Project Info

Machine Learning and AI for Mineral Supply Chains

Sebnem Duzgun
Tulay flamand

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.

Primary Contacts:

sebnem duzgun,

Student Preparation


Some knowledge of python or R or motivation to learn.  Some experience on data science




machine learning, supply chain analytics, programming in R and/or python and data science


Students will work with graduate students and will join weekly project meetings.  Faculty advisors will join meetings biweekly

Preferred Student Status

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