Faculty Directory

Raghav Singal

Assistant Professor of Business Administration; Wei-Chung Bradford Hu T’89 Faculty Fellow





Personal Website



PhD, Columbia University, 2020; BASc, University of Toronto, 2015

Areas of Expertise

Analytics, optimization, modeling




Professor Singal’s research focuses on analytics in a mix of marketplaces, including transportation, labor, sports, and advertising. He leverages data to develop application-driven models that help businesses evaluate current systems and optimize decision-making. Outside academia, Singal enjoys playing tennis and has played at a national level.

Current Research Topics

Marketplace analytics, data-driven modeling and optimization, attribution

Professional Activities

Academic positions

  • Assistant Professor of Business Administration, Tuck School of Business, 2021–present

Nonacademic positions

  • Data Scientist Intern (Supply Chain Optimization Technologies), Amazon, 2020–21
  • Data Scientist Intern (Media and Advertising Solutions), Adobe, 2017
  • Quantitative Research Co-op (Asset Mix and Risk), Ontario Teachers’ Pension Plan, 2013–14

Editorial positions (reviewer)

  • Management Science
  • Operations Research
  • Manufacturing & Service Operations Management
  • Production and Operations Management
  • Transportation Science


  • Harvey H. Bundy III T’68 Faculty Fellow, 2022
  • 2nd place, Michael H. Rothkopf Junior Researcher Paper Prize (INFORMS Auctions and Market Design), 2022
  • Finalist, Data-driven Research Challenge (INFORMS Revenue Management and Pricing), 2021
  • 2nd place, Jeff McGill Student Paper Award (INFORMS Revenue Management and Pricing), 2019
  • Cheung-Kong Graduate School of Business Fellowship, Columbia University, 2019, 2018
  • Outstanding Teaching Assistant Award, Columbia University, 2018
  • Highly Commended, The Undergraduate Awards, 2015
  • University of Toronto Excellence Award, 2013

Working Papers

  • With M. B. Haugh, “Bounding Counterfactuals in Hidden Markov Models and Beyond,” preliminary version appeared in ICML, 2023
  • With O. Besbes, V. Goyal, and G. Iyengar, “Effective Wages under Workforce Scheduling with Heterogeneous Time Preferences”
  • With G. Michailidis and H. Ng, “Flow-based Attribution in Graphical Models: A Recursive Shapley Approach,” preliminary version appeared in ICML, 2021

Selected Publications

  • With O. Besbes, A. Desir, V. Goyal, and G. Iyengar, “Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising,” Management Science, 68(10), 2022
  • With M. B. Haugh, “How to Play Fantasy Sports Strategically (And Win),” Management Science, 67(1), 2021
  • With J. Bhandari and D. Russo, “A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation,” Operations Research, 69(3), 2021
  • With T. C. Y. Chan, “A Bayesian Regression Approach to Handicapping Tennis Players Based on a Rating System,” Journal of Quantitative Analysis in Sports, 14(3), 2018
  • With T. C. Y. Chan, “A Markov Decision Process-based Handicap System for Tennis,” Journal of Quantitative Analysis in Sports, 12(4), 2016