Divya Singhvi

Assistant Professor of Technology, Operations and Statistics
Leonard N. Stern School of Business, New York University
Kaufman Management Center
44 West Fourth Street, 8-75
New York, NY 10012
ds6844 "at" stern "dot" nyu "dot" edu
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About

I am an assistant professor of Technology, Operations and Statistics at the Leonard N. Stern School of Business at New York University. Prior to joining Stern, I was a postdoctoral researcher in the IBM Research AI Residency Program. I recieved my Ph.D. in Operations Research from MIT, ORC where I was advised by Professor Georgia Perakis. During my PhD, I spent a summer as an intern at the Business Analytics and Mathematical Sciences division of the IBM T. J. Watson Research Center. Prior to joining MIT, I graduated from Cornell University in 2015 with S.B. (Hons.) in Operations Research and Engineering.

My research lies at the intersection of machine learning and operations management. I have worked on problems related to optimal demand learning, pricing, recommendations and logistics in online and offline retail operations. More recently, I have also worked on developing analytical tools to optimize planning and allocation of resources during times of a pandemic (in the context of COVID-19).

Papers
  • Working Papers
  • Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management. (with P Harsha, A Jagmohan, J Kalagnanam, B Quanz). preprint
  • Increasing Charity Donations: A Bandit Learning Approach (with S Singhvi). preprint
  • Ancillary Services in Targeted Advertising: from Prediction to Prescription (with A Borenstein, J Lua, A Mangal, G Perakis, S Poninghaus, O Skali Lami), minor revision in Manufacturing & Service Operations Management. preprint
    • 2021 - Honorable Mention in M&SOM Practice Based Competition
  • COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation (with M Bennouna, D Ndong, G Perakis, O Skali Lami, I Spantidakis, L Thayaparan, A Tsiourvas), minor revision in Manufacturing & Service Operations Management.
    • 2021 - Winner, ICSS Best Conference Paper Competition
    • 2021 - Finalist, Doing Good with Good OR Competition
    • 2021 - Finalist, Public Sector Operations Research Best Paper Award
  • Extended Sampled Trees for Classification and Regression (with G Perakis, O Skali Lami). preprint
    • 2021 - First place, MIT Operations Research Center Best Student Paper Competition
    • Spotlighted presentation in 2021 INFORMS RM&P Conference
  • First Delivery Gaps: A Supply Chain Lever to Reduce Product Returns in Online Retail (with M Chaurasia, S Pandey, H S Rathore, G Perakis), major revision in Manufacturing & Service Operations Management.
    • Preliminary version in 2019 MSOM Supply Chain Management SIG
  • Dynamic Pricing with Unknown Non-Parametric Demand and Limited Price Changes (with G Perakis), major revision in Operations Research. preprint
    • Spotlighted presentation in 2019 INFORMS RM&P Conference
  • Managing in-class Fulfillment at a Large Fashion E-Retailer (with G Perakis, Y Spantidakis). preprint
  • Leveraging Comparables for New Product Sales Forecasting (with L Baardman, I Levin and G Perakis), 2018, major revision in Operations Research. preprint
    • 2018 - First place in POMS Applied Research Challenge
    • 2018 - First place in POMS College of Supply Chain Management Best Student Paper Award
    • 2018 - Finalist in INFORMS Service Science Section Best Student Paper Award
    • 2018 - Honorable mention in MIT Operations Research Center Best Student Paper Competition
    • Preliminary version in 2018 MSOM Supply Chain Management SIG
  • Personalized Pricing with Feature Measurement Error (with N Gabouge, P Harsha, G Perakis).
  • Published Papers
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (with E Y Cramer et. al.), forthcoming in Proceedings of National Academy of Science. Final article
  • COVID 19: A Multiwave SIR Based Model for Learning Waves (with G Perakis, O Skali Lami, L Thayaparan), forthcoming in Production and Operations Management Society. Final article
    • 2021 - Finalist, INFORMS Service Science Best Student Paper Award
    • Preliminary version in 2021 MSOM Healthcare Operations SIG
  • Learning Personalized Product Recommendations with Customer Disengagement (with H Bastani, P Harsha, G Perakis), accepted in Manufacturing & Service Operations Management. Final article.
    • 2019 - Second Place in INFORMS Best Service Science Paper Award competition
    • 2019 - Honorable Mention in POMS College of Supply Chain Management Best Student Paper Award
  • Predicting Bike Usage for New York City's Bike Sharing System (with S Singhvi, P I Frazier, S G Henderson, E O'Mahony, D B Shmoys and D B Woodard), AAAI Workshop: Computational Sustainability, 2015. Final article.
Honors and Awards
  • 2021 - Finalist, Public Sector Operations Research Best Paper Award
  • 2021 - Finalist, INFORMS Service Science Best Student Paper Award
  • 2021 - Winner, ICSS Best Conference Paper Competition
  • 2021 - Finalist, Doing Good with Good OR Competition
  • 2021 - Honorable Mention in M&SOM Practice Based Competition
  • 2021 - MIT Operations Research Center Best Student Paper Award
  • 2019 - MIT Best Analytics Capstone Award (Student Mentor)
  • 2019 - Second Place in INFORMS Best Service Science Paper Award
  • 2019 - Honorable Mention in POMS College of Supply Chain Management Best Student Paper Award
  • 2018 - First place in POMS Applied Research Challenge
  • 2018 - First place in POMS College of Supply Chain Management Best Student Paper Award
  • 2018 - Finalist in INFORMS Service Science Section Best Student Paper Award
  • 2018 - Honorable mention in MIT Operations Research Center Best Student Paper Competition
  • 2011-2015 - TATA Scholarship for Students from India, Cornell University