Dimitris Bertsimas

About

Dimitris Bertsimas is the Boeing Leaders for Global Operations Professor of Management, a Professor of Operations Research, and the Associate Dean for Online Education & Artificial Intelligence at MIT Sloan. He was named Vice Provost for Open Learning in September 2024. A faculty member since 1988, his research interests include optimization, stochastic systems, machine learning, and their applications, with recent work focusing on robust optimization, statistics, healthcare, transportation, and finance. Bertsimas has coauthored over 200 scientific papers and several books, and has supervised numerous doctoral and master's students. He is a member of the National Academy of Engineering and an INFORMS fellow, and has received multiple awards for his research and educational contributions. His educational background includes a BS in electrical engineering and computer science from the National Technical University of Athens, Greece, and an MS and PhD in operations research and applied mathematics from MIT.

Research topics

• Computer Science
• Political Science
• Medicine
• Economics
• Operations research
• Engineering
• Geography
• Artificial Intelligence
• Econometrics
• Mathematics

Selected publications

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

  Proceedings of the National Academy of Sciences · 2022 · 311 citations

  Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hu…

  DOI

• COVID-19 mortality risk assessment: An international multi-center study

  PLoS ONE · 2020 · 193 citations

  1st authorCorresponding

  Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admis…

  DOI

• The United States COVID-19 Forecast Hub dataset

  Scientific Data · 2022 · 126 citations

  Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hosp…

  DOI

• From predictions to prescriptions: A data-driven response to COVID-19

  Health Care Management Science · 2021 · 89 citations

  1st authorCorresponding
  PublisherOA PDFDOI

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

  medRxiv (Cold Spring Harbor Laboratory) · 2021 · 77 citations

  Abstract Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Fo…

  DOI

Recent grants

• SHB: Type II (INT): Collaborative Research: Algorithmic Approaches to Personalized Health Care

  NSF · $886k · 2012–2017

• Robust and Adaptive Optimization; a Tractable Approach to Optimization Under Uncertainty

  NSF · $450k · 2006–2009

Frequent coauthors

• Jean Pauphilet

  London Business School

  62 shared

• Jack Dunn

  53 shared

• Ying Daisy Zhuo

  48 shared

• Michael Lingzhi Li

  46 shared

• Ryan Cory-Wright

  Imperial College London

  46 shared

• John Silberholz

  Ross School

  45 shared

• Velibor V. Mišić

  University of California, Los Angeles

  43 shared

• Colin Pawlowski

  Nference (United States)

  42 shared

Labs

• MIT Laboratory for Financial EngineeringPI

Awards & honors

• Harold Larnder Prize (2016)
• Philip Morse Lecturship prize (2013)
• William Pierskalla best paper award in health care (2013)
• best paper award in Trapsoration (2013)
• Farkas Prize (2008)

Similar researchers at Massachusetts Institute of Technology

• James B. Orlinh-60
• Thomas Magnantih-44
• David D. Gamarnikh-39