Prof. Dimitris J Bertsimas

Vice Provost for Open Learning
Boeing Leaders for Global Operations Professor of Management
Professor of Operations Research

Primary DLC

MIT Sloan School of Management

MIT Room: E62-560

Assistant

Stephanie Tran
stran15@mit.edu

Areas of Interest and Expertise

Air Safety
eCommerce
Financial Engineering
Information Technology
Operations Research
Optimization
Revenue Management
Statistics

Research Summary

Professor Bertsimas has coauthored more than 200 scientific papers and the following books: Introduction to Linear Optimization (with J. Tsitsiklis, Athena Scientific and Dynamic Ideas, 2008); Data, Models, and Decisions (with R. Freund, Dynamic Ideas, 2004); Optimization over Integers (with R. Weismantel, Dynamic Ideas, 2005); and The Analytics Edge (with A. O'Hair andW. Pulleyblank, Dynamic Ideas, 2016). He is former department editor of Optimization for Management Science and of Operations Research in Financial Engineering. Bertsimas has supervised 59 doctoral and 31 Master students. He is currently supervising 22 doctoral students. A member of the National Academy of Engineering and an INFORMS fellow, he has received numerous research awards, including the Harold Larnder Prize (2016), the Philip Morse Lecturship prize (2013), the William Pierskalla best paper award in health care (2013), best paper award in Trapsoration (2013), the Farkas Prize (2008), the Erlang Prize (1996), the SIAM Prize in Optimization (1996), the Bodossaki Prize (1998), and the Presidential Young Investigator Award (1991–1996). He has also received recognition for his educational contributions: The Jamieson prize (2013) and the Samuel M. Seegal prize (1999).

Bertsimas holds a B.S. in electrical engineering and computer science from the National Technical University of Athens, Greece, as well as an MS in operations research and a Ph.D. in applied mathematics and operations research from MIT.

Recent Work

  • Video

    2.28-29.24-Ethics-Bertsimas

    February 28, 2024Conference Video Duration: 28:57
    2024 MIT AI Conference: Tech, Business, and Ethics

    Dimitris Bertsimas - 2019 Citi-NY

    November 6, 2019Conference Video Duration: 42:49

    Interpretable AI and its Applications in Financial Services

    This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable. Optimal classification (OCT) and regression (ORT) trees are introduced for prediction and prescription with and without hyperplanes. It will be shown that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. These optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional and recurrent) neural networks and comparable performance in a variety of real world data sets. Finally, a variety of optimal trees applications in financial services will be discussed.

    2019 MIT Citi Conference in NYC

    AI in LIfe Science 2018 - Dimitris Bertsimas

    December 4, 2018Conference Video Duration: 28:23

    Interpretable AI

    This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional, and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale, and provide state of the art performance compared to black box methods.

    2018 MIT AI in Life Sciences and Healthcare Conference

    Personalized Medicine and Healthcare

    October 18, 2016MIT Faculty Feature Duration: 27:28

    Dimitris Bertsimas
    Boeing Leaders for Global Operations Professor of Management