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Raghav Singal

Assistant Professor of Business Administration

Email

raghav.singal@tuck.dartmouth.edu

Phone

603-646-0975

Personal Website

http://www.columbia.edu/~rs3566/

Degree

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

Areas of Expertise

Analytics, decision-making, modeling

Courses

Analytics

Bio

Professor Singal’s primary research interest is in the area of analytics. He investigates revenue-management applications in e-commerce and online advertising, as he aims to build data-driven models that help businesses understand complex systems and optimize decision-making. Singal worked in data science at Amazon, where he developed models for supply-chain attribution. He has previously developed online-advertising attribution models as a data-scientist intern at Adobe. Singal has also played tennis at a national level.

Current Research Topics

Analytics, revenue-management applications in e-commerce and online advertising, data-driven models in systems and decision-making


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 and Operations Research


Awards

  • Cheung-Kong Graduate School of Business Fellowship, Columbia University, 2019, 2018
  • Outstanding Teaching Assistant Award, Columbia University, 2018
  • INFORMS Jeff McGill Revenue Management and Pricing Student Paper Award, 2018
  • Highly Commended, The Undergraduate Awards, 2015
  • Best Poster Award at MIE Research Symposium, University of Toronto, 2015
  • MIE Summer Award, University of Toronto, 2015
  • Dan Cornacchia/Ernst & Young Scholarship, University of Toronto, 2013
  • University of Toronto Excellence Award, 2013
  • University of Toronto Scholar, 2011

Working Papers

  • With G. Michailidis and H. Ng, “Flow-based Attribution in Graphical Models: A Recursive Shapley Approach”

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, forthcoming
  • With J. Bhandari and D. Russo, “A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation,” Operations Research, 69(3), 2021
  • With M. B. Haugh, “How to Play Fantasy Sports Strategically (And Win),” Management Science, 67(1), 2020
  • 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