Project Info
Machine Learning and AI for Mineral Supply Chains
Sebnem Duzgun
duzgun@mines.edu
Tulay flamand
tflamand@mines.edu
Project Goals and Description:
This project aims at using graph analytics and machine learning models for gold and other mineral supply chains. We will use the database to identify the network graphs of mineral supply chains, as well as the network of actors and materials. This will allow us to identify critical nodes of the supply chains and we will adopt graph analytics tools including path, connectivity, community, and centrality analyses. The path analysis will help to identify critical paths containing illicit activities, while connectivity analysis will highlight the weaknesses in the supply chain networks. Students in this project will learn graph analytics, machine learning, data curation
The students will work with graduate students and join weekly project meetings. Students will work in a computational field
More Information:
Grand Challenge: Engineer the tools of scientific discovery.
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935630&HistoricalAwards=false
Primary Contacts:
sebnem duzgun, duzgun@mines.edu
Student Preparation
Qualifications
Some knowledge of python or R or motivation to learn. Some experience on data science
TIME COMMITMENT (HRS/WK)
8
SKILLS/TECHNIQUES GAINED
machine learning, supply chain analytics, programming in R and/or python and data science
MENTORING PLAN
Students will work with graduate students and will join weekly project meetings. Faculty advisors will join meetings biweekly
Preferred Student Status
Sophomore
Junior