaborating with mentors and potential industry contacts to refine the project scope and direction.

The student’s learning objectives include:
Develop a foundational understanding of drug delivery systems and biomaterials.
Gain hands-on experience with material testing and experimental design.
Learn about regulatory pathways and commercialization strategies for medical devices.
Enhance critical thinking and problem-solving skills in biomedical engineering.

Faculty Mentor: Andy Herring | Chemical and Biological Engineering

Project Abstract: 

Electrolysis of water to produce hydrogen from renewable electricity is an extremely attractive to reduce energy dependence on fossil fuels. Development of membrane, ionomer, and electrode materials which maintain high performance with long lifetimes are needed. We have developed and investigated the performance and durability of a series of polyethylene-based ABA triblock copolymer anion exchange ionomer and membrane (AEI, AEM) materials for anion exchange membrane water electrolysis. Integration of electrodes using non-precious metal catalyst with these materials is now needed to see the DOE objective for Green hydrogen production of $1/kg. In this project cobalt oxide will redeposited on Ni foan electrodes and the integration of the AEI with the catalyst studied. Small electrolyzes, 5 cm2 will be built and tested with our AEM.

Student’s role and learning objectives: 

The student will learn how to electrode deposit an electrocatalyst on a Ni electrode.

The student will study ionomer/catalyst interactions with electrochemistry and spectroscopy.

The student will learn to fabricate a single cell electrolyzer and characterize it with electrochemical techniques both DC and EIS.

Faculty Mentor: Ramya Kumar, Adam Humpal | Chemical and Biological Engineering

Project Abstract: 

The therapeutic potential of many drugs is limited by ineffective drug delivery resulting in off-target effects and increased costs of treatment. These delivery challenges can be overcome through careful design of drug carriers, non-therapeutic companions that shepherd drugs to their intended target. Polymers brushes have emerged as a promising method to decorate drug-laden nanoparticles. Especially appealing are stimuli-responsive polymer brushes that can be used to make “smart surfaces” that can respond to their environment. Physiologically, pH is precisely regulated and specific to different regions of the body. Making pH-responsive materials particularly attractive for targeted drug delivery. However, current limitations in the ability to adequately control the pH at which brushes “switch” have hindered their widespread application. In this SURF we will be tuning the pH-response of polymer brushes by altering the local environment of the film through copolymerization. You will gain experience with surface polymerization techniques (ATRP), characterization (liquid cell ellipsometry, water contact angle, FTIR, and quartz crystal microbalance), data analysis, and communication of results. This research will build on the understanding of pH-responsive polymer brushes, thereby potentiating their application in the healthcare space.

Student’s role and learning objectives: 

The student will learn to synthesis polymer brushes on silicon wafers using atom transfer radical polymerization (ATRP) with control over the brush thickness and composition. The student will then characterize these films using ellipsometry and quartz crystal microbalance to measure the pH response of the films. They will also learn how to process the data using python and produce publication quality figures. Additionally, the student will learn theory about polymer brushes and their applications.

Mentoring Activites: 

The student will be closely mentored by a PhD student who will teach the student techniques for synthesis and characterization. The student will meet at least weekly with the PhD student mentor to go over findings and troubleshoot any issues that arise in the research process. There will also be monthly meetings between the student, PhD student mentor, and PI to present results, discuss overall finding, refine figures, and aide in professional development

Faculty Mentor: Kevin Cash | Chemical and Biological Engineering

Project Abstract: 

This project is to determine the impact on varying nanoparticle size for our oxygen-sensitive nanosensors. These particles are typically ~100-200 nm in diameter, and in this work we want to use controlled fabrication approaches (flash nanoprecipitation) to vary the size of the sensors to monitor changes in sensor characteristics (response, lifetime, stability) as a function of size.

Student’s role and learning objectives: 

Analytical characterization (UV-Vis spectroscopy, fluorescence spectroscopy, fluorescence imaging, particle characterization) Particle fabrication (flash nanoprecipitation, solvent emulsification nanoprecipitation) Sensor development and characterization. Weekly lab group meetings, mentoring from current graduate students and other students in the lab, as needed individual meetings or subgroup meetings.

Faculty Mentor: David Halat | Chemistry

Project Abstract: 

Localized high-concentration electrolytes (LHCEs) are a promising class of battery electrolytes that improve stability, ionic conductivity, and viscosity control, making them attractive for next-generation energy storage. However, outstanding questions remain regarding how the choice of electrolyte chemistries influences solvation structures and ion transport mechanisms. This project will use nuclear magnetic resonance (NMR) techniques to investigate these properties at a molecular level. Student will utilize modern techniques and pulse programs within 1H, 7Li, 19F, 17O, and 13C NMR spectroscopy to analyze solvation interactions and chemical environments within LHCEs. Furthermore, pulsed-field gradient (PFG) diffusion NMR will be employed to measure ion diffusivities, while electrophoretic NMR (eNMR) will be utilized to quantify ion mobilities under an applied electric field. By integrating these techniques, we aim to elucidate how various solvent compositions and ion interactions influence transport behavior. The work will significantly contribute to the development of optimized electrolyte formulations for high-performance batteries.

Student’s role and learning objectives: 

The undergraduate researcher will play a significant role in investigating ion transport and solvation structures within the class of LHCE electrolytes by utilizing NMR-based methods, electrochemical techniques and other characterization techniques. Key responsibilities include: (1) electrolyte formulation and development; the student will prepare and handle LHCEs, working with high-purity lithium salts, solvents, and additives inside an argon-filled glovebox to maintain moisture- and air-free conditions; (2) NMR spectroscopy and data collection: the student will characterize solvation structures and chemical environments in the electrolytes, also employing pulsed-field gradient (PFG) i.e. diffusion NMR to measure ion diffusivities and electrophoretic NMR (eNMR) to determine ion mobilities under an applied electric field; (3) the researcher will perform electrochemical impedance spectroscopy (EIS) to measure ionic conductivity and compare it to NMR-derived transport properties; (4) the student will develop skills in analyzing NMR spectra, extracting transport parameters, and using Python, MATLAB, or IGOR for plotting and quantitative analysis; (5) the student will present findings in group meetings, enhancing their ability to discuss research clearly and effectively while receiving constructive feedback. Through this project, the student will gain direct, hands-on experience in advanced electrolyte preparation, NMR techniques, and data interpretation, developing a deeper understanding of ion transport mechanisms in battery electrolytes and acquiring technical skills in electrochemistry and spectroscopy, preparing them for future research or industry roles. Structured mentoring will be provided through one-on-one training, regular progress discussions, and guidance on experimental design; this will occur within the context of the research group and through directed interactions with graduate students working across similar research activities, ensuring a supportive environment.

Faculty Mentor: Michael McGuirk | Chemistry

Project Abstract: 

Sulfated zirconia is a solid superacid used in the refinement of hydrocarbons. However, its synthesis and structure are not well defined. In this project, we aim to refine the synthesis of sulfated zirconia, towards enabling a stronger understanding of its structure and how it interactions with hydrocarbons.

Student’s role and learning objectives: 

– organic and inorganic synthesis
– characterization of crystalline solids
– science communication
– presentations skills
– organization and planning

Faculty Mentor: Samantha Johnson | Chemistry

Project Abstract: 

Capturing carbon dioxide from industrial processes or the atmosphere is difficult, owing to the strong, stable bonds in carbon dioxide. However, some nitrogen containing solvents are able to do this. We aim to use computational modeling to understand how these solvents behave at interfaces (for example, at a free liquid surface exposed to air) and how they are able to capture carbon dioxide at these interfaces. Molecular dynamics and density functional theory, respectively, are the primary tools we use to understand the atomistic interactions in carbon dioxide capture.

Student’s role and learning objectives: 

The student will learn to use a supercomputing cluster, and to run density functional theory and molecular dynamics simulations on these clusters. They will also learn the details of chemical reactions between carbon dioxide and nitrogen-containing solvents, how to read literature pertaining to this chemistry and how to engage in scientific communication through presentations and written literature. The student will be jointly overseen by myself, the PI, and a postdoctoral research who is also working on the project. The student will be expected to participate in group meeting, eventually giving a talk to the group in addition to their SURF presentation. Additionally, the student will have the opportunity to interface with a collaborating group at a Department of Energy national laboratory.

Faculty Mentor: Richard Holz | Chemistry

Project Abstract: 

Nitrile hydratases (NHases) are a group of enzymes that catalyze the conversion of nitriles to amides at room temperature and pressure. NHases perform this reaction at higher efficiency and lower energetic cost than the chemical alternative production of amides, providing a “green” chemistry alternative. NHases are alpha2/beta2 heterotetramers that have either an Fe(III) or Co(III) hexadentate metal center, coordinated by a water, two backbone nitrogen atoms, and three cysteine residues. These cysteine residues are in three different oxidation states; the axial cysteine remains reduced while the two equatorial cysteines are oxidized to sulfenic and sulfinic acids. These oxidation states are strictly required for enzyme activity. Additionally, the process of metalation and maturation of the NHase tetramer is highly unique and proposed to be a subunit swapping mechanism where a metallochaperone protein forms a complex with one of the alpha subunits; the alpha-subunit matures to the metalated and oxidized form and is then swapped into the apo-tetramer to produce the functional tetramer. Since the metallochaperone protein is unique for both the Fe-type and Co-type of the enzyme, and neither have been fully characterized, the summer project will focus on understanding the structure and function of these novel metallochaperones. This work is funded by the National Science Foundation.

Student’s role and learning objectives: 

The student should have experience in expressing enzymes in E. coli, purification methods, and kinetic analysis.

The interdisciplinary approach that includes kinetic, spectroscopic, biochemical, computational, X-ray crystallographic, extended X-ray absorption fine structure (EXAFS), and biomaterials will be utilized.

The PI will assign the undergrad researcher to a graduate student who will oversee day to day activities. The PI will meet with the student once a week and more often if needed. The student will give a presentation on their work at the end of the summer. The overall goal is to have the students name on a publication.

Faculty Mentor: Alan Sellinger | Chemistry

Project Abstract: 

Detection of special nuclear materials (SNMs) at borders and ports of entry is critical to ensuring peaceful use of fissile materials. Plastic and organic scintillators are viable options for first line of detection for these materials due to their low cost and scalability. This project will allow students to design, synthesize, purify and characterize organic and polymer materials for application in ionizing radiation detection. This project is very organic chemistry synthesis oriented and requires use of many characterization equipment, i.e. nuclear magnetic resonance (NMR), thermal analysis (DSC, TGA), optical (uv/vis, fluorescence), mass spectrometry to name a few.

Student’s role and learning objectives: 

Students will design, synthesize, purify and characterize organic and polymer materials for application in ionizing radiation detection. This project is very organic chemistry synthesis oriented and requires use of many characterization equipment, i.e. nuclear magnetic resonance (NMR), thermal analysis (DSC, TGA), optical (uv/vis, fluorescence), mass spectrometry to name a few. Students will report directly to Prof. Sellinger but will work closely in the lab with PhD graduate student mentors.

Faculty Mentor: Shubham Vyas | Chemistry

Project Abstract: 

Per- and polyfluoroalkyl substances (PFASs) are one of the most recalcitrant manmade environmental contaminants that are found in the blood of nearly all Americans. Furthermore, it has been found through an exhaustive study that PFASs cause six different types of cancers, and several ongoing studies indicate numerous other correlations between PFAS exposure and several medical conditions. As a result, the United States Environmental Protection Agency (USEPA) has designated PFASs as hazardous waste. One of the key pathways by which PFASs degrade in remediation techniques is oxidative degradation which leads to perfluoroalkyl radicals. Fundamental properties of perfluoroalkyl radicals as per initial results obtained by an undergraduate and a graduate researcher in my group are dramatically different than alkyl radicals, which suggests that investigation of these properties will lead to techniques by which we can measure perfluoroalkyl radicals in experiments. Therefore, this project will investigate fundamental properties of perfluoroalkyl radicals and compare them against alkyl radical analogues. These investigations will involve computationally obtaining the molecular geometries and then computing fundamental properties of these radicals. Density functional theory calculations with possible targeted experimentations will be performed.

Student’s role and learning objectives: 

Learn computational chemistry techniques, specifically applying density functional theory using high performance computing facilities at Mines.
Compute fundamental properties such as dipole moment, partial atomic charges and spin densities.
Perform literature searches and synthesize currently known information and place the proposed research in the context of current literature.
Learn technical writing skills and scientific presentation skills.

Faculty Mentor: Shubham Vyas | Chemistry

Project Abstract: 

Per- and polyfluoroalkyl substances (PFASs) are oleophobic and hydrophobic substances that are used in a variety of applications due to their extreme stability towards chemical oxidation, reduction and thermolysis. Due to the widespread use of PFASs for several decades, PFASs have found their way in several water streams, soils, food items and aquatic life. As a result, nearly every American has PFASs in their blood. Since nature did not make these chemicals, nature does not know how to break these chemicals which is supported by the fact that PFASs causes six different types of cancers. Recent reports show that PFASs detected in the rainwater is extremely diverse when compared to samples collected from water streams and soils. This indicates that PFASs are undergoing chemical transformations in the atmosphere, a topic which is rarely investigated.

This research project will investigate atmospheric chemical transformation of PFASs via chlorine radicals. Both thermodynamic and kinetic parameters will be calculated using computational chemistry tools. Density functional theory calculations along with bulk scale molecular dynamics simulations will be applied to understand these processes.

Student’s role and learning objectives: 

Learn to utilize high performance computing facility at Mines
Learn to work in Linux environment
Utilize computational chemistry tools to obtain thermodynamic and kinetic parameters
Implement the concepts of chemical kinetics in probing rate constants for chemical reactions
Learn technical writing and scientific presentation skills

Mentoring Activity: The student will meet with the PI once a week or as needed to learn scientific concepts needed to execute the proposed research activities, and also to get feedback on ongoing research activities along with professional development advice. The student will also meet a graduate student mentor on weekly basis to get technical/hands-on experience to help achieve the learning objectives outlined above. During weekly group meetings, the student will also present at least 1-2 times during the summer to the research group. At the end of the summer semester, the student will summarize the research findings in a technical research report.

Faculty Mentor: Yangming Shi | Civil and Environmental Engineering

Project Abstract: 

Construction workers, particularly painters and drywall installers, often require elevation to perform tasks on walls and ceilings. Traditional solutions, such as ladders and fixed-height stilts, pose limitations in terms of mobility, safety, and ergonomic adaptability. This project focuses on designing and prototyping adaptive stilts—a height-adjustable, stable, and ergonomic wearable system that enhances worker efficiency and safety in construction activities like wall painting and drywall installation.

The proposed adaptive stilts will integrate adjustable height mechanisms, shock-absorbing components, and stability enhancements to reduce the risk of falls and fatigue. The design will incorporate lightweight yet durable materials to maintain mobility while ensuring structural integrity. A user-centered approach will guide the development, including input from construction professionals to optimize functionality. The prototype will undergo testing for ergonomics, weight capacity, ease of use, and safety compliance.

By improving mobility and reducing strain on workers, adaptive stilts have the potential to enhance productivity and workplace safety in construction and maintenance tasks. This innovation aligns with industry needs for ergonomic and adaptive solutions in construction equipment, contributing to improved work conditions and efficiency.

Student’s role and learning objectives: 

As an undergraduate research assistant, the student will actively contribute to the design, prototyping, and testing of adaptive stilts for construction work. The student will engage in engineering design, material selection, fabrication, and performance evaluation while considering industry safety standards and user ergonomics. Responsibilities include:

(1) Conducting literature reviews on existing stilts, height-adjustment mechanisms, and ergonomic considerations.
(2) Assisting in modeling and simulation to develop conceptual designs.
(3) Performing material selection based on strength, weight, and durability requirements.
(4) Participating in prototype fabrication using 3D printing, metalworking, or composite materials.
(5) Conducting load testing, stability analysis, and user trials to evaluate performance.

By the end of this project, the student will:

(1) Apply engineering design to develop a functional prototype.
(2) Select materials based on strength, durability, and weight.
(3) Develop CAD models and analyze performance.
(4) Fabricate and test the prototype for stability and ergonomics.

Faculty Mentor: Shilai Hao | Civil and Environmental Engineering

Project Abstract: 

Lithium-ion batteries (LiBs) are widely used in electric vehicles, consumer electronics, and renewable energy storage systems. While spent LiBs are often treated as solid waste, they hold significant potential for recycling because they contain valuable transition metals, such as lithium (Li) and cobalt (Co). Although considerable efforts have been made to recover these metals through chemical engineering processes, the presence and fate of emerging contaminants during recycling have largely been overlooked. One such contaminant is bis-perfluoroalkyl sulfonimides (bis-FASIs), a novel class of per- and polyfluoroalkyl substances (PFASs) used as electrolytes and cathode binders in LiBs. These compounds can be released into the environment during recycling, posing significant risks to ecosystems and drinking water supplies. Developing a method to simultaneously recover valuable metals and destroy emerging contaminants like bis-FASIs is therefore essential.

This project proposes a thermochemical approach—hydrothermal reaction—to recover Li and Co while simultaneously destroying bis-FASIs. Hydrothermal processes have been demonstrated to effectively produce biofuels from biomass waste and degrade PFASs in environmental wastes. By optimizing key reaction parameters such as additives, temperature, and reaction time, this project aims to develop a method that not only recovers valuable resources from spent LiBs but also eliminates associated contaminants. This innovative approach will be crucial for the sustainable recycling of LiBs, addressing both resource recovery and environmental safety in response to the growing global demand for these batteries.

Student’s role and learning objectives: 

The student will investigate hydrothermal treatment for contaminant destruction and critical metal recovery, conducting batch reactions, analyzing results with advanced techniques like high-resolution mass spectrometry, and optimizing reaction conditions.

The student will work with a graduate student mentor to learn the necessary skills and plan out experimental work. The student will meet with the graduate student mentor + faculty mentor biweekly to discuss progress, challenges, and plans for upcoming work.

Faculty Mentor: Amy Landis | Civil and Environmental Engineering

Project Abstract: 

The student will be asked to aid on a NSF funded RARE grant. This research will quantify and evaluate macro, micro, and nano plastic (MMNP) flows throughout different geographic locations with the aim of identifying solutions that have the most impact on the environment and potential for resource recovery. This research will evaluate the effect that policies -like single-use plastic bans in the US and Europe- have on the overall plastic waste problem. This research will begin to curate a database of MMNPs from published data as well as data collected and measured from 4 geographically diverse areas of the US (CA, CO, MI, NY) and 2 countries in the Caribbean (Dominican Republic and Belize). The student will be asked to assist with field work and lab analysis, as well as conducting literature reviews on related topics and understanding models such as material flow analysis. The student will be given the opportunity to focus on one part of the grant, if they wish, and present findings at a conference.

Student’s role and learning objectives: 

The student will take on a learning and assisting role for current graduate students. The undergraduate student will be tasked with help the graduate student conduct field work, lab work, data analysis and result discussion. Additionally the undergraduate student will be given the opportunity to lead one part of the project and conduct their own analysis on data collected with the research group. Undergraduate students will be encouraged to submit abstracts to relevant conferences and write a conference proceeding. This will provide the undergraduate student not only field and lab experience, but also presenting and writing experience. The graduate students will take on the main role of mentoring, with additional mentoring from the advising faculty.

Faculty Mentor: Johan Vanneste | Civil and Environmental Engineering

Project Abstract: 

Increasing stress on water resources due to population increase and climate change augments the pressure for tapping into alternative water resources like brackish groundwater. Many groundwaters in the Front Range are brackish and are currently used for irrigation of low value salt-tolerant crops like alfalfa. In parallel a lot of the agricultural land is being converted for housing development increasing the demand for high quality water. Desalination with reverse osmosis can provide a solution. However, sparingly soluble minerals in the groundwater, like gypsum, can limit water recoveries and membrane life.
When gypsum precipitates upon concentration, it can severely damage reverse osmosis (RO) membranes. Until now no effective sensors have been identified that can detect this precipitation before it impacts the membrane. Several CO groundwaters will be tested on a 20 gallon per minute pilot-scale closed-circuit RO system from Dupont equipped with advanced turbidity sensors on the concentrate which enabled detection of the onset of gypsum crystallization before it affects the membrane performance and this without the use of antiscalants, expensive chemicals often used to retard but not prevent scaling. Even for rapidly changing water quality, scaling can be mitigated by adapting water recovery in real-time. This will reduce membrane cleaning, membrane replacement and labor costs. Moreover, avoiding antiscalants facilitates subsequent gypsum recovery as a fertilizer or building material and supernatant recycling will further increase water recovery.

Student’s role and learning objectives: 

Me and my postdoc as well as PhD student will guide the student in achieving the following learning objectives
– Operate a highly automated desalination system
– How to take water samples, analyze them on-site and submit them for advanced metals and anion analysis
– How to perform jar tests for gypsum/silica recovery
– How to analyze data
– How to write reports for our customer`s at the field sites and support publications with our commercial partner Dupont

Faculty Mentor: Yangming Shi | Civil and Environmental Engineering

Project Abstract: 

We are developing a system that allows a person to effectively “step into” a remote robot’s body through virtual or augmented reality. By wearing a special headset, the operator will see and hear what the robot sees and hears, all in real-time. As the robot moves and interacts with its surroundings, the human operator experiences the environment as though they are physically present. This immersive technology could be a game-changer in fields such as disaster relief, space exploration, and remote maintenance work, where human safety and access are major challenges.

Student’s role and learning objectives: 

1. Gain knowledge of virtual and augmented reality concepts and how they apply to teleoperated robots.
2. Learn how to combine hardware (robotic platforms, sensors, headsets) and software (control algorithms, real-time data streaming) into a cohesive system.
3. Explore methods to improve user experience, focusing on intuitive controls, realistic visuals, and ease of operation.
4. Tackle design and engineering challenges relevant to emergency response, space exploration, or other high-risk scenarios.

Faculty Mentor: Reza Hedayat | Civil and Environmental Engineering

Project Abstract: 

Mine tailings (MTs) are finely ground residuals left after valuable metals and minerals are extracted during ore beneficiation in the mining industry. Globally, over 14 billion tons of MTs have accumulated. Repurposing these materials into sustainable building and construction products offers an innovative solution that mitigates the environmental and human impacts of mining while addressing the construction sector’s sustainability needs.

Our research group is among the few worldwide pioneering the reuse of MTs. We have successfully converted MTs from various sources into durable construction materials. This position will focus on producing aggregates and bricks from MTs, including material characterization and product evaluation. We are also developing an automated process for MT processing, and the SURF student will collaborate with faculty and graduate researchers to advance these technologies. This work is expected to generate high-quality data, potentially leading to joint publications with SURF students.

Student’s role and learning objectives: 

Here are the learning objectives for an undergraduate researcher working on this project:
1. Understand the Fundamentals of Mine Tailings and Their Reuse
o Explain the composition and environmental impact of mine tailings (MTs).
o Describe the potential for reusing MTs in construction materials, aligning with sustainability goals.

2. Develop Laboratory and Analytical Skills
o Learn to characterize MTs through physical, chemical, and mineralogical analysis
o Gain hands-on experience in pre-treatment techniques such as mechanical activation and thermal processing.

3. Explore Geopolymerization Techniques
o Understand the chemistry behind geopolymerization and its role in sustainable brick and lightweight aggregate production.

4. Apply Engineering and Process Optimization Principles
o Investigate process variables that affect the performance of value-added products, including material ratios, curing conditions, and forming techniques.
o Use experimental data to refine and optimize production processes.

5. Enhance Scientific Communication and Research Skills
o Learn to document experimental procedures and results systematically.
o Present findings in written

This experimental project focuses on the characterization of MTs and the development of value-added products in the laboratory. The student will have the opportunity to test these products and assess their viability for market adoption. The principal investigator will hold weekly meetings with the SURF student, providing hands-on laboratory training and guidance on both technical and non-technical aspects of the research. This includes support in report writing, reviewing scientific literature, preparing presentations, and developing technical reports.

Faculty Mentor: Tom Williams | Computer Science

Project Abstract: 

Communication delays are the reality of lunar missions and future Mars missions, and these communication delays can create inefficiencies in task execution that risk increasing crew workload, leading to performance decrements and long-term psychosocial impacts. While studies have examined the effects of delays on communication quality and task performance, they have either been constrained to computer-based activities lacking the complexity and naturalistic environment of expected lunar EVAs or have not quantified impacts on both team dynamics and individual cognitive constructs.
​

This project aims to:
1. Quantify the inefficiencies of communication delays through psychophysiological, neurophysiological, and behavioral measures in a complex analog environment​
2. Develop communications countermeasures (CMs) to provide structure within the unstructured wait time produced by communications delays. ​

Student’s role and learning objectives: 

The student participating in this summer research experience will assist a team of scholars to develop and evaluate LLM-driven tools that help astronauts to brainstorm decisions to difficult challenges and anomalies.

The student will learn key techniques surrounding LLMs and experimental design, and will participate in regular 1:1 and group meetings, including invited talks from scholars from around the world.

Faculty Mentor: Tom Williams | Computer Science

Project Abstract: 

Designers seeking to create lifelike robot motions often take inspiration from robot animation principles, like Disney’s Illusion of Life. However, these principles are not designed for physical artifacts that are bound by the laws of physics. In this project seek to instead explore how puppeteering principles might provide better inspiration for robot design.

Student’s role and learning objectives: 

The involved student will help to create a new framework of puppeteering-inspired robot design principles, create proofs-of-concept robot designs inspired by the principles, and design and conduct experiments to evaluate the benefits of those principles.

The student will gain knowledge in robot design, experimental design, and scientific paper writing.

The student will regularly engage in 1:1 and group meetings, including invited talks from a range of international experts.

Faculty Mentor: Tom Williams | Computer Science

Project Abstract: 

Establishing when, how, and why robots should be considered moral agents is key for advancing human-robot interaction (HRI). For instance, whether a robot is considered a moral agent has significant implications for how researchers, designers, and users can, should, and do make sense of robots and whether their agency in turn triggers social and moral cognitive and behavioral processes in humans. Robotic moral agency also has significant implications for how people should and do hold robots morally responsible, ascribe blame to them, develop trust in their actions, and determine when these robots wield moral influence. Measuring or quantifying moral agency is thus of critical importance for human-robot interaction research. Although there have been some recent attempts to develop scales that might achieve this goal, these approaches do not align with the philosophical literature on machine moral agency, and moreover, mistake agency (which we argue to be an ontological state of being) for a psychological construct. In this work, we thus seek to develop a tool for quantifying moral agency that better aligns with the philosophical literature which offers rigorous frameworks for conceptualizing machine moral agency. Specifically, we aim to create new methods for quantifying Moral Agency in which researchers (1) separately assess the individual core constructs of moral agency: capacity for moral action, autonomy, interactivity, and adaptability (the MIAA scales), and (2) logically combine the outputs of those scales. We will draw upon experimental psychological approaches for construct measure development and merge them with techniques rooted in mathematical logic and philosophical theory for determining robots’ ontological status as moral agents. We will also demonstrate the usefulness of the MIAA scales to assess moral agency of artificial agents and the logical procedures for combining the four constructs measured with the scales in empirical studies.

Student’s role and learning objectives: 

The student participating in this project will explore Dempster-Shafer Theoretic methods for scale combination. They will work closely with PI Williams and participate in lab meetings, including talks from invited speakers.

Faculty Mentor: Estelle Smith | Computer Science

Project Abstract: 

State-of-the-art age verification in online contexts is currently extremely limited. For example, to access many websites and online content, the only protections are pop-up windows asking users to confirm whether they are 18 years or older or for users to create an account in which they must enter their birthday. However, there is nothing to prevent users from lying, which readily leads to situations where minors can access inappropriate, malicious, or damaging content. For example, tragically, there are numerous cases of minors becoming targets of sexploitation or engaging with abusive adults online who are significantly older. The purpose of this SURF project will be to conduct in-depth qualitative research with parents with children between the ages of 9-17 in order to understand parental needs and concerns regarding new approaches to age verification. Specifically, we aim to understand how to design consent processes in which parents can create verified profiles and onboard their children into online spaces or communities with better safeguards for their wellbeing and access to appropriate content. The results of this research may be applied toward the development of innovative new technologies that can be applied in online contexts in the near future (e.g., via a startup company).

Student’s role and learning objectives: 

The undergraduate student will be involved in all aspects of this research project, including literature review, CITI training, recruitment of parents, collection of data (e.g., 1:1 interviews with parents, including additional design activities such as rapid prototyping by sketching interfaces or creating paper prototypes), analysis of data, and contributions to writing scientific paper(s) for publication at top-tier Human-Computer Interaction or Cybersecurity and Privacy conferences.

As a key member of an interdisciplinary research group, you will be embedded in the only Human-Computer Interaction (HCI) research lab at Mines. Throughout your work, you will be mentored by a research team consisting of Professor Estelle Smith, PhD, and Research Associate, Alemitu Bezabih, PhD. Mentorship activities include:

-Participation in in-person weekly team meetings on campus.
-Regular 1:1 mentoring sessions with Drs. Smith and Bezabih.
-Invitation to participate in lab social activities during the summer.
-Support to contribute to scientific paper publications.
-Possibility to attend conferences and present first-authored papers or posters.
-Opportunity to participate in tech startup activities related to this research.

Our goal is to provide an excellent opportunity to learn about research in Human-Computer Interaction. We will provide career guidance, mentorship, support, and networking opportunities, with the potential to help initiate a successful career in research or industry.

Required Qualifications:
-Strong interest in technology design, UI/UX, Human-Computer Interaction and Social Computing.
-Currently enrolled as an undergraduate student, preferably in Engineering, Design and Society; Computer Science; Humanities, Arts, and Social Sciences; or related area.
-Excellent project management skills, attention to detail, and interpersonal sensitivity.
-Self-motivated and able to work independently, as well as to work collaboratively in a team environment.

Additional Possible Qualifications:
-Prior personal experience as a social media user.
-Prior training in design-related coursework, such as Human-Centered Design, or co-design methodologies such as interviews, design workshops, etc.
-Prior exposure to research ethics and CITI training.

Faculty Mentor: Chuan Yue | Computer Science

Project Abstract: 

This research project provides an undergraduate student with hands-on experience in developing an identity and credential management system by utilizing modern cryptographic techniques. As digital services and online platforms continue to grow, secure and efficient management of user identities and credentials has become crucial. In this project, the student will explore and implement methods for creating systems that protect users’ personal information while enabling secure access to digital services. Through practical exercises and research, the student will gain an understanding of key concepts such as encryption, authentication, and trust, and how these principles can be applied to build real-world systems. The goal is for the student to develop a working system that demonstrates the application of modern cryptographic tools, while also learning about the challenges and solutions involved in protecting digital identities. This project provides an opportunity for the student to develop both technical skills and critical thinking, preparing them for future careers in cybersecurity and digital system development. To complement the project, a corresponding cryptographic lab exercise will be developed, allowing more students to gain deeper insights into cryptographic algorithms and their practical applications. This lab exercise will be designed for use in typical cryptography courses, providing students with the opportunity to work with real cryptographic tools and techniques, such as encryption, hashing, and digital signatures, in a controlled and hands-on environment.

Student’s role and learning objectives: 

The student will:
(1) Investigate existing identity management systems and cryptographic techniques to understand their practical applications.
(2) Design and implement a secure identity and credential management system using cryptographic tools like encryption and hashing.
(3) Test the system for vulnerabilities and ensure its functionality, security, and user-friendliness.
(4) Collaborate with other researchers in our team.
(5) Document the design, development, and testing process to present the final results clearly and professionally.

LOs for the student:
(1) Learn key cryptographic concepts and how they secure identity management systems.
(2) Gain practical experience with encryption, hashing, and digital signatures to build secure systems.
(3) Tackle real-world challenges in identity management and cryptography, refining solutions and designs.
(4) Improve teamwork and communication through group activities and project presentations.
(5) Recognize the importance of data protection and secure practices in building robust systems.

My mentoring activities:
(1) Provide foundational knowledge on cryptography and identity management, helping students understand key principles.
(2) Assist in designing the architecture and selecting appropriate cryptographic methods for the system.
(3) Review students’ code, offering feedback on security, performance, and improvements.
(4) Guide students through testing, identifying vulnerabilities, and applying fixes to ensure system security.
(5) Track student progress, provide feedback, and encourage reflection on challenges and lessons learned.

Faculty Mentor: Micah Corah | Computer Science

Project Abstract: 

The ability to explore and map an unknown environment is an important prerequisite for many tasks in robotics. From mapping a house to long term operations at an industrial site or warehouse, exploring the environment can be an important first step before continued operation. Therefore, the primary task for this project will be for the student to implement, integrate, and evaluate an exploration system on a real robot. Classical methods such as frontier exploration will be suitable, but incorporating more advanced concepts such as information gain, environment semantics, or a 3D map would also be appropriate. Through the course of this project, the student will gain experience with common software, hardware, and sensors in robotics domains while also applying methods for path planning, mapping, and control. Depending on the preferences of the student and availability of time, a secondary task will be to implement methods to search the environment for a specified object or navigate to a goal specified by natural language.

Student’s role and learning objectives: 

The student engaging in this project will be provided with an appropriate mobile robot equipped with a depth camera and/or lidar sensor and compute resources to complete this task.

Through the course of this project the student will:
* Study methods for navigation and control for mobile robots, especially methods for active perception and exploration, and apply this knowledge to their own implementation for this search and exploration task
* Work directly with a physical sensor-equipped robot gain experience with tools, controllers, and packages related to that robot’s operation and address challenges related to operating a real robot in a physical environment
* Demonstrate function of the complete search and exploration system in a laboratory environment
* Evaluate the implementation in physical and/or simulation experiments both in terms of direct measures of performance such as cells observed, time to completion, or frequency in which the robot gets stuck or fails at a task

Faculty Mentor: Mehmet Belviranli | Computer Science

Project Abstract: 

Traditional VR-based cleanroom training methodologies (i) lack relevance to the real-life lab environments, (ii) requires manualannotationofthecomponentsinthelaband(iii)necessitatesacomplexsetupofthelearningenvironment. Furthermore, they require an extensive development effort when more training modules are to be covered. In this project, we propose a two-step, scalable and interactive training module delivery framework. Both steps feature neural network (NN) based object detection and segmentation (ODS) algorithms to automatically detect and highlight components of interest (i.e., cleanroom equipment and their parts) in the delivery environment. Automated ODS significantly reduces the effort to add new modules and enables our framework to be adoptable by a wide range of semiconductor manufacturing labs.

1) We will develop an interactive platform where learners could interact with all of the components in a given video frame and retrieve further information on their overall utility and relevance to the specific step of the manufacturing process being demonstrated.

2) We will integrate our automated ODS-based interaction mechanism with the 360-degree videos and deliver the experience via commonly available and affordable VR glasses. The detected components in the video will be highlighted via a 3D overlay, and the interaction (e.g., clicking, panning and zooming) with the components in the video will be enabled through the VR hand controllers, head movements or a virtual pointing device.

Student’s role and learning objectives: 

The student will work with a senior UG student to implement several aspects of the two tasks listed above:
1) Creation of an interactive web based video player.
2) Integrating object detection model into the player. (Object detection model will be developed separately by others)
3) Creation of an Quest app that could play 360 degree videos and let’s user interact with it
4) Integration of OD model into the Quest app
5) Enabling gesture based interaction in the quest app.

The student will be expected to accomplish at least 3 of the 5 tasks above, at the end of the program.

The specific qualifications we are looking for (at least two or more is needed):
– Experience with Oculus app development.
– Experience with React or similar web-based development frameworks.
– Experience with pytorch or similar NN execution frameworks.

There will be weekly meetings with the mentor and another weekly meeting with other students in the project.

Faculty Mentor: Maxwell Brown | Economics and Business

Project Abstract: 

This project would begin to explore the interactions of natural gas exports with the electricity grid. In particular, we would leverage linked models of the US economy and electricity grid to explore how increased natural gas availability would impact the evolution of the power grid and, ultimately, household economic welfare. The scope of the work is fairly general and this is the first stage of a longer-term research project.

Student’s role and learning objectives: 

The project would begin in the summer and the objectives for the student would be to..
(a) summarize existing literature and identify salient research questions around natural gas exports
(b) propose model modifications and scenarios
(c) [progress pending] analyze, summarize, and present results

Faculty Mentor: Yamuna Phal | Electrical Engineering

Project Abstract: 

This summer research project will focus on designing and creating a computer-aided design (CAD) model for an advanced optical system, with the ultimate goal of increasing the Technology Readiness Level (TRL) of the technology. Our team, supported by funding from NASA and NSF, develops optical technologies with applications ranging from space exploration to pharmaceutical research. The project’s aim is to create a modular design for the system, allowing its components to be easily customized, scaled, and adapted for different uses. By developing flexible and interchangeable components, the system will be better equipped to meet various operational needs and progress toward deployable solutions. The student will contribute to prototyping, testing, and validating the system’s performance, ensuring it meets the standards required for real-world applications. This work is a critical step in advancing the technology closer to practical implementation in diverse fields.

Student’s role and learning objectives: 

The undergraduate student will play a key role in the design and development of a modular optical system. Their responsibilities will include creating and refining the CAD model for the system, ensuring that components are flexible, scalable, and adaptable to various applications. The student will collaborate on prototyping, assembling, and testing the modular components to validate their performance and reliability. Additionally, they will engage in troubleshooting and iterating on designs based on testing outcomes while working as an integral part of an interdisciplinary team that bridges expertise in electrical engineering, physics, and industry applications.

Learning Objectives
1. Technical Skills Development: Gain proficiency in CAD software for optical and mechanical design, as well as hands-on experience in prototyping and testing high-precision systems.
2. Problem-Solving Abilities: Learn to identify and address design challenges, such as improving component compatibility and ensuring system modularity.
3. Research and Analysis Skills: Develop skills in data collection, analysis, and documentation for system performance evaluation.
4. Collaborative Teamwork: Build effective communication and teamwork skills by working closely with graduate students, postdoctoral researchers, and industry professionals.
5. Broader Impacts: Understand the practical applications of modular optical systems in fields like space sensing and pharmaceutical research, and their importance in advancing technology readiness levels (TRLs).

Mentoring Activities
The student will receive regular guidance through weekly one-on-one meetings with the PI or a graduate/postdoctoral mentor to discuss project progress, set goals, and address challenges. They will participate in research group meetings to gain exposure to collaborative problem-solving and interdisciplinary discussions. Additionally, they will receive training in CAD modeling, system assembly, and laboratory safety. Workshops on technical writing, presentation skills, and responsible conduct of research will further enhance their professional development.

Faculty Mentor: Qiuhua Huang| Electrical Engineering

Project Abstract: 

The rapid advancement of foundation models, such as Large Language Models (LLMs), has opened new avenues for their application in complex, real-time decision-making systems. Inspired by recent works in other domain such as [1], this project explores the potential of leveraging LLMs as co-pilots in power system operations. The student will investigate how foundation models can assist grid operators by providing real-time insights, predictive analytics, and decision support in managing power systems.
The project will focus on integrating LLMs with existing power system control frameworks to enhance situational awareness, optimize grid stability, and improve response times during contingencies. By fine-tuning a foundation model on power system datasets, including historical grid performance, weather patterns, and load forecasts, the co-pilot will be trained to interpret complex system dynamics and provide actionable recommendations. The research will also address challenges such as model interpretability, data security, and the integration of human expertise with AI-driven insights.
This work aims to demonstrate the feasibility of LLMs as co-pilots in power system operations, paving the way for more resilient, efficient, and adaptive energy grids. The outcomes of this project will contribute to the growing body of research on AI-augmented infrastructure management and provide a foundation for future innovations in the field.

[1] S. Wang, Y. Zhu, Z. Li, Y. Wang, L. Li and Z. He, “ChatGPT as Your Vehicle Co-Pilot: An Initial Attempt,” in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 12, pp. 4706-4721, Dec. 2023
[2] C. Huang, S. Li, R. Liu, H. Wang and Y. Chen, “Large Foundation Models for Power Systems,” 2024 IEEE Power & Energy Society General Meeting (PESGM), Seattle, WA, USA, 2024, pp. 1-5,
[3] Majumder, Subir et al.Exploring the capabilities and limitations of large language models in the electric energy sector, Joule, Volume 8, Issue 6, 1544 – 1549

Student’s role and learning objectives: 

**Undergraduate Student Roles**
1. Literature Review and Background Research:
o The student will conduct a comprehensive review of existing research on Large Language Models (LLMs), foundation models, and their applications in power systems and other domains.
o They will also study power system operation fundamentals, including grid contingency management.
2. Data Collection and Preprocessing:
o The student will assist in gathering and preprocessing power system datasets, including historical grid performance data, and load forecasts.
o They will learn to clean, format, and prepare data for model training and evaluation.
3. Model Fine-Tuning and Integration:
o The student will work on fine-tuning a pre-trained LLM (e.g., GPT or similar) using power system-specific datasets.
o They will collaborate on integrating the model with simulation tools or power system operation frameworks to test its functionality as a co-pilot.
4. Experimentation and Evaluation:
o The student will participate in designing experiments to evaluate the model’s performance in providing actionable insights and recommendations.
o They will assist in analyzing results, identifying strengths and limitations, and suggesting improvements.
5. Documentation and Communication:
o The student will document their work, including methodologies, findings, and challenges, in a clear and structured manner.
o They will present their progress and results through regular updates, a final report, and a presentation at the end of the SURF program.

**Student Learning Objectives**
1. Technical Skills:
o Gain hands-on experience with LLMs, including fine-tuning and deploying foundation models for domain-specific applications.
o Develop proficiency in data preprocessing, model integration, and experimental evaluation.
2. Domain Knowledge:
o Acquire a foundational understanding of power system operations, including grid management, stability, and contingency planning.
o Learn about the challenges and opportunities of integrating AI into critical infrastructure.
3. Research and Problem-Solving:
o Develop critical thinking and problem-solving skills by addressing real-world challenges in AI and power systems.
o Learn to design and conduct experiments, analyze results, and draw meaningful conclusions.
4. Communication and Collaboration:
o Enhance written and oral communication skills through documentation, presentations, and team collaboration.
o Gain experience working in an interdisciplinary research environment.

**Mentoring Activities**
1. Regular Meetings and Guidance:
o Weekly one-on-one meetings to discuss progress, address challenges, and provide feedback.
o Structured guidance on research methodologies, technical tools, and domain-specific knowledge.
2. Hands-On Training:
o Provide tutorials and resources on LLMs, power system fundamentals, and relevant software tools (e.g., Python, TensorFlow/PyTorch, power system simulation tools).
o Supervise and assist with data preprocessing, model fine-tuning, and integration tasks.
3. Professional Development:
o Offer advice on effective scientific communication, including writing reports and delivering presentations.
o Provide insights into career paths in AI, power systems, and interdisciplinary research.

By the end of the SURF program, the student will have gained valuable technical expertise, research experience, and professional skills, preparing them for future academic or industry pursuits in AI and power/energy systems.

Faculty Mentor: Michael Wakin | Electrical Engineering

Project Abstract: 

STEM Kits are themed outreach kits that are developed by Mines faculty and students in coordination with Scouting Colorado. These kits are distributed for free to local schools and youth organizations. Each kit contains several activities. Detailed instructions for leaders and students are provided online.

To date, the STEM Kits have focused on three themes: Sensing Circuits; Machines Lend a Hand; and Earth, Energy, and Environment. In Summer 2025, we are seeking a student to assist with enhancing the sustainability of the STEM Kits program in the future. Activities may include brainstorming and/or designing new STEM Kits, activities, or components (such as circuit boards); streamlining the supply chain for producing STEM Kits at larger scale; identifying possible grant opportunities for future funding; refining instructions; and evaluating and improving outcomes from existing kit themes.

Student’s role and learning objectives: 

Activities may include brainstorming and/or designing new STEM Kits, activities, or components (such as circuit boards); streamlining the supply chain for producing STEM Kits at larger scale; identifying possible grant opportunities for future funding; refining instructions; and evaluating and improving outcomes from existing kit themes. Beneficial experience/qualifications could include experience with outreach/teaching/tutoring, video editing, circuit board design, nonprofits, and/or logistics. The project will help the student refine their oral and written communication skills, teaching/outreach skills, and project planning skills.

Faculty Mentor: Marion McKenzie and Ryan Venturelli | Geology and Geological Engineering

Project Abstract: 

This work will have a student use Earth surface and sediment records to explore areas of past glaciation across the Northern Hemisphere. We will work together to analyze evidence of ice flow, deposition, and advance and retreat behavior of ice sheets that existed 20,000 years ago. This work will use records that inform interactions between glacial ice, ocean dynamics, and landscape structure.

Student’s role and learning objectives: 

We seek to involve an undergraduate student in the analysis of landscape records using ArcGIS Pro and published datasets. Students will also be involved in lab work analyzing sediment samples using a suite of tools including, but not limited to, microscope analyses, image analysis (CT scans, primarily), and carbon extraction. The SURF student on this project will critically evaluate geologic data used to reconstruct glacial change from past ice sheets. The results of this work will be included in a publication on which we will include our SURF student as an author.

Faculty Mentor: Yvette Kuiper | Geology and Geological Engineering

Project Abstract: 

I will need someone to help two graduate students map the Loveland Pass 7.5’ quadrangle. The undergrad will have an opportunity to do their own project within the larger project. The focus of that project can be somewhat flexible and depends on student interests. We will be mapping Proterozoic to Paleogene bedrock, ductile and brittle structures, and surficial deposits (e.g., glacial, landslides). The student must be fit to hike mountainous terrain at elevations between ~9,500 and ~13,500 feet. Students with hiking and backpacking experience are preferred. Mapping and GIS experience are a plus also, but not required.

Student’s role and learning objectives: 

The student will doing their own project within the larger project. They will gain invaluable geological mapping experience, including field safety, designing a mapping strategy, and learning about geologic and particularly tectonic problems of the Colorado Front Range. Mentoring will be provided pretty well continuously during field work and though regular meetings in the office as needed, by MS students and Kuiper. The student will be encouraged to present at the GGE Research Fair, and at the Mines Undergraduate Research conference, and perhaps co-author abstracts/publications and/or the quadrangle map as appropriate.

Faculty Mentor: Adrienne Marshall | Geology and Geological Engineering

Project Abstract: 

In the western US, mountain snowpacks are a vital water resource for recharging aquifers and sustaining stream flow into the drier summer months. However, human-caused climate change is increasing year-to-year variability in water resources, disrupting patterns of snow accumulation and runoff, and making it difficult to predict stream flow. In recent years, multi-year snow droughts have posed a threat to water availability from managed reservoirs and downstream users. These droughts can be caused by unusually warm winters—where more precipitation falls as rain rather than snow (a “warm drought”)—or by unusually low winter precipitation (a “dry drought”). Additionally, years with heavy snowfall, or snow deluges, have been interspersed with drought, making consistent forecasting challenging.

This research project seeks to understand the frequency, extent, and threat of multi-year snow droughts and deluges on water resources across the Western US. An undergraduate student will analyze data from the SNOTEL network (weather stations scattered across high-elevation, remote watersheds) and spatial data from model outputs or satellite remote sensing to understand the spatial extent of these phenomena. Ultimately, this work will help water managers and the scientific community better assess the risks that extreme snow conditions pose to snow-derived water resources.

Student’s role and learning objectives: 

The student will work closely with the primary mentors, Arielle Koshkin and Dr. Adrienne Marshall. We will set up weekly meetings to discuss project updates, ask questions of mentors and discuss next steps. Mentors will provide assistance and feedback on the project, help form achievable research goals, track overall progress, help overcome scientific or coding roadblocks, and discuss interesting findings and results. The student is also invited to join weekly group meetings with graduate students from the Marshall Lab Group which includes discussion of scientific articles, skill development and research presentations. We will encourage the student to present at the annual department science fair, submit an abstract and present at a conference, and/or co-author a publication.

The student will gain skills in computational data analysis, data access, manipulation and visualization of large datasets based in R programing, and basic statistical techniques. Additionally, the student will learn about climate science, snow hydrology and generally research project workflows.

Faculty Mentor: Piret Plink-Bjorklund | Geology and Geological Engineering

Project Abstract: 

Once a watery world, Mars holds clues to its ancient climate and habitability in the fan-shaped landforms across its surface. Some of these landforms are attractive locations for mission landing sites due to their potential for habitability, high biosignature preservation, and ability to record past climate records. Other fan-shaped landforms may be less promising for detecting past life. This project explores morphometrics of fan shaped landforms on Earth and Mars with the goal to develop quantitative criteria for their distinction on Earth, Mars and other planetary bodies.

We are open to students from any department – you do not need to have prior geological knowledge.

Student’s role and learning objectives: 

The undergraduate researcher will assist in Earth and Mars orbital data analyses in close collaboration with a PhD student. We will together develop a research plan and decide on expected outcomes. The student will learn how to set up and conduct research projects, including scientific questions and testable hypothesis, how to plan and conduct work that ensures results, and how to disseminate the results by conference presentations (Mines and well as national conferences) or publications.

The student will learn to use GIS and analyze morphological data on Earth and Mars. The student will also learn about the terrestrial and extraterrestrial fans, and their role in search for life.

Faculty Mentor: Piret Plink-Bjorklund | Geology and Geological Engineering

Project Abstract: 

River floods are among the most common and destructive natural hazards, with significant societal impacts. However, their role in shaping landscapes and contributing to the sedimentary record remains poorly understood. Traditionally, rivers are viewed as relatively stable systems where moderate floods drive most changes. We propose that while this holds true for some rivers, in others, extreme floods play a defining role—reshaping channels and triggering cascading flood effects.

This project takes a fresh approach to studying river floods by analyzing global river discharge patterns. By leveraging the untapped information in river discharge time series – in hydrograph shapes, we will move beyond traditional statistical indices to gain new insights.

Student’s role and learning objectives: 

The undergraduate researcher will assist in global river discharge data analyses in close collaboration with faculty. We will together develop a research plan and decide on expected outcomes. The student will learn how to set up and conduct research projects, including scientific questions and testable hypothesis, how to plan and conduct work that ensures results, and how to disseminate the results by conference presentations (Mines and well as national conferences) or publications.

The student will learn about statistical methods for time-series analyses and their geoscience applications. The student will also learn about rivers globally, and how distinct discharge patterns shape landscapes and influence flood hazards.

We prefer students familiar with using Python, R, or MATLAB for data analysis, and with experience with applied statistics or machine learning (MATH324, CSCI303, or equivalent). These skills are, however, not required.

Faculty Mentor: Manika Prasad | Geophysics

Project Abstract: 

Design and build a system to quantify indirect parameters for reactions. These indirect parameters are based on chemistry, rock physics, and physical characteristics. The reaction processes that will be studied are between minerals, brine, and carbon dioxide. The system would quantify the results of the reactions on different samples.

Student’s role and learning objectives: 

The undergraduate student’s roles would be to;
1. Design and build the system used in the measurements.
2. Test the system by calibrating it.
3. Apply the system to different samples.
4. Analyze the data.

The student’s learning objectives would be to;
1. Exercise design skills and manufacturing skills.
2. Demonstrate experimental skills via benchmark test
3. Document the reaction process between minerals and fluids via established models (PhreeqC, published charts, GWB).
4. Compare reaction processes with indirect signals and, if possible, quantify the results.
3. Interpret data based on chemical and physical parameters.

The student would be mentored as follows:
1. Meetings with research team: advisor, graduate students, and staff.
2. Weekly meetings with entire group that will help student with presentation and writing skills, and Presentation to group to gain confidence and discuss the data.
3. Assist with data interpretation and design as needed.

Faculty Mentor: Ge Jin | Geophysics

Project Abstract: 

Understanding the temporal evolution of subsurface processes requires the effective visualization of time-dependent 3D geophysical datasets. This project aims to develop a Python package for the interactive visualization of 4D geophysical data, specifically focusing on microseismic events and distributed fiber-optic sensing (DFOS) data. The tool will enable users to visualize time-dependent geophysical measurements within a customizable temporal window, facilitating the joint interpretation of subsurface dynamics. Additionally, the package will integrate static datasets such as well logs and 3D seismic images into the same interactive 3D environment, allowing users to toggle different data layers for enhanced interpretation. The final product will be designed to be user-friendly and adaptable for a broad range of geophysical applications, supporting both academic research and industry use.

Student’s role and learning objectives: 

The student will take the lead in developing the Python package, implementing interactive 4D visualization functionalities, and testing the effectiveness of the tool using an open-source field dataset. Key tasks will include:

– Writing Python code to process and render time-dependent 3D geophysical data.
– Integrating visualization libraries such as Plotly to create interactive 3D and 4D visualizations.
– Implementing features to toggle between different data types, adjust time windows, and overlay static geophysical datasets.
– Documenting the package and providing user guidelines to facilitate adoption by the research community.
– Testing and validating the tool using publicly available geophysical datasets.

Through this project, the student will:

– Gain hands-on experience in geophysical data processing and visualization.
– Develop advanced Python programming and software development skills, including working with 3D graphics libraries.
– Learn about different types of geophysical subsurface measurements and their applications.
– Enhance their ability to design user-friendly computational tools for scientific research.
– Gain experience in software development, including version control, documentation, and community engagement.

Mentoring Activities:

To support the student throughout the project, the following mentoring activities will be provided:

– Bi-weekly meetings to discuss project progress, address challenges, and set goals.
– Guidance on geophysical knowledge and relevant literature to ensure the student develops a solid understanding of the subsurface data being visualized.
– Technical mentorship on software package development, including best practices in coding and version control.

This research experience will provide the student with valuable interdisciplinary skills applicable to careers in geophysics, data science, and software development.

Faculty Mentor: Zachary Katz | Geophysics

Project Abstract: 

Almost all motion of Whillans Ice Stream in West Antarctica occurs for just one hour each day. During this hour, the ice stream lurches forward, acting like a very slow earthquake that repeats daily. Although these lurches have been observed since 2003, the rich variability in motion remains unexplained. An 11-year time series of GPS data from Whillans Ice Stream provides a detailed record of ice velocity and can be used to investigate how the lurches are triggered. In this project, you will use machine learning to classify different types of lurches on Whillans Ice Plain and test hypotheses for the variability in observed motion. Unraveling Whillans Ice Stream’s strange dynamics will help us better understand the ice stream’s future and how earthquakes are triggered on tectonic faults.

Student’s role and learning objectives: 

We are looking for a student who is eager to combine physics and computer science to analyze how glaciers move. To succeed in this project, you should have experience with Python programming and an eagerness to learn about glaciology. You will work closely with members of the Mines Glaciology Lab group and gain expertise in data analysis, machine learning, and scientific communication. During this project, you will:

-Analyze large data sets programmatically and use machine learning classification to test hypotheses
-Apply geophysics principles to explain GPS observations of glacier dynamics
-Practice collaborative software development and project management using GitHub
-Gain expertise effectively presenting your research to other students and scientists

You will be co-mentored by Zachary Katz and Dr. Matthew Siegfried. We will meet weekly to discuss progress and provide mentorship on future research paths/career goals. Depending on your progress and interest, there will be opportunities to present your work at scientific conferences.

Faculty Mentor: Matthew Siegfried | Geophysics

Project Abstract: 

In December 2022, NASA launched the Surface Water and Ocean Topography (SWOT) mission to measure surface height of water features worldwide. This mission uses an instrument unlike any that has ever been launched into space and has started to provide unprecedented, but extraordinarily complex data on the global ocean. Over most of the Earth, the mission operates in “low resolution” mode, but in selected areas, the mission turns on “high resolution” mode, which is typically used for terrestrial water bodies (lakes and rivers). As part of the SWOT Science Team, Mines researchers, with collaborators at NASA and around the world, have requested experimental areas for collection of high resolution mode, including in coastal Antarctica and coastal North America, to understand data quality and the potential scientific payoff of collecting high resolution datain coastal ocean environments.

Student’s role and learning objectives: 

The SURF fellow on this project will be responsible for developing scientific hypotheses that can be investigated using high-resolution coastal data and creating pipelines for visualizing and assessing the data quality of SWOT data in coastal environments. The student will be mentored on this project by Dr. Matthew Siegfried and Dr. Bia Villas Boas (both in Geophysics and both on NASA’s SWOT Science Team), along with graduate student Zachary Katz. The SURF fellow will gain skills in geospatial Python programming and software development using GitHub, gain knowledge about coastal ocean processes in North America and Antarctica, and practice key professional development skills such as written, visual, and oral scientific communication to a variety of communities.

Faculty Mentor: Bia Villas Bôas | Geophysics

Project Abstract: 

Interactions between the ocean and the atmosphere play a crucial role in the development and intensification of extreme weather events, yet key uncertainties remain in understanding how ocean waves influence momentum and heat exchange at high winds. This project will analyze satellite data and in situ wind and wave observations from past hurricane seasons to improve our understanding of these processes. The student will work with satellite-derived sea surface properties and compare them with buoy and aircraft measurements to assess air-sea coupling under extreme conditions. The findings will help refine observational strategies for the upcoming SASCWATCH (Study on Air-Sea Coupling with WAves, Turbulence, and Clouds at High winds) field campaigns, which aim to collect high-resolution data in active tropical cyclones during hurricane season. This project will provide hands-on experience in remote sensing, data analysis, and physical oceanography, contributing to efforts to enhance extreme weather forecasting.

Student’s role and learning objectives: 

We’re seeking a student who is eager to apply scientific computing and basic STEM concepts to a problem that will contribute to understanding extreme weather. As part of this project, you will work closely with researchers from the Mines Oceanography research group and receive training in data analysis methods, programming, and scientific communication. By the end of the project, the student will have:

– Developed knowledge of physical oceanography and the physics of ocean waves and tropical cyclones.
– Developed data analysis skills and knowledge of ocean applications of satellite remote sensing
– Gained experience in best practices of scientific computing and software development, including version control, unit testing, and documentation.
– Practiced collaborative software development, open science, and project management through GitHub

The student will be mentored by Dr. Villas Bôas through weekly meetings, where guidance and feedback on the project’s progress will be provided. Depending on the student’s progress and interest, there is the potential for submitting the results of this project for publication in a scientific journal as well as presenting at oceanography conferences.

Required experience: To succeed in this project on this short time scale, the student should have experience with Python programming, differential equations, and basic statistics. Previous knowledge of physical oceanography and climate sciences is not required.

Faculty Mentor: Colin Beyers | Geophysics

Project Abstract: 

Ocean surface waves are a ubiquitous feature of the surface ocean. These waves are responsible not only for how the atmosphere and ocean communicate and exchange information with each other but also play an important role in upper ocean processes such as transport and mixing. While waves are generated by wind, they are also modeled by ocean currents. Recent numerical modeling has suggested that current effects on waves have the potential to impact the statistics and spatial structure of wave properties. However, there remain some gaps in validating these findings with measurements from the real ocean.

This project will focus on analyzing wave statistics from buoys and numerical model output. Our primary goal will be to assess where there is agreement and disagreement between buoys and models. The student will explore the effects of currents on waves, and learn about ocean waves and their properties. This project will advance our understanding of numerical model output, wave properties, and current effects on waves.

Student’s role and learning objectives: 

We’re seeking a student who is eager to apply scientific computing and basic STEM concepts to a problem that will contribute to our understanding of the ocean. As part of this project, you will work closely with researchers from the Mines Oceanography research group, and receive training in data analysis methods, programming, and scientific communication. By the end of the project, the student will have:

– Developed knowledge of physical oceanography and the physics of ocean waves,
– Developed data analysis skills and knowledge of wave observations and modeling,
– Gained experience in best practices of scientific computing and software development, including version control, unit testing, and documentation, and
– Practiced collaborative software development, open science, and project management through GitHub.

The student will be primarily mentored by Ph.D. student Colin Beyers, with guidance from Dr. Bia Villas Bôas. Through weekly meetings, guidance and feedback on the project’s progress will be provided. Depending on the student’s progress and interest, there is the potential for submitting the results of this project for publication in a scientific journal as well as presenting at oceanography conferences

Required experience: To succeed in this project on this short time scale, the student should have experience with Python programming and basic statistics. Previous knowledge of physical oceanography and climate sciences is not required.

Faculty Mentor: Brandon Dugan | Geophysics

Project Abstract: 

In this project, we will perform sediment grain size analysis of materials from the Sumatra-Andaman subduction zone and correlate them with geophysical and geological data to better understand the distribution of slip in the subduction zone. This is a critical parameter in understanding how fluid pressures can build up in subduction zones and ultimately yield large slip during megathrust earthquakes, such as the 2004 Sumatra-Andaman M9+ earthquake that generated a large and deadly tsunami.

Student’s role and learning objectives: 

Cooperatively the student and I will set the expectations for the summer research including summer-long goals and short-term goals that will allow us to meet the summer-long goals. I will provide the student with all the necessary lab training for the grain size analyses and provide scaffolding for understanding how to relate grain size to geophysical signals in the wells that have been drilled. We will have weekly meetings were we (1) discuss the results from the last week, (2) address any questions that arise, and (3) set the goals for the next week of work. I will also provide supplemental information on the bigger picture of what we know and want to know about large tsunami-generating earthquakes.

Faculty Mentor: Neal Sullivan | Mechanical Engineering

Project Abstract: 

The research team at the Colorado Fuel Cell Center (CFCC) seeks a research intern for mechanical design of an advanced fuel-cell test bed. The test bed is undergoing significant revision as a result of a new research project with the U.S. Department of Energy’s Industrial Efficiency and Decarbonization Office (DoE – IEDO). The broad objective of our new three-year DoE program is to advance the technology readiness level (TRL) of emerging low-carbon electric-power systems that hybridize solid-oxide fuel cells (SOFCs) with an internal combustion engine (ICE). The system also features heat capture to further increase system efficiency (combined heat and power – CHP). Hybrid system performance will be characterized across a broad range of fuels, including natural gas, hydrogen, and fuel blends. The project is in partnership with Colorado State University and Rehlko Power.

The CFCC research team will carefully characterize SOFC performance across this range of fuels. The research intern will develop mechanical designs of the test bed for packaging and testing 5-kWe SOFC stacks provided by Ceres Power, Ltd. (Horsham, UK). The successful candidate will have some background with SolidWorks mechanical design software, and will harness this background to modify existing testbed designs. Once complete, the intern will work with CFCC research staff to execute SOFC performanace measurements.

Student’s role and learning objectives: 

The student will work with a team of researchers within the Colorado Fuel Cell Center laboratory (cfcc.mines.edu). This laboratory is among the nation’s leaders in the field of solid-oxide fuel cells and electrolyzers. The student will continuously develop and improve our solid models of fuel cell test bed, and implement designs into the experiment once complete. When the test bed is in proper operational order, the student will work with researchers to execute experiments that quantify fuel-cell performance over a broad range of operating temperatures, pressures, and fuel compositions. The student will regularly meet with fellow researchers in moving the project forward, and completing key research objectives and milestones.

Faculty Mentor: Paulo Tabares | Mechanical Engineering

Project Abstract: 

This is a project in collaboration with NREL where we are developing new heat pump water heaters that can be installed in low-income housing (manufactured homes, multi-family buildings). The student will work with grad students and NREL engineers to develop heat exchangers filled with phase change materials to increase the water heater thermal storage capacity. This might include 3-D printing heat exchangers, material thermal characterization, modeling and lab testing.

Student’s role and learning objectives: 

1.-prototype 3-D printed heat exchanger filled with phase change materials.
2.- characterize PCM-polymer thermal properties and performance
3.-support laboratory testing
4.- Prototype different heat exchanger configurations

The students will work closely with PhD students and an engineer from NREL and meet regularly (at least once a week) with them. The student will get the appropriate training to develop the research tasks. I will meet with the student once a week to review progress and answer any questions. Some of this work might be at NREL.

Faculty Mentor: Paulo Tabares | Mechanical Engineering

Project Abstract: 

This exploratory project aims to quantify the impact climate change has on the ability of buildings to cool down using fresh outdoor air naturally. The student will work closely with two grad students to obtain publicly available weather data and pollution data for 100’s locations in the US and develop maps for the entire US. This high-impact project could get published in a high-impact journal such as Nature Energy.

Student’s role and learning objectives: 

Map and pull data from 100s of stations around the US and obtain air temperature and air quality data for at least the past 2-3 years
Review current literature on this topic
Calculate cooling potential and develop GIS maps for the entire U.S.
Write technical paper

The student will learn how to process and obtain large data sets for entire US
THe student will learn how to develop advanced graphics to better display all collected and process data

The student will work directly with two graduate students and meet with me (faculty) once a week.

Faculty Mentor: Frances Zhu | Mechanical Engineering

Project Abstract: 

Scientific progress relies on data—whether it’s mapping ocean currents, predicting weather patterns, or understanding planetary motion. In the past, researchers relied on intuition and experience to decide where to gather information. Today, we use advanced algorithms to make these decisions more efficiently, helping autonomous systems collect the most useful data in the shortest time possible.
This research compares two different approaches—Gaussian Processes (GPs) and Bayesian Neural Networks (BNNs)—to determine which is better suited for guiding multiple autonomous agents, such as drones or underwater vehicles, in collecting data. Traditionally, Gaussian Processes have been favored for their accuracy when data is limited. However, as we enter an era of big data, where multiple autonomous agents can gather vast amounts of information, Gaussian Processes become computationally inefficient. Bayesian Neural Networks, a newer approach, may be better equipped to handle large and complex datasets, but they are not yet widely tested in this setting.
The study will simulate different data collection scenarios, varying the number of autonomous agents (from 10 to 1,000) and the complexity of the data being gathered. The goal is to identify the tipping point—when it becomes more effective to use Bayesian Neural Networks instead of Gaussian Processes. The research will measure accuracy, computational efficiency, and the ability to handle complex data.
This work has broad implications for scientific discovery and real-world applications. If successful, it could enable fleets of robots to efficiently map ocean currents, monitor environmental changes, or even explore other planets—all by intelligently deciding where to gather data next. By determining the best approach for different scenarios, this research will guide scientists and engineers in making more informed choices about how to collect and analyze data at scale.

Student’s role and learning objectives: 

The undergraduate student involved in this research will take on a combination of computational, analytical, and experimental tasks to contribute meaningfully to the study alongside faculty and graduate students. Their specific responsibilities will include:
1. Implementing and Running Simulations
– Assisting in setting up and executing multi-agent simulations using Python-based tools.
– Configuring experiments with different numbers of agents and learning models (Gaussian – Processes vs. Bayesian Neural Networks).
– Collecting and organizing simulation results for further analysis.
2. Data Processing and Visualization
– Writing scripts to analyze performance metrics (e.g., accuracy, computation time).
– Generating plots and visual summaries to compare the two learning approaches.
– Helping to interpret results and identifying trends in data.
3. Literature Review and Algorithm Understanding
– Reviewing background materials on active learning, Gaussian Processes, and Bayesian Neural Networks.
– Summarizing key findings from relevant research papers.
– Understanding how these models are applied in adaptive sampling scenarios.
4. Collaboration and Communication
– Participating in regular research meetings to discuss progress and challenges.
– Preparing presentation slides or research posters for potential conferences or university research symposia.
– Writing documentation and comments to ensure reproducibility of code and analysis.

Student Learning Objectives:
By participating in this project, the undergraduate student will develop key skills in scientific computing, machine learning, and research methodology. Their learning objectives include:
1. Computational and Algorithmic Skills
– Understanding the fundamentals of active learning and machine learning models.
– Gaining hands-on experience in running and modifying machine learning simulations.
– Learning to evaluate and compare model performance using quantitative metrics.
2. Scientific and Analytical Thinking
– Developing the ability to critically analyze model predictions and computational efficiency.
– Strengthening problem-solving skills by troubleshooting code and refining simulation setups.
– Learning to formulate hypotheses and test them through computational experiments.
3. Research Communication and Presentation
– Improving skills in scientific writing and presenting technical results.
– Learning to synthesize findings into clear, compelling narratives for different audiences.
-Gaining experience in writing research summaries and documenting code effectively.
4. Collaboration and Professional Development
– Engaging in collaborative problem-solving within a research team.
– Understanding best practices in research ethics and data integrity.
– Preparing for future academic or industry roles that involve machine learning and data analysis.

Mentoring Activities:
I have and will continue to mentor through structured guidance, regular weekly check-ins, and hands-on support. I am always reachable through discord. The mentoring plan includes:
1. Weekly Research Meetings
– Holding structured weekly check-ins to discuss progress, challenges, and next steps.
– Providing feedback on code, analysis, and conceptual understanding.
2. Pair Programming and Code Reviews
– Conducting hands-on coding sessions to debug issues and improve coding practices.
– Reviewing code together to ensure clarity, efficiency, and reproducibility.
3. Skill Development Workshops
– Recommending key readings and tutorials on Gaussian Processes, Bayesian Neural Networks, and scientific computing.
– Providing access to online courses or learning materials to supplement their understanding.
– Encouraging participation in university workshops on data science or machine learning.
4. Encouraging Independent Inquiry
– Guiding the student through literature reviews and helping them form their own research questions.
– Encouraging them to present findings in small internal meetings or research poster sessions.
– Supporting their exploration of follow-up research opportunities (e.g., senior thesis, conference participation).
By the end of the project, the student will have gained significant computational experience, a deeper understanding of active learning in machine learning, and improved confidence in conducting and presenting independent research.

Faculty Mentor: Frances Zhu | Mechanical Engineering

Project Abstract: 

Robotic exploration is a critical part of space missions, but decision-making is still largely controlled by human teleoperators. While human oversight ensures high-quality scientific operations, it also introduces significant time delays and costs. This research investigates whether automation can enhance or even replace human decision-making in space exploration. Specifically, it compares three approaches: (1) fully human-operated missions, (2) fully autonomous missions, and (3) human operators assisted by algorithmic suggestions. The goal is to evaluate how these different approaches affect mission success, decision speed, and interpretability.
To test these approaches, we will use a digital twin simulation that mimics a lunar rover searching for the largest ice reserves—a real objective of NASA’s Artemis program. The simulation will provide camera and sensor data in real-time, allowing the operator (human, autonomous system, or a combination of both) to decide where the rover should go next. By the end of the mission, the operator must determine the location of the largest ice deposit. Performance will be measured based on decision speed, accuracy, and user feedback on interpretability.
If automation proves effective and trustworthy, it could significantly reduce mission costs and increase the number of missions NASA can support. This is especially important as NASA moves toward a sustained human presence on the Moon, which will require a fleet of autonomous robots for resource extraction and infrastructure development. This research will help determine the extent to which automation can take on scientific decision-making in space, improving the efficiency and scalability of future missions.

Student’s role and learning objectives: 

Undergraduate Student Roles
The undergraduate student will contribute to the project by assisting with simulation setup, data collection, and analysis. Their specific responsibilities will include:
1. Simulation Development & Experiment Setup
– Helping configure the digital twin simulation of a lunar rover, including sensor data visualization (camera images, temperature readings).
– Assisting in designing the experimental conditions for testing human vs. AI decision-making.
2. Data Collection & Analysis
– Running simulations and recording key performance metrics, such as decision speed, accuracy, and interpretability.
– Organizing and processing survey responses from human operators to evaluate user experience with AI-assisted suggestions.
– Helping analyze trends in how different operator types (human-only, AI-only, human+AI) perform in decision-making.
3. User Interface & Survey Development
– Assisting in refining the Graphical User Interface (GUI) to display real-time rover data.
– Helping design pre- and post-experiment surveys to assess human operators’ familiarity with mission objectives and their perception of AI suggestions.
4. Research Communication & Documentation
– Preparing visualizations, tables, and figures summarizing experimental results.
– Assisting in writing research summaries or reports on findings.
– Contributing to potential conference presentations or research posters.

Student Learning Objectives
By participating in this project, the undergraduate student will gain hands-on experience in space robotics, automation, and human-computer interaction. Their key learning objectives include:
1. Understanding Human-AI Collaboration in Decision-Making
– Learning about the challenges and benefits of human vs. AI control in space missions.
– Understanding how AI-based suggestion systems influence human decision-making in real-world applications.
2. Developing Computational and Experimental Research Skills
– Gaining experience with simulation tools used in robotics and space mission planning.
– Learning how to set up, run, and analyze experiments involving human and AI operators.
– Practicing data processing and statistical analysis to interpret results.
3. Building Science Communication & Research Presentation Skills
– Learning how to document research findings clearly and effectively.
– Gaining experience in presenting scientific data through graphs, reports, and presentations.
– Understanding how to frame research questions and conclusions for a general audience.
4. Enhancing Problem-Solving & Critical Thinking Abilities
– Developing an ability to troubleshoot simulation issues and refine experimental designs.
– Learning to analyze user feedback to improve AI-assisted decision-making interfaces.

Mentoring Activities
To support the student’s learning and professional development, the mentoring plan includes:
1. Weekly Check-Ins & Guidance
– Holding regular meetings to discuss progress, challenges, and next steps.
– Providing hands-on guidance in debugging code, analyzing data, and refining research approaches.
2. Skill-Building & Knowledge Support
– Recommending key readings and tutorials on AI, robotics, and human-computer interaction.
– Offering programming and simulation assistance through pair programming and code reviews.
3. Encouraging Research Communication & Career Development
– Providing feedback on presentations and writing to improve the student’s ability to communicate research findings.
– Discussing career paths related to robotics, space exploration, and AI-driven automation.
– Encouraging participation in university research symposiums or conferences.
By the end of the project, the student will have gained practical skills in robotics simulation, AI decision-making, and human-computer interaction, preparing them for future opportunities in research and industry.

Faculty Mentor: Xiaoli Zhang | Mechanical Engineering

Project Abstract: 

The overarching goal of this project is to design and develop a snow plowing robotic system that supports households, especially vulnerable populations, to remove snow on the sidewalk for ensuring the safety of pedestrians and households during or after snow events.

Student’s role and learning objectives: 

Objectives
Design and prototype the snow plowing mechanism
Design a controller to control the plowing snow plowing system

Skills:
3D modeling
Machining and prototyping
Distance, force, and temperature sensing
Feedback control and programming

Faculty Mentor: Rajavasanth Rajasegar | Mechanical Engineering

Project Abstract: 

High-altitude unmanned aerial vehicles (UAVs), such as the MQ-1C Gray Eagle, currently operate using heavy fuel piston engines that function beyond their optimal performance envelope at extreme altitudes. Retrofitting such UAVs with compact gas turbines could significantly enhance their mission capabilities by providing increased power-to-weight ratio, improved reliability, and extended operational ceiling. Understanding and optimizing gas turbine performance at high altitudes is crucial for evaluating their feasibility in these applications.

This project will develop a test bench for two small-scale gas turbines: the JetCat P500 PRO-GL and the PBS TJ-40 turbojet engine, both of which are high-performance propulsion systems commonly used in UAVs and RC aircraft. The test bench will be equipped with an advanced instrumentation suite, including thermocouples, pressure sensors, thrust measurement systems, and fuel flow meters, to capture key performance parameters. To simulate high-altitude conditions, the test bench will be placed in a vacuum chamber where intake pressure and temperature can be systematically controlled, replicating flight conditions up to 50,000 feet. For a visual representation of how the test stand will function and the type of experimental setup involved, watch this video on a similar test stand concept.

Data from these experiments will provide critical insights into turbine performance at high altitudes and inform strategies to optimize engine operation for UAVs and other aerospace applications. Unlike prior studies that rely solely on computational models, this project will provide empirical validation of high-altitude propulsion behavior, offering a foundation for improved turbine designs and enhanced flight performance. Additionally, the findings will contribute to the development of retrofitted components optimized for high-altitude operation, supporting the U.S. Army’s needs for compact drone propulsion systems.

Student’s role and learning objectives: 

Undergraduate students will be engaged in all aspects of the project, including the design, fabrication, and instrumentation of the test bench. They will receive hands-on training in integrating data acquisition (DAQ) systems, LabVIEW programming, and real-time sensor data processing to monitor engine performance.

The experimental setup will include the installation and calibration of thermocouples for temperature measurement, pressure transducers for monitoring airflow dynamics, load cells for thrust analysis, and flow meters to track air and fuel flow rates at various stages through both gas turbines. These measurements will establish a baseline dataset, characterizing the operational envelope of each engine under standard atmospheric conditions.

Following baseline testing, high-altitude simulations in the vacuum chamber will provide key insights into the engines’ ceiling capabilities, identifying performance limitations and necessary modifications for enhanced high-altitude operation. The data will inform component retrofits such as improved compressor designs, optimized fuel injection systems, and enhanced thermal management solutions to extend operational performance at extreme altitudes.

Students will also participate in conducting vacuum chamber tests, collecting high-fidelity data on turbine operation under simulated high-altitude conditions. In addition to technical skill development, students will learn to interpret experimental data, troubleshoot instrumentation, and refine testing methodologies. They will work closely with faculty mentors and graduate students, present their findings in lab meetings, and contribute to research reports and conference presentations.

This project offers a unique opportunity to develop expertise in aerospace propulsion testing, sensor integration, and high-altitude flight simulation, preparing students for careers in propulsion engineering and experimental aerodynamics.

Faculty Mentor: Anne Silverman | Mechanical Engineering

Project Abstract: 

This project will evaluate existing biomechanical data including motion capture, ground reaction forces, and electromyography to understand balance and control of a step-down task in relation to functional mobility. This broad dataset requires detailed data processing and data visualization to determine how this movement is controlled to modulate center of mass position and control balance.

Student’s role and learning objectives: 

-Processing of biomechanical signals
-Data visualization
-Statistical analysis
-Journal paper development
-Participation in mentoring activities and research workshops associated with the Integrative Movement Sciences Institute, occurring concurrently
-Regular meetings with postdoctoral fellow mentor and broader research team.

Faculty Mentor: Frances Zhu | Mechanical Engineering

Project Abstract: 

To understand how rovers move on the Moon, aerospace engineers typically conduct rigorous testing on a similar rover in an analogous terrain testbed. Over the course of the summer, we will test the mobility limits of our in-house rover in our in-house lunar analogue.

Student’s role and learning objectives: 

Undergraduate Student Roles
The undergraduate student will play a key role in designing, conducting, and analyzing rover mobility experiments. Their specific responsibilities will include:
1. Experimental Setup & Testing
– Assisting in setting up the lunar analogue testbed, including terrain preparation and rover calibration.
– Running mobility tests by driving the rover over different terrain conditions to evaluate its performance.
– Adjusting rover configurations (e.g., wheel type, weight distribution) to test different mobility scenarios.
2. Data Collection & Analysis
– Recording key performance metrics such as wheel slip, traction, and energy consumption.
– Using cameras and sensors to document rover movement and environmental conditions.
– Analyzing data to identify terrain challenges and determine rover mobility limits.
3. Simulation & Comparison
– Assisting in comparing real-world rover performance with existing mobility simulations.
– Helping refine simulation parameters based on experimental results.
4. Documentation & Research Communication
– Keeping detailed lab notes on test procedures, observations, and results.
– Creating plots and summaries to visualize key findings.
– Assisting in writing research summaries or preparing materials for presentations.

Student Learning Objectives
By participating in this project, the undergraduate student will gain hands-on experience in robotic mobility testing, experimental design, and aerospace engineering research. Their key learning objectives include:
1. Understanding Lunar Rover Mobility
– Learning how terrain properties (e.g., slope, surface roughness, regolith composition) impact rover movement.
– Gaining insight into the challenges of mobility in reduced-gravity environments.
2. Developing Hands-On Experimental & Engineering Skills
– Gaining practical experience in setting up and running real-world mobility tests.
– Learning how to use data collection tools and sensors to evaluate rover performance.
– Understanding the process of iterating on test designs to refine mobility predictions.
3. Building Scientific Analysis & Problem-Solving Skills
– Developing the ability to analyze test results and troubleshoot mobility issues.
– Learning how to compare real-world results with simulation predictions.
4. Improving Research Communication & Technical Writing
– Documenting findings in a clear, structured way to contribute to scientific research.
– Gaining experience in creating graphs, tables, and reports to summarize results.
– Preparing presentations or posters for potential research symposiums.

Mentoring Activities
To support the student’s learning and professional growth, mentoring activities will include:
1. Weekly Meetings & Guidance
– Regular check-ins to discuss experimental progress, data analysis, and challenges.
– Providing guidance on troubleshooting experimental issues and refining test methods.
2. Skill Development Support
– Offering hands-on training in rover operation, data logging, and terrain analysis.
– Providing readings and learning materials on lunar mobility and aerospace engineering.
3. Encouraging Research Communication & Career Growth
– Assisting in preparing research summaries or posters for conferences or university presentations.
– Discussing potential career paths in robotics, planetary exploration, and aerospace engineering.

Faculty Mentor: George Kontoudis | Mechanical Engineering

Project Abstract: 

Environmental monitoring presents significant challenges across various applications that evolve in a spatio-temporal manner. Accurate tracking and prediction of environmental changes typically require extensive computational resources and multiple sensors. In this context, a network of drones offers a viable solution by leveraging a divide-and-conquer approach. While individual robots possess limited computational and sensing capabilities, operating within a network enables workload distribution and enhances exploration efficiency. This project will utilize a swarm of Crazyflie drones in an indoor environment equipped with a motion capture system to monitor an unknown phenomenon. The drones will autonomously map their assigned regions and employ rendezvous protocols to converge in close proximity for information exchange. This decentralized approach facilitates effective collaboration while addressing communication constraints.

Student’s role and learning objectives: 

The student will be responsible for implementing a provided algorithm that enables a network of drones to actively explore and monitor an unknown phenomenon. Through this project, the student will gain hands-on experience in graph theory, distributed networks, and programming real robotic systems. In addition, the student will become familiar with state-of-the-art indoor hardware, including a motion capture system. Furthermore, the student will contribute to the scientific writing of a report adhering to the standards of a technical paper. Regular faculty meetings will provide guidance, track progress, and ensure the successful completion of the project.

Qualifications: Basic requirements include a major in Mechanical Engineering, Electrical Engineering, or Computer Science, along with coding experience in MATLAB and Python and relevant coursework in linear algebra and/or feedback control systems. Preferred qualifications include experience with drones, familiarity with ROS, and coursework in machine learning.

Faculty Mentor: Samantha Webster | Mechanical Engineering

Project Abstract: 

Metal additive manufacturing (AM) is a group of new technologies that have become popular in the aerospace, biomedical, and repair industries due to their flexible manufacturing capabilities. Laser powder blown directed energy deposition (DED-LB) is one of these manufacturing techniques where small metal powders are delivered through a nozzle onto a piece of metal and are melted by a laser that travels through the center of the nozzle. The nozzle and laser move together to deposit material where “lines” of metal can be drawn on a surface. Three dimensional parts can be made by layering these lines on top of each other and next to each other. A key parameter of this process is powder delivery from the nozzle. The mass flow of the powders coming out of the nozzle is not uniform and needs to be characterized while the machine deposits material. There is not currently an off-the-shelf solution to this problem, thus the design of a powder flowrate characterization tool using laser light sources and optical sensors is needed. This will be used to study the flow of powders from different nozzles, detect blockages during a build, and understand the relationship between powder flow and laser melting.

Student’s role and learning objectives: 

The undergraduate student will be responsible for designing a test fixture which can be used to measure the distribution of powder flow in DED-LB. This will include CAD work, physical prototyping, and development of data analysis scripts. The student is expected to learn about the DED process and become familiar with the equipment as well as learn about data acquisition and analysis. The undergraduate student will be supported/supervised by a PhD student and will regularly attend weekly group research meetings to present their work. I will also individually meet with the student periodically to give them project guidance and advising for their future in research.

Faculty Mentor: Joy Gockel | Mechanical Engineering

Project Abstract: 

Metal additive manufacturing inherently produces defects such as porosity and rough surfaces which are detrimental to the properties such as fatigue behavior and corrosion resistance. To determine the functional relationships between the defects and the properties, the manufacturing processing parameters used can result in a spread of different porosities and surface conditions. These samples can be further tested to determine the properties and correlative relationships can be drawn between the values. However, this is only useful if there are robust measurement methods to quantify the defects in the sample. This project will explore a variety of measurement methods to quantify the porosity and surface roughness for samples produced in laser powder bed fusion additive manufacturing at a range of processing parameters. Statistical methods will be used to compare the accuracy of the different methods and provide recommendations for measurement method specifications for additive manufacturing users.

Student’s role and learning objectives: 

The student will perform the experimental measurements for the project and data analysis. The student will also learn about the additive manufacturing process and what will affect surface roughness and porosity. Weekly meetings will be scheduled with the faculty and the student to discuss research progress and plans. The participating student will attend the ~biweekly group meetings to discuss with other students performing research. The student will also be invited to attend the ADAPT quarterly meetings (both summer and fall), with an opportunity to present research and interact with industry members.

Faculty Mentor: George Kontoudis | Mechanical Engineering

Project Abstract: 

Dual-arm grasping and manipulation is an emerging field that enables the replication of dexterous tasks, expanding the capabilities of robotic systems in industrial and service applications. However, integrating two robotic arm-hand systems significantly increases complexity, requiring advanced collaborative planning and control methodologies. This project aims to develop and optimize a dual-arm robotic system, focusing on efficient motion coordination and adaptive grasping strategies. To validate the proposed approaches, hardware experiments will be conducted using a testbed of manufacturing tasks that require robust grasping and dexterous manipulation. The system’s performance will be evaluated with a manufacturing testbed for grasping and manipulation. Ultimately, this research seeks to advance dual-arm robotic manipulation, enhancing automation in complex assembly, handling, and fabrication processes.

Student’s role and learning objectives: 

The student will be responsible for developing a dual-arm manipulator and conducting hardware experiments to validate its functionality. Through this project, the student will gain hands-on experience with rapid fabrication methodologies, including SLS 3D printing and hybrid deposition manufacturing (HDM). Additionally, they will become proficient in robotics software, including ROS, as well as Python and C++ programming. Furthermore, the student will contribute to the scientific writing of a report that meets the standards of a technical paper. Regular faculty meetings will provide guidance, monitor progress, and ensure the successful completion of the project.

Qualifications: Basic requirements include a major in Mechanical Engineering, Electrical Engineering, or Computer Science, along with coding experience in Python and C++. Relevant coursework includes robotics and dynamics. Preferred qualifications include experience with rapid fabrication methodologies, familiarity with ROS, and coursework in robot planning.

Faculty Mentor: Leslie Lamberson | Mechanical Engineering

Project Abstract: 

Protective materials—such as those used in helmets, body armor, and spacecraft shielding—must withstand extreme forces while remaining lightweight and effective. To design better protective materials, scientists test how they behave under rapid and intense impacts, similar to those experienced in high-speed collisions or explosions.

One of the most common ways to study these materials is by using a Kolsky bar, a specialized device that generates and measures high-speed forces in a controlled environment. However, traditional Kolsky bars are large and expensive, limiting their use in certain applications. This research project focuses on developing a miniaturized version of the Kolsky bar, making it more accessible for small-scale experiments while still providing accurate results.

A smaller, portable Kolsky bar could allow researchers to test advanced materials more efficiently and in a wider range of conditions. This work will involve designing, building, and testing the new system, with the goal of improving our ability to create stronger and safer protective materials for the future.

Student’s role and learning objectives: 

The undergraduate student will actively contribute to the design, fabrication, and testing of a miniaturized Kolsky bar for high-rate material testing. Their specific responsibilities include:
(1) Experimental Setup & Fabrication: Assisting in the design and assembly of the scaled-down Kolsky bar, including material selection and machining of components.
(2) Instrumentation & Data Collection: Learning to operate high-speed diagnostic tools, such as strain gauges and digital image correlation (DIC), to measure material responses.
(3) Material Testing & Analysis: Conducting dynamic compression tests on protective materials and analyzing data to determine material performance under extreme conditions.
(4) Documentation & Communication: Preparing weekly to biweekly progress reports, presenting findings in group meetings, and contributing to a final research presentation or report.

By the end of this research experience, the student will:

(1) Gain Hands-On Experimental Skills – Learn how to set up and run high-strain-rate experiments using specialized laboratory equipment.
(2) Develop a Stronger Understanding of Impact Mechanics – Understand fundamental concepts of dynamic material response and protective material behavior.
(3) Improve Data Analysis & Interpretation Skills – Learn how to process and interpret high-speed experimental data, including strain and stress wave propagation.
(4) Enhance Engineering Design Abilities – Apply principles of mechanical engineering and material science to prototype a functional miniaturized Kolsky bar.
(5) Strengthen Scientific Communication – Develop the ability to document and present technical findings effectively through reports and presentations.

The student will receive mentoring through direct collaboration with the mentor and/or team in the lab, meetings at consistent intervals to discuss planning, results and deliverables, and discussions on future research opportunities, graduate school, and industry applications related to high-strain-rate materials research.

Faculty Mentor: Rajavasanth Rajasegar| Mechanical Engineering

Project Abstract: 

We are seeking a motivated undergraduate intern to assist with the design and integration of a control system (in LabVIEW) for the ARC-M1 combustor, currently under development. The ARC-M1 is an optically accessible gas turbine sector rig capable of operating on a variety of liquid (SAFs) and gaseous fuels (natural gas, hydrogen, ammonia). The system relies on swirl flow for flame stabilization and features four-side complete optical access, enabling laser and optical diagnostic techniques to study combustion processes. This rig will allow us to investigate how fuel properties affect atomization, vaporization, and combustion, and determine how alternative fuels perform compared to conventional ones. The goal is to ensure that alternative fuels can be certified for use in existing engines without modification, as drop-in replacements.

Student’s role and learning objectives: 

The intern will work on developing a custom LabVIEW program to control the ARC-M1 combustor. The program must ensure safe, remote operation of various sub-systems, including air mass flow controllers, air heaters, fuel mass flow controllers, exhaust throttling valves, igniters, and solenoid valves. The system will be designed for mostly automatic operation with minimal input from the operator, with built-in safety functionality to place the system into a safe state if any parameters fall outside acceptable ranges or if the emergency stop button is pressed.

Additionally, the LabVIEW program will provide data acquisition and logging capabilities for various sensors, including thermocouples, pressure sensors, flowmeters, and valve positions at a rate of 50 Hz. High-speed logging (up to 5 MHz) will be required for signals from photodiodes or other fast sources. This work is crucial for ensuring safe and efficient operation of the ARC-M1 combustor.

Expected Learning Outcomes:

1. Gain proficiency in LabVIEW programming by developing a custom control and data acquisition system for a cutting-edge research rig.
2. Enhance problem-solving skills through real-time system design, integration, and troubleshooting complex control systems.
3. Develop hands-on experience in system safety protocols, sensor integration, and high-speed data acquisition.
4. Improve communication and teamwork skills by collaborating with a multidisciplinary research team and presenting findings.
5. Contribute to impactful research that could influence the future of sustainable aviation fuels and their integration into existing engines.

Qualifications:

• Proficiency in LabVIEW programming.
• Basic understanding of sensors, actuators, and electrical/electronic circuits.

Faculty Mentor: Anna Staerz | Metallurgical and Materials Engineering

Project Abstract: 

Metal oxide based gas sensors are a compact inexpensive alternative to other sensing options. The detection of both hydrogen and methane is of interest for widespread gas leak detection. One of the biggest challenges hindering the more widespread use is the drift (changing background) of the sensor response overtime. This drift is dependent on the operation conditions of the sensor, i.e. temperature, background humidity level and impurity gases. In this project we plan to systematically study the influence of these parameters on the degradation rate. We are now starting measurements in which the sensor is exposed repeatedly, in short time intervals to pulses of the gases of interest. In each of these measurements one of the variables above will be modified, i.e. different operation temperature, high humidity levels, or the presence of impurities. During the summer internship, the student will build upon these measurements to determine conditions that are particularly harsh for the sensors. Depending on the background and the interests of the students, the conditions could also be varied dynamically during the degradation measurement. This work will aid in verifying the suitability of the selected metal oxide sensors for leak detection.

Student’s role and learning objectives: 

The scientific objective of these project is to identify which measurement parameters can increase sensor stability.
The student will work closely with the PhD students to determine suitable ageing conditions. The student will then be asked to prepare and present a measurement plan at a weekly group meeting. Together with the PhD students, the measurement set-up will be modified to allow for the envision measurements. The student will then be responsible for monitoring the measurements and evaluating the sensor behaviour over time. After the measurements ended, additional inputs can be gained for characterization methods, i.e. microscopy or Raman.
During the summer internship, the student will learn how to design an experiment and define a measurable hypothesis. The project will give the student the chance to work with micro-electronics (sensor readout), 3D printing (measurement chamber) some exposure to simple programming (measurement control), gas sensors (preparation of home-made sensors) and materials characterization (microscopy and Raman). Depending on the background and interests of the student any of these aspects can be focused on more heavily.
I am a very hands-on mentor with an open office policy. Unless I am in a meeting students are always allowed to stop by my office to ask questions and share ideas. I currently advise three PhD students who all have expertise in various aspects of the project. We have a weekly group organizational meeting and, in the summer, we will also do a weekly paper discussion. The undergraduate student will be encouraged to participate in both meetings.

Faculty Mentor: Megan Holtz | Metallurgical and Materials Engineering

Project Abstract: 

Understanding the local (nanometer to atomic scale) crystal structure of modern materials is crucial to their development. In this SURF project, you will simulate electron diffraction patterns, learn to take experimental ones, and develop tools for analyzing them. This will enable unprecedented views of crystal structures in complex materials.

Student’s role and learning objectives: 

You will become proficient in MATLAB to simulate and analyze electron diffraction data. You will gain experience on a scanning transmission electron microscope for taking data.

Faculty Mentor: Megan Holtz | Metallurgical and Materials Engineering

Project Abstract: 

Hexagonal ferroelectric oxides are promising multiferroic and quantum materials that are promising for low-energy computation. In this SURF project, we will grow various compositions using molecular beam epitaxy.

Student’s role and learning objectives: 

Learn growth and characterization methods.

Faculty Mentor: Jihye Kim | Metallurgical and Materials Engineering

Project Abstract: 

The recent expansion of clean energy technologies, particularly electric vehicles, has driven enormous growth in the lithium-ion battery (LIB) market. The sustainability of this growth critically depends on the robustness of supply chains for essential battery metals. However, the increasing demand for critical metals such as Co and Ni is projected to outpace identified reserves in the near future. Consequently, developing sustainable processes for recovering these metals from spent LIBs has become imperative.

One of the most significant challenges in this field is the selective recovery of Co and Ni. These two metals share inherently similar physicochemical properties, including vapor pressure, reduction potentials (-0.277 V for Co and -0.250 V for Ni vs. SHE), and solubility, causing substantial challenges for separation using conventional pyrometallurgical and hydrometallurgical methods, as well as emerging electrochemical approaches.

To overcome this bottleneck, the project aims to develop a programmable electrodeposition technology to selectively recover Co and Ni at precisely controlled and predictable ratios. Since non-equilibrium behavior is strongly influenced by ion size-induced diffusion, we leverage this effect and enhance separation efficiency through complexation reactions. Electrodeposition tests, both with and without complexing agents, will determine whether stabilizing one element via complexation can maximize deposition differences under transient conditions.

Student’s role and learning objectives: 

The undergraduate student’s primary roles will include conducting literature reviews, assisting the graduate student mentor with experimental design and setup, supporting laboratory experiments, and analyzing preliminary data. The student learning objectives for the project will be 1) develop a deeper understanding of critical metals separation; 2) gain hands-on experience with experimental and analytical techniques; and 3) learn to process experimental data and interpret results in a scientific context.

My mentoring activities will be structured to support the undergraduate student’s learning and long-term professional growth. I will hold regular biweekly meetings to discuss progress, address challenges, and brainstorm new ideas. The student will also participate in biweekly project meetings where they will present updates, engage in scientific discussions, and receive constructive feedback.

Faculty Mentor: Jihye Kim | Metallurgical and Materials Engineering

Project Abstract: 

Reliable and robust energy storage systems play a crucial role in the national transition towards a carbon neutral economy. Lithium-ion batteries (LIBs) have brought a paradigm shift in the field of energy generation and storage, particularly in electric transportation. However, the annual generation of spent LIBs waste is projected to exceed 5 million tons by 2030, with only 5% being recycled worldwide. While existing recycling processes have been implemented industrially, they face significant challenges such as high energy consumption, low metal separation efficiency, limited recyclability, and substantial waste generation. To promote a circular economy and establish a waste-to-resource supply chain, it is imperative to develop efficient and sustainable metallurgical technologies for LIBs recycling.

The objective of this research project is to advance the recycling of spent LIBs through the development of next-generation recycling technologies based on benign leaching and electrodeposition using deep eutectic solvents. Specifically, we develop a closed-loop process that minimizes waste while maximizing metal recovery. The investigation primarily focuses on improving metal recovery, process kinetics, selectivity, product purity, waste generation, and energy costs.

Student’s role and learning objectives: 

The undergraduate student’s primary roles will include conducting literature reviews, assisting the graduate student mentor with experimental design and setup, supporting laboratory experiments, and analyzing preliminary data. The student learning objectives for the project will be 1) develop a deeper understanding of critical metals recovery; 2) gain hands-on experience with experimental and analytical techniques; and 3) learn to process experimental data and interpret results in a scientific context.

My mentoring activities will be structured to support the undergraduate student’s learning and long-term professional growth. I will hold regular biweekly meetings to discuss progress, address challenges, and brainstorm new ideas. The student will also participate in biweekly project meetings where they will present updates, engage in scientific discussions, and receive constructive feedback.

Faculty Mentor: Parisa Bazazi | Petroleum Engineering

Project Abstract: 

The project focuses on developing advanced hydrogen storage solutions for aviation by utilizing liquid-based hydrogen carriers. Given the challenges of storing hydrogen at cryogenic temperatures (-253°C) or high pressures (up to 700 bar), this research explores alternative storage methods that enhance safety, energy efficiency, and long-term viability for aviation applications. Liquid hydrogen carriers, such as metal hydrides, liquid organic hydrogen carriers (LOHCs), or ammonia, offer potential solutions by enabling hydrogen storage in a chemically stable form, reducing infrastructure complexity and minimizing risks associated with high-pressure gaseous storage.

The project aims to evaluate the thermodynamic and kinetic properties of different liquid-phase hydrogen storage materials under aviation-relevant conditions, considering factors such as hydrogen release rates, cycle stability, and compatibility with existing aircraft fuel systems. A key aspect of this work involves assessing the feasibility of integrating these storage methods into aviation by addressing challenges related to hydrogen uptake, release efficiency, and energy density compared to conventional jet fuels.

Additionally, this research will investigate storage material degradation, thermal management strategies, and the impact of impurities on hydrogen release and recombination processes. By developing scalable hydrogen storage solutions tailored for aviation, this work contributes to the broader goal of decarbonizing air travel while ensuring performance and operational feasibility.

Student’s role and learning objectives: 

The student will be actively involved in researching, designing, and testing liquid-based hydrogen storage solutions for aviation applications. Their responsibilities include:

Literature Review & Data Analysis:

Investigate existing hydrogen storage technologies, focusing on liquid organic hydrogen carriers (LOHCs), metal hydrides, and ammonia-based storage.
Analyze thermodynamic and kinetic properties of different hydrogen storage materials.

Experimental Design & Testing:

Assist in setting up and conducting laboratory-scale hydrogen storage and release experiments under aviation-relevant conditions (elevated temperatures and pressures).
Characterize hydrogen uptake and release kinetics using gas chromatography, thermal analysis, and spectroscopic methods.

Computational Modeling & Simulation.

Faculty Mentor: Patrice Genevet | Physics

Project Abstract: 

Metasurfaces, with their extraordinary ability to manipulate electromagnetic waves, have opened up plenty of opportunities in various fields such as optics, telecommunications, and medical diagnostics. However, the fabrication and the replication of metasurfaces require precise manufacturing of millions, and even billions of nanostructures over large dimensions but keeping high precision accuracy. Because of these requirements, manufacturing scalability is extremely challenging. This is why we want to exploit the technique of nanoimprint lithography.

Nanoimprint lithography is a fabrication technique that involves pressing a nanostructured mold, or said differently a master metasurface, into a substrate to replicate the pattern of the metasurfaces at the nanoscale. Compared to traditional lithographic methods, NIL presents distinct advantages. It can produce extremely precise and repeatable nanostructures, and over large areas, and this is a crucial advantage when we talk about optical components such as metasurfaces. Metasurfaces’ optical properties are fully controlled by the exact arrangement of a large number of nanostructures with different sizes geometry and orientations.

Another remarkable advantage of NIL is that it reduces the cost of the metasurface manufacturing process. In our research laboratory, we traditionally use electron-beam lithography, which is certainly accurate, but extremely expensive and time-consuming especially for large-scale production. NIL, on the other hand, allows for the mass replication of complex nanostructures at a fraction of the cost. Because of this scalability, NIL could be used to produce metasurfaces for commercial applications, bringing nanophotonic technology to the wider market. Large-scale manufacturing of metasurfaces would enable applications beyond imagination, for example, one can imagine smartphones with unexpected camera capabilities (direct imager processing), medical diagnostic tools with unprecedented sensitivity, and solar panels with significantly improved efficiency. These are just a few examples of how metasurfaces, replicated through nanoimprint lithography, can transform our world.

Student’s role and learning objectives: 

To learn concepts associated with metasurfaces and optical diffraction.
To perform laboratory experiments.
To setup nano-imprinting equipment and develop standard operation procedures to replicate master mold.
To perform nanofabrication of master mold, including electron beam lithography, material evaporation and etching of Si-based master stamp.
To characterize the optical properties of the fabricated structures.

Faculty Mentor: Patrice Genevet | Physics

Project Abstract: 

Gallium nitride (GaN) is a highly versatile material with applications in electronics and photonics, such as LED lighting, high-power transistors, and lasers. This material is also widely used for the fabrication of transparent nanostructured optical materials known as metasurfaces. However, to fabricate metasurface devices from GaN, precise shaping and patterning of the material are required. One critical method to achieve this is reactive ion etching (RIE), a process that uses chemically reactive ions to selectively remove material.

The development of an RIE process for GaN involves optimizing several factors to achieve smooth surfaces, accurate shapes, and efficient material removal without causing damage. This is particularly challenging because GaN is a robust material with strong atomic bonds, making it resistant to etching. Researchers have investigated different gas mixtures (such as chlorine-based gases) and process parameters, including pressure, power, and temperature, to find the best conditions for etching GaN.

Through careful experimentation, the process has been refined to balance speed and precision, while minimizing unwanted effects like roughness or residue. The success of these developments allows for the creation of high-performance GaN-based metasurface devices with intricate features, advancing technology in areas like energy efficiency, telecommunications, optical design, and consumer electronics. This research demonstrates how a scientific approach to material processing can enable cutting-edge innovations in modern technology.

Student’s role and learning objectives: 

Undergraduate Student Roles:
– Literature Review and Background Research: The student will begin by reviewing the fundamental principles of reactive ion etching (RIE) and its application to gallium nitride (GaN) devices. This includes understanding the properties of GaN, common etching chemistries, and the operation of RIE equipment.

– Process Setup and Parameter Exploration: The student will learn to set up and calibrate the RIE system, including loading GaN samples and configuring initial process parameters (e.g., gas flow rates, RF power, pressure, and temperature). They will systematically modify these parameters to study their effects on etching rate, surface morphology, and anisotropy.

– Data Collection and Analysis: The student will measure etching outcomes using techniques such as scanning electron microscopy (SEM) to examine surface profiles, profilometry for etch depth, and atomic force microscopy (AFM) for surface roughness. They will compile, visualize, and analyze data to identify trends and optimize the etching process.

– Safety and Equipment Maintenance: The student will adhere to cleanroom safety protocols and learn to maintain and troubleshoot RIE equipment under supervision.

Documentation and Communication: The student will document their process development steps, observations, and results in a lab notebook and prepare reports or presentations for regular group meetings.

Faculty Mentor: Kyle Leach | Physics

Project Abstract: 

Typically, when one hears “particle physics” they think of the high-energy colliders that have produced many of the new fundamental discoveries over the past several decades of what the basic building blocks of our Universe look like, including that of the Higgs Boson in 2012 at CERN’s LHC.  By contrast, our work focuses on new experimental approaches where instead of using the highest energies we can produce at colliders to directly create these particles, we preform small “table-top-scale” particle physics experiments by making extremely precise measurements of known systems at low energies.  At the precision level that we are able to achieve, small differences in a measured value from what we would expect, can tell us fundamental things we don’t know about our Universe.  These experiments are highly complimentary to the collider particle physics experiments, and in some areas provide new information that is not accessible at the highest energies. This project is part of a large international experimental collaboration, called the BeEST (pronouced “beast”), that we lead at the Colorado School of Mines within the Physics Department and the Quantum Engineering and Nuclear Engineering interdisciplinary programs, with major contributions from Lawrence Livermore National Laboratory (California), Pacific Northwest National Laboratory (Washington), and TRIUMF (Canada), as well as institutions in Europe.  The experiment uses radioactive beryllium atoms embedded in microscopic detectors that are roughly as wide as a human hair, but roughly a thousand times thinner.  These devices are engineered from advanced superconducting materials to allow for us to make a very precise measurement of the beryllium radioactive decay products that are created — most notably a lithium atom, and the most elusive particle we know of in nature, the neutrino.  Neutrinos are the second most abundant form of matter in the universe, yet they are the building block of nature that we know by far the least about.  The reason is that studying them is incredibly challenging since they do not readily interact with other normal matter in our Universe (ie. Materials to make our detectors out of).  Our approach uses the fact that the lithium and the neutrino are “quantum entangled” when they are created following the decay of radioactive beryllium, and thus by making a very precise measurement of the lithium (which we can do), we can access all sorts of properties of the neutrino that you cannot otherwise see. To measure these radioactive decays, we require temperatures colder than interstellar space, which we achieve with the equipment that will be used for this project. The primary goal of this project is to learn and operate these cryogenic environments.

Student’s role and learning objectives: 

In CK014, we have an adiabatic demagnetization refrigerator (ADR) which cools our sensors to ~0.1 K – well below the temperature of liquid helium and interstellar space. This ADR (nicknamed “Marvin”) needs some refurbishment and adaptation to operate STJs reliably and provides an excellent training ground for students who plan on continuing into engineering fields of quantum information science. The primary goal of this project is to get the fridge back to working condition with appropriate temperature readout and control, as well as implementing the wiring and shielding needed to operate STJs for a future experimental program. The student will work closely with the graduate students and postdocs who will instruct them on operation of the equipment

Prior Background
• Experience with electronic signals and advanced experimental techniques
• Prior experience with cryogenic environments (ADR or dilution refrigerator) would be an asset

Student Expectations
• This is on campus work, and must be performed in CK014 so hours on-site are required
• Work together, with PhD student guidance, to devise a plan for Marvin
• End of semester goal to demonstrate operation of STJs in Marvin
• Attending EI group weekly meetings (day/time TBD)

Supervision Plan
Drew Marino – Senior PhD Student, advise on technical aspects
Joe Smolsky – EI Group Postdoc, advise and support
Kyle Leach – Weekly Meetings with progress updates and milestone discussions.
Stephan Friedrich (LLNL,Mines) – Senior Scientist at LLNL. Specific guidance/help as needed.

Faculty Mentor: Meenakshi Singh | Physics

Project Abstract: 

Many qubits are single atoms or electrons whose state one is attempting to read for quantum computation. Thus, the physical output from quantum computing experiments is often a very small signal (typically one photon). In order to process this measurement, the signal must be amplified to a measureable amplitude while increasing the background noise as minimally as possible. This project focuses on the development and characterization of one possible device to solve this problem (with numerous applications in other microwave and Radio Frequency [RF] signal processing challenges): a traveling wave parametric amplifier (TWPA). TWPAs utilize RF resonance to amplify select signals with high signal-to-noise-ratios. The TWPA is comprised of several key components, one of which is a voltage-variable capacitor (varactor), which will be paid special attention in the SURF project. The device must meet several challenges, including operation in the cryogenic conditions needed for solid-state quantum information processing. Working with the PI and a PhD student, the student will use a variety of electronics manufacturing tools and processes to iteratively develop and optimize the varactor and TWPA and will use an equally wide spread of characterization techniques to validate their findings.

Student’s role and learning objectives: 

The student will be directly involved with the physics, electrical engineering, and materials science elements of the theory, circuit design, fabrication, and characterization of this device. The student will be expected to keep a detailed lab notebook documenting his/her manufacturing and testing parameters, and they will meet regularly with the PI and PhD student to discuss their findings and progress. The student will learn laboratory processes and use tools including: sputter deposition, thermal annealing, Lock-in Amplifier measurement, profilometry, ellipsometry, cryogen operation, scanning-electron microscope imaging, and much more!

Faculty Mentor: Meenakshi Singh | Physics

Project Abstract: 

EDMR is a powerful technique to measure spin dynamics with high sensitivity. Such characterization is useful for studying spin qubits in Silicon. This SURF project is part of a larger project to raise the operation temperature of spin qubits in silicon using phonon band structure engineering. In the sub-project, the students will fabricate MOSFET devices (as proxy for electrostatically-defined quantum dots in silicon) which will be measured at NREL in an EDMR setup.

Student’s role and learning objectives: 

The fabrication of the device involves several steps in a clean room and device characterization using a probe station. The students will learn to use AUTOCAD and experimental skills such as clean room protocols, photolithography, oxide growth, reactive ion etching, and electron microscopy. At the end of the project, the students will be proficient in semiconductor device manufacturing and testing. I will meet with the students once a week in a group meeting and they will present their results once every two weeks also in a group meeting. In addition, I will be available during office hours and senior graduate students and post-docs will be available for trainings etc.

Faculty Mentor: Wouter Van De Pontseele | Physics

Project Abstract: 

Neutrinos are ultralight particles that rarely interact with matter. Within the Quantum Technologies at the Sensitivity Frontier group, we are trying to understand their properties. To achieve this goal, we need a very low-noise environment to trigger, process, and digitize the weakest signals from superconducting sensors. This project aims to investigate several techniques to calibrate these low-noise environments and process the signals. One option involves using a Xillinx RFSoc fabricated by AMD with a Maybell Quantum Dilution refrigerator. Another pathway includes data analysis collected at a nuclear reactor in France using anomaly detection techniques in Python. Another one involved microwave signal processing and reconstruction of neutrino events.

Student’s role and learning objectives: 

The selected student will acquire or build upon the following skills:

– Operation of a dilution refrigerator
– FPGA operation of an RFSoc using QICK python framework
– Measurements of superconducting sensors such as SQUIDs
– Machine learning techniques such as anomaly detection

Faculty Mentor: Kamil Ciesielski | Physics

Project Abstract: 

The emergence of new complex electronic materials is vital for improvements in quantum information, sensing and computing. Recently, our laboratory discovered ternary materials with frustrated vanadium sublattices, i.e. networks where the magnetic spins cannot arrange in alternating directions (imagine putting the spins on the corners of a triangle). These materials have been shown to host multiple coexisting quantum effects, including superconductivity, topological properties and charge density waves. The phenomena are found to interact and compete, making the materials tunable and hence potentially useful for quantum technology. Full understanding and control of these interactions, however, is challenging because of very limited material examples known so far. In this project, we aim to discover novel compounds with frustrated vanadium nets by screening broad array of ternary phase diagrams. As our synthesis technique, we will use single crystal growth from metal flux. The obtained samples will be characterized structurally by scanning electron microscopy (SEM) and x-ray diffraction (XRD). The student will also engage in discussion on scientific literature both directly relevant to the laboratory activities (i.e. focusing on synthesis and new compounds discovery), as well as the articles discussing the newest findings on quantum effects in frustrated Kagome nets. The obtained results will be shared every 2-3 weeks with the research group by the short students’ presentations.

Student’s role and learning objectives: 

Learning objectives:
– Proficiency in single crystal growth from metal flux,
– Familiarity with fundamental tool of structural characterization for inorganic materials: SEM and XRD,
– Good practices in data management and scientific communication,
– Scientific literacy.

Students’ role:
– Co-designing experiments for the single crystal growth with the SURF mentor, sample weighing, sealing the silica ampoules with the torch, furnace operation, and centrifuging,
– Operating scanning electron microscopy, x-ray diffraction devices with mentor supervision,
– Maintaining personal laboratory logbook and leading short presentations of the findings to the research group every 2-3 weeks,
– Reading relevant scientific literature provided by the mentor and chosen independently by the student.