Talks

Online Matching with Heterogenous Supply and Minimum Allocation Guarantees
  • HBS: Dec 17, 4:15-4:35pm EST

Model-free Approximate Bayesian Learning for Large-scale Conversion Funnel Optimization
  • HBS: Dec 17, 4:50-5:10pm EST
  • CORS: June 6, 11:35-11:55am PDT

Effective Wages under Workforce Scheduling with Heterogeneous Time Preferences
  • Rothkopf Prize: Oct 17, 5-6:15pm EDT
  • CORS: June 5, 5:15-5:35pm PDT
  • RMP (Spotlight): June 22, 1-1:45pm CT
  • MSOM (SIG): June 26, 3:45-4:30pm CEST

Education

Ph.D. in Operations Research
2015 - 2020
Columbia University
CKGSB Fellow

B.A.Sc. in Industrial Engineering
2010 - 2015
University of Toronto
Ranked 1 (out of 100 students)

Papers

Counterfactual Analysis in Dynamic Latent State Models
Martin Haugh and Raghav Singal
Working paper
[arXiv]

Effective Wages under Workforce Scheduling with Heterogeneous Time Preferences
Omar Besbes, Vineet Goyal, Garud Iyengar, and Raghav Singal
Major revision at Manufacturing & Service Operations Management
2nd place in Rothkopf Junior Researcher Paper Prize, 2022
Spotlight at RMP and MSOM, 2022
[SSRN] [Video]

Flow-based Attribution in Graphical Models: A Recursive Shapley Approach
Raghav Singal, George Michailidis, and Hoiyi Ng
Working paper
Preliminary version appeared in ICML (spotlight), 2021
[ICML] [Video]

Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising
Raghav Singal, Omar Besbes, Antoine Desir, Vineet Goyal, and Garud Iyengar
Management Science, 2022
Preliminary version appeared in WWW, 2019
2nd place in INFORMS RMP Student Paper Award, 2019
[Journal] [SSRN] [WWW] [Video] [Press]

A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation
Jalaj Bhandari, Daniel Russo, and Raghav Singal
Operations Research, 2021
Extended abstract appeared in COLT, 2018
[Journal] [arXiv] [COLT]

How to Play Fantasy Sports Strategically (and Win)
Martin Haugh and Raghav Singal
Management Science, 2021
Finalist in MIT SSAC, 2018
[Journal] [SSRN] [Video] [Press]

A Bayesian Regression Approach to Handicapping Tennis Players Based on a Rating System
Timothy C. Y. Chan and Raghav Singal
Journal of Quantitative Analysis in Sports, 2018
[Journal] [PDF]

A Markov Decision Process-based Handicap System for Tennis
Timothy C. Y. Chan and Raghav Singal
Journal of Quantitative Analysis in Sports, 2016
[Journal] [PDF]

Teaching

Instructor
Analytics, MBA (Dartmouth), Rated 5.25/6, Fall 2021, 2022
Optimization Models and Methods, MS (Columbia), Rated 4.50/5, Spring 2019

Co-instructor
Introduction to Algorithms, SHP (Columbia), Fall 2018
Graph Theory by Example, SHP (Columbia), Spring 2018

Case: Analytics in American Football: A New Frontier
C. Daniel Guetta, Raghav Singal, and John Wolfe
Columbia CaseWorks, 2020
Used in the Business Analytics II elective at Columbia Business School

Industry

Amazon
Data Scientist Intern
October 2020 - June 2021 (NYC/Seattle, USA)
Developed models for attribution in supply chain

Adobe
Data Scientist Intern
June 2017 - August 2017 (San Jose, USA)
Developed models for attribution in online advertising

Ontario Teachers' Pension Plan
Quantitative Research Co-op
May 2013 - August 2014 (Toronto, Canada)
Worked as a front office quant

Awards

2nd place in Michael H. Rothkopf Junior Researcher Paper Prize (2022)

Harvey H. Bundy III T’68 Faculty Fellow (2022)

Finalist in RMP Data-Driven Research Challenge (2021)

2nd place in INFORMS RMP Jeff McGill Student Paper Award (2019) [Press]

Cheung-Kong Graduate School of Business (CKGSB) Fellowship (2018) [Press]

Outstanding Teaching Assistant Award at Columbia University (2018)

Highly Commended by The Undergraduate Awards (2015) [Press]

University of Toronto Excellence Award (2013)

Contact

Mailing address
Raghav Singal
100 Tuck Hall
Chase 304
Tuck School of Business
Hanover NH 03755 USA

Email
rs3566 [at] columbia [dot] edu