Divya Singhvi

About

Divya Singhvi is an Assistant Professor of Technology, Operations, and Statistics at the NYU Stern School of Business, having joined the institution in September 2021. His research focuses on the intersection of machine learning and operations management, specifically addressing problems related to optimal demand learning, pricing, recommendations, and logistics within online and offline retail operations. Prior to his current role, Singhvi spent a year at IBM Research as a postdoctoral researcher in the IBM Research AI Residency program. He holds a Bachelor of Science degree in Operations Research and Engineering from Cornell University and a PhD in Operations Research from the Massachusetts Institute of Technology.

Research topics

• Computer science
• Business
• Operations research
• Machine learning
• Econometrics

Selected publications

• COVID‐19: A multiwave SIR‐based model for learning waves

  Production and Operations Management · 2022-01-29 · 38 citations

  articleOpen accessCorresponding

  One of the greatest challenges of the COVID‐19 pandemic has been the way evolving regulation, information, and sentiment have driven waves of the disease. Traditional epidemiology models, such as the SIR model, are not equipped to handle these behavioral‐based changes. We propose a novel multiwave susceptible–infected–recovered (SIR) model, which can detect and model the waves of the disease. We bring together the SIR model's compartmental structure with a change‐point detection martingale proce…

  PublisherOA PDFDOI

• Learning Personalized Product Recommendations with Customer Disengagement

  Manufacturing & Service Operations Management · 2021-12-09 · 33 citations

  articleSenior author

  Problem definition: We study personalized product recommendations on platforms when customers have unknown preferences. Importantly, customers may disengage when offered poor recommendations. Academic/practical relevance: Online platforms often personalize product recommendations using bandit algorithms, which balance an exploration-exploitation trade-off. However, customer disengagement—a salient feature of platforms in practice—introduces a novel challenge because exploration may cause custome…

  PublisherDOI

• The role of optimization in some recent advances in data-driven decision-making

  Mathematical Programming · 2022-08-11 · 22 citations

  articleOpen access

  Abstract Data-driven decision-making has garnered growing interest as a result of the increasing availability of data in recent years. With that growth many opportunities and challenges have sprung up in the areas of predictive and prescriptive analytics. Often, optimization can play an important role in tackling these issues. In this paper, we review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions…

  PublisherOA PDFDOI

• COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation

  Manufacturing & Service Operations Management · 2022-12-15 · 21 citations

  article

  Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the…

  PublisherDOI

• Deep Policy Iteration with Integer Programming for Inventory Management

  Manufacturing & Service Operations Management · 2025-01-06 · 10 citations

  articleSenior author

  Problem definition: In this paper, we present a reinforcement learning (RL)-based framework for optimizing long-term discounted reward problems with large combinatorial action space and state dependent constraints. These characteristics are common to many operations management problems, for example, network inventory replenishment, where managers have to deal with uncertain demand, lost sales, and capacity constraints that results in more complex feasible action spaces. Our proposed programmable…

  PublisherDOI

Frequent coauthors

• Georgia Perakis

  20 shared

• Omar Skali Lami

  IIT@MIT

  12 shared

• Leann Thayaparan

  Massachusetts Institute of Technology

  8 shared

• Qi‐Jun Hong

  Arizona State University

  7 shared

• Somya Singhvi

  6 shared

• Shelby Wilson

  5 shared

• Rachel J. Oidtman

  Merck & Co., Inc., Rahway, NJ, USA (United States)

  5 shared

• Simon I Hay

  5 shared

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