News & Events

ID4-Affiliated Workshops

ID4 is partnering with other researchers to host a variety of in-person workshops. We strive to intentionally bring together intellectually diverse researchers and stakeholders to frame questions, develop cross-domain communication skills, and design shared platforms for solutions from inception. These workshops are designed around best practices in convergence research.

If you are interested in helping to organize and/or participating in one of these conferences, please email efreed@mines.edu.

2024 Workshops

JULY
Integration of Data Fluency into 6th-12th Grade Education
Dates: July 11-12, 2024
Location: Colorado School of Mines, Golden, CO
Organizers: Emily Freed (Colorado School of Mines), Brad Beadell (Wilcox High School), Matthew Graham (CalTech), Alex Pak (Colorado School of Mines)
Overview: Data analytic skills, i.e. leveraging data to improve decision-making, are increasingly needed to be competitive in the workforce across numerous industries. It is therefore important to increase the data fluency of students starting from the K-12 level. The purpose of this workshop is for data science and domain experts across biology, chemistry, physics, and engineering from academia and industry to collaborate with 6th-12th grade teachers to design curricular materials that include data collection, analysis, modeling, or inference for use in 6th-12th grade classrooms. Participants will develop learning modules in a hackathon environment which will be piloted and iterated throughout the workshop. All developed learning modules will be made publicly available at the end of the workshop.

APRIL
AI-Ready Data: Navigating the Dynamic Frontier of Metadata and Ontologies
Dates: April 15-16, 2024
Location: Quorum – University City Science Center, Philadelphia, PA
Organizers: Jane Greenberg (Drexel), Yuan An (Drexel), Yasin Bakis (TUBRI), Hank Bart (TUBRI), Vandana Janeja (UMBC), Jianwu Wang (UMBC)
Overview: AI-ready data refers to the high-quality and well-prepared data that is optimized for use in artificial intelligence (AI) applications. AI-ready data increasingly encompasses the inclusion of metadata and ontologies to enhance the value and usability of data. Challenges underlie the full data lifecycle from data creation, collection, and research, to longer-term aims of data preservation, archiving, reuse and support for research reproducibility. Collective, community driven efforts are needed to address current obstacles and maximize the value and reliability of data. This workshop is a step toward addressing this challenge. This workshop will bring together a community of individuals with expertise across the data lifecycle to discuss issues, share solutions, and chart a path forward for addressing key challenges in preparing AI-ready data for scientific research.

FEBRUARY
AI for Materials Science
Sponsor: AAAI Conference
Dates: February 20-21, 2024
Location: 2024 AAAI Conference, Vancouver, Canada
Organizers: Roman Garnett (WashU. in St Louis), Peter Collins (Iowa State U.), Peter Frazier (Cornell), Patrick Johnson (Iowa State U.), Jessica Koehne (NASA), Lars Kotthoff (U. Wyoming)
Overview: The development of new materials and production processes and the customization of existing ones is increasingly driven by AI, in particular Bayesian optimization and surrogate modeling. In many cases, materials science has relied on compute-intensive simulations to evaluate the properties of proposed designs, or the effect a change might have. Such simulations do not scale to the vast design spaces that materials scientists explore. Machine learning provides an alternative: properties are approximated through the predictions of surrogate models rather than computed by simulations, orders of magnitude faster. Both AI and materials science are working on conceptually similar problems — how to efficiently identify the best design choices, be that for a machine learning pipeline or a new material. Yet, there is little collaboration between the communities. The purpose of this Bridge is to bring the communities closer together, facilitate cross-disciplinary collaborations, identify common problems, and develop plans for tackling them. 

NSF Workshop: Integrating LLMs into the Materials Chemistry Curriculum
Sponsor: NSF DMR-2333654
Dates: February 12-13, 2024
Location: Colorado School of Mines, Golden, CO
Organizers: Annalise Maughan (Colorado School of Mines), Eric Toberer (Colorado School of Mines), Alex Zevalkink (Michigan State U.)
Overview: This two day NSF-sponsored workshop aims to explore and develop innovative ways to incorporate Large Language Models (LLMs, including GPT, ChatGPT, and Bard) into upper division chemistry laboratories and virtual lab experiences. During the workshop, participants will brainstorm and create demonstrations incorporating LLMs into the curriculum. These could include designing pre-labs, virtual lab activities, and lab reports. We will also discuss limitations and safety measures for its effective use in laboratory settings.

2023 Workshops

OCTOBER
HDR Annual Meeting
Dates: October 16-18, 2023
Location: Denver, CO
Organizers: Eric Toberer (Colorado School of Mines/ID4), Tanya Berger-Wolf (Ohio State U./Imageomics), Bo Li (U. Illinois/I-GUIDE), Paula Mabee (NEON/Imageomics), Mark Neubauer (U. Illinois/A3D3), Sharad Sharma (U. North Texas/iHARP)
Overview: The annual meeting is an opportunity to foster convergence between (i) the HDR Institutes, (ii) NSF DSC teams, and (iii) NSF TRIPODS. We expect to interweave reflective presentations on the accomplishments to date of the Institutes with facilitated discussions and activities that are forward-looking to seed new collaborations and establish best practices.

Uniting Junior Faculty in the Applied Sciences with Junior Faculty in Computer Science – part 1
Date: October 16-18 (events concurrent with HDR Annual Meeting)
Location: Denver, CO
Organizers: Eric Toberer (Colorado School of Mines/ID4), Osman Gani (U. Maryland/iHARP), Anuj Karpatne (Virginia Tech/Imageomics), Pan Li (Georgia Tech/A3D3), Alvitta Ottley (WashU/ID4), Zhaonan Wang (U. Illinois/I-GUIDE)
Overview: This workshop will facilitate collaboration and networking for both junior faculty and postdocs who are applying for faculty positions from domain and data sciences. The workshop will include open networking sessions, brainstorming sessions, and discussion groups.

JUNE
Systematic Analysis of Errors and Uncertainty Across Scales from Materials Modeling & Discovery to Manufacturing: Towards Best Practices
Sponsor: NSF CMMI-2315913 and the Air Force Office of Scientific Research
Dates: June 8-9, 2023
Location: Basic Research Innovation Collaboration Center, Arlington, VA
Organizers: Elif Ertekin (U. Illinois), Giulia Galli (U. Chicago), Ali Sayir (AFOSR)
Overview: With substantial developments and focused efforts targeting the creation of a Materials Innovation Infrastructure over the last ten years, computation, modeling, and simulation have become integral to the materials discovery, innovation, and deployment cycle. The purpose of this workshop is to bring together researchers in first-principles modeling, machine learning/informatics approaches, multiscale modeling, and modeling/artificial intelligence for process design/manufacturing. We will discuss cross-cutting pathways, challenges, and opportunities for systematic quantification of errors and uncertainty. We aim to identify best practices and create momentum towards the integration of error and uncertainty analysis into standard computational workflows, so that rigorous error determination and reporting become a standard for the community of modelers.

Interactive Visualization and Analysis of High-Dimensional Scientific Data
Dates: June 13-16, 2023
Location: Workbar, Boston, MA
Organizers: Remco Chang (Tufts), Erik Anderson (Novartis), Nils Ghelenborg (Harvard Medical School), Steven Lopez (Northeastern), Jen Rodgers (Tufts), Qianwen Wang (Harvard Medical School), Cory White (Merck)
Overview: Large, high-dimensional scientific (numeric) data is difficult for domain scientists to visualize. Successful visualization of such data can help scientists develop intuition about their data and discover new and unexpected patterns. Several dimensionality reduction (projection) techniques exist, such as t-SNE, UMAP, Isomap, and many others. However, it can be difficult to trust or draw meaning from the underlying data from current projection methods, for example, trusting the retention of the underlying data structure or understanding features that drive separation. The purpose of the workshop is to invite domain scientists and visualization experts to co-develop the next generation of interactive high-dimensional data visualization techniques. We aim to collect and summarize the key requirements from the perspective of the domain scientists and the technical challenges faced by the visualization researchers.

MAY
Young Researcher Mentoring Workshop
Sponsor: Alliance for Diversity in Science and Engineering
Dates: May 17-19, 2023
Location: Texas A&M University, College Station, TX
Organizers: Steven Lopez (Northeastern), Fernando Fajardo-Rojas (Colorado School of Mines), Leticia Gomes (Northeastern), Shayan Monadjemi (WashU)
Overview: Applying for research positions? This is a professional development workshop to prepare you for the upcoming steps in your career. We will have mentors and speakers from academia, government, and industry. This conference is a great way to expand your professional network. It is open to students at any level – high school, community college, undergraduate, and graduate.

JANUARY
Rocky Mountain Solid State Chemistry Workshop
Dates: January 10-11, 2023
Location: University of Colorado Boulder, Boulder, CO
Organizers: Eric Toberer (Colorado School of Mines), Ann Greenaway (NREL), Annalise Maughan (Colorado School of Mines), Eve Mozur (Colorado School of Mines), Jamie Neilson (Colorado State U.), Michael Toney (U. Colorado)
Overview: Solid state chemistry spans the synthesis, structure, and properties of solid phase materials. Examples within synthesis and structure include new material discovery, single crystal and thin film growth, and diffraction and scattering techniques. The mission of this workshop is to bring together the growing community of solid state chemists in the Colorado Front Range and provide a forum for seeding new collaborations.