Vishal Gupta

Assistant Professor of Data Science & Operations
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PhD, MIT; BA, Yale University

Vishal Gupta is an Assistant Professor in Data Sciences and Operations. Vishal completed his PhD in Operations Research from MIT, focusing on data-driven methods in optimization. He earned his BA in Mathematics and Philosophy from Yale University, graduating Magna Cum Laude with honors, and completed Part III of the Mathematics Tripos at the University of Cambridge with distinction as part of the Paul Mellon Graduate Fellowship. In addition, Vishal spent 4 years working in Quantitative Analytics for Barclays Capital with particular emphasis in commodities modeling, derivatives pricing and risk management. He continues to collaborate with industrial partners in electricity markets and financial service providers. Vishal's research interests focus on developing novel techniques for representing uncertainty and behavior in optimization problems. He draws heavily on state-of-the-art statistical techniques for large data sets.
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Vishal Gupta, Song-Hee Kim, Hyung Paek, Rong Qing Han () "Maximizing Intervention Effectiveness ,"  Working Paper.
Dimitris Bertsimas, John Griffith, Vishal Gupta, Mykel Kochenderfer, Velibor Misic, Robert Moss () "A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation ,"  European Journal of Operations Research  263,
Vishal Gupta, Paat Rusmevichientong () "Small-Data, Large-Scale Linear Optimization ,".
Dimitris Bertsimas, Vishal Gupta, Nathan Kallus () "Robust Sample Average Approximation ,"  Mathematical Programming, Series A,
Dimitris Bertsimas, Vishal Gupta, Nathan Kallus () "Data-driven Robust Optimization ,"  Mathematical Programming, Series A,
Iain Dunning, Vishal Gupta, Angela King, Jerry Kung, Miles Lubin, John Silberholz () "A Course on Advanced Software Tools for Operations Research and Analytics ,"  INFORMS Transactions on Education  15, 169–179.
Dimitris Bertsimas, Vishal Gupta, IoannisCh. Paschalidis () "Data-driven estimation in equilibrium using inverse optimization ,"  Mathematical Programming, 1-39.