Sanjay Mehrotra

About

Sanjay Mehrotra is a Professor of Industrial Engineering and Management Sciences at Northwestern University, where he also serves as the Director of the Center for Engineering and Health and co-Leads Project Minerva. His research develops methods for decision optimization under uncertainty by studying the geometric and algebraic properties of these problems. He is passionate about applied research problems in Health Systems Engineering, with significant contributions to modeling the US National Transplant System, liver disease, and pandemic modeling. His expertise spans areas such as Energy, Inventory Management, and Optimal Learning, with a focus on balancing mathematical rigor and practical applicability. Professor Mehrotra has established groundbreaking results in the convergence of two-stage stochastic optimization, enabling solutions to previously intractable problems. He has also contributed fundamental insights into quantifying convergence metrics in stochastic systems described as Markov Chains and pioneered efforts in Risk-Adjusted and distributionally robust optimization. His work in healthcare systems engineering includes modeling complex health systems, and he has held leadership roles such as chairing the INFORMS Fellow Committee and serving on the INFORMS board.

Research topics

• Computer Science
• Mathematics
• Artificial Intelligence
• Mathematical optimization
• Marketing
• Computer network
• Business
• Applied mathematics
• Combinatorics
• Operations research

Selected publications

• A model of <scp>supply‐chain</scp> decisions for resource sharing with an application to ventilator allocation to combat <scp>COVID</scp> ‐19

  Naval Research Logistics (NRL) · 2020 · 187 citations

  1st authorCorresponding

  Abstract We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings su…

  DOI

• Frameworks and Results in Distributionally Robust Optimization

  Open Journal of Mathematical Optimization · 2022 · 160 citations

  Senior authorCorresponding

  The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions…

  DOI

• Distributionally robust optimization with decision dependent ambiguity sets

  Optimization Letters · 2020 · 88 citations

  Senior authorCorresponding

  Abstract We study decision dependent distributionally robust optimization models, where the ambiguity sets of probability distributions can depend on the decision variables. These models arise in situations with endogenous uncertainty. The developed framework includes two-stage decision dependent distributionally robust stochastic programming as a special case. Decision dependent generalizations of five types of ambiguity sets are considered. These sets are based on bounds on moments, covariance…

  DOI

• Optimization modeling for pandemic vaccine supply chain management: A review and future research opportunities

  Naval Research Logistics (NRL) · 2024-04-18 · 16 citations

  reviewOpen accessSenior authorCorresponding

  During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID-19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inven…

  PublisherOA PDFDOI

• Social Disadvantage and Disparities in Chronic Liver Disease: A Systematic Review

  The American Journal of Gastroenterology · 2024-10-29 · 15 citations

  reviewOpen access

  INTRODUCTION: Social determinants of health (SDOH) may impact chronic liver disease (CLD) outcomes but are not clearly understood. We conducted a systematic review to describe the associations of SDOH with mortality, hospitalizations, and readmissions among patients with CLD. METHODS: This review was registered (PROSPERO ID: CRD42022346654) and identified articles through MEDLINE, Embase, Cochrane Library, and Scopus databases. The review included studies that reported SDOH characteristics withi…

  PublisherDOI

Recent grants

• Promoting Utilization of Kidneys by Improving Patient Level Decision Making

  NIH · $418k · 2016–2020

• Addressing Geographical Disparities in Transplant Organ Accessibility Across United States

  NSF · $320k · 2011–2016

• Models and Algorithms for Risk Adjusted Optimization with Robust Utilities

  NSF · $200k · 2011–2015

Frequent coauthors

• Daniela P. Ladner

  Northwestern University

  47 shared

• Vikram Kilambi

  31 shared

• John J. Friedewald

  Northwestern University

  27 shared

• Karolina Schantz

  Vector (United States)

  18 shared

• Ashley E. Davis

  RTI Health Solutions

  17 shared

• Kevin Bui

  University of California, Irvine

  15 shared

• Fengqiao Luo

  14 shared

• Masoud Barah

  Northwestern University

  14 shared

Labs

• Center for Engineering and HealthPI

Awards & honors

• INFORMS Fellow Committee Chair
• INFORMS board member

Similar researchers at Northwestern University

• Tobin J. Marksh-176
• Joseph Hupph-171
• James D Thomash-160