Siddharth Chandra
Michigan State University, MI·Social Science
Cocaine reaches U.S. streets through trafficking networks that are hidden, fragmented and constantly shifting, which makes them hard for law enforcement to map or disrupt.
- Fields
Social Science
Engineering
Mathematics & Statistics
- Topics
illicit supply networks
drug trafficking
cocaine
- Grants
- 1 known grant, last ended Sep 2026
What they work on
- Infer the hidden structure and flow of cocaine supply networks by applying network analysis to cocaine price data that vary across locations and over time.
- Use artificial intelligence and machine-learning models on empirical drug-market data to detect how trafficking networks change their behavior in response to interdiction, meaning seizures and enforcement actions.
- Evaluate competing interdiction strategies with game-theoretic models, which treat traffickers and enforcers as opponents, combined with combinatorial optimization and agent-based simulation of individual actors.
- Integrate these results into a network optimization model that explains illicit drug supply chain structure and identifies evidence-based disruption strategies.
More about the research
This collaborative project asks how those supply networks are structured and how they respond when authorities seize drugs or arrest traffickers (interdiction). It treats the drug trade as a supply chain that can be studied with the tools of economics, geography, criminology, public policy, computer science and operations research, the mathematics of planning and optimization.
Why it matters
Anti-narcotics and other law enforcement agencies need to know where to act to disrupt cocaine trafficking with limited resources. By inferring the shape of supply networks from price data and testing interdiction strategies in models before they are used, this work could give these agencies evidence for more effective counter-narcotics efforts in the United States.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Agostino Capponi
Columbia University, NY·Engineering
Develop a dynamic decision-making framework, using dynamic games and network theory, that quantifies trade-offs between routine efficiency and resilience to supply and demand shocks in supply chain networks.
Agnieszka Truszkowska
University of Alabama in Huntsville, AL·Biomedical Engineering·Early-career
Build a computational model of how circulating tumor cell (CTC) clusters move through the bloodstream, including their surroundings, interactions with other cells, and cell-to-cell signaling.
Juba Ziani
Georgia Institute of Technology, GA·Computer Science·Early-career
Model how individual data providers shape the outcomes of machine learning systems through strategic choices about what data to share, combining tools from computer science and economics.
The school
Michigan State University: 28 labs on PI Pages, $932M a year on research, 555 research doctorates a year.
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