Bradley Sturt

PhD candidate in Operations Research at MIT

Contact: bsturt (at) mit (dot) edu

CV, Google scholar


I am a third-year PhD candidate in the Operations Research Center at MIT, where I am advised by Dimitris Bertsimas. My research interests lie at the intersection of data-driven optimization, nonparametric statistics, algorithms and complexity, and their applications in operations management and analytics.

I did my undergraduate studies in Computer Engineering and the Hoeft Technology and Management Program at the University of Illinois in Urbana, Champaign, where I was a Chancellor's Scholar. Prior to starting my Ph.D, I worked with Jiawei Han and Marta Gonzalez in data mining, with applications in recommendation systems and transportation.

I love teaching, and have been involved with courses on data analytics and optimization for MBA, PhD, MBAn, and undergraduate students. I have taught tutorials on Unix, software development, and analytics to different organizations at MIT.


  1. From Data to Multi-Stage Decisions (with D. Bertsimas and S. Shtern), In Preparation, (2018).
  2. Data-Driven Two-Stage Adaptive Optimization (with D. Bertsimas and S. Shtern), Under Review, (2018).
  3. Computation of exact bootstrap confidence intervals: complexity and deterministic algorithms (with D. Bertsimas), Under Review (2017). (Preprint, Optimization-Online).
    Second Place in the INFORMS George Nicholson Student Paper Competition, 2017.
  4. The path most traveled: Travel demand estimation using big data resources (with J.L. Toole, S. Colak, L.P. Alexander, A. Evsukoff, and M.C. González), Transportation Research Part C: Emerging Technologies (2015) (Final article)
  5. Personalized Entity Recommendation in Heterogeneous Information Networks with Implicit User Feedback (with X. Yu, X. Ren, Y. Sun, Q. Gu, U. Khandelwal, B. Norick, and J. Han), Proceedings of the 7th ACM international conference on Web Search and Data Mining (WSDM) (2014) (Final article).
  6. Recommendation in heterogeneous information networks with implicit user feedback (with X. Yu, X. Ren, Y. Sun, U. Khandelwal, Q. Gu, B. Norick, and J. Han), Proceedings of the 7th ACM conference on Recommender systems (2013) (Final article).