Georgia Perakis

About

Georgia Perakis is the William F. Pounds Professor of Management and a Professor of Operations Management, Operations Research, and Statistics at the MIT Sloan School of Management. She has been on the faculty at MIT Sloan since July 1998. Her teaching spans undergraduate, MSc, PhD, MBA, and EMBA programs, and she has received multiple awards for excellence in teaching, including the Graduate Student Council Teaching Award, the Jamieson Prize, and the Teacher of the Year award in 2017. Her research focuses on analytics and artificial intelligence, particularly at the intersection of optimization and machine learning, with applications in pricing, revenue management, supply chains, healthcare, and energy. She investigates the theory and practice of analytics, aiming to solve complex and practical problems in various domains. Perakis has published extensively in leading journals such as Operations Research, Management Science, and Mathematical Programming, and has received numerous honors including the NSF CAREER Award, the PECASE Award, and election as an INFORMS Fellow and Distinguished MSOM Fellow. She has been recognized for her leadership and innovation in supply chain management and operations research, receiving awards such as the INFORMS MSOM Distinguished Service Award and the Salzburg Medallion from Syracuse University. Perakis has also served in leadership roles at MIT Sloan, including co-director of the Operations Research Center and Associate Dean for SERC. She is…

Research topics

• Computer science
• Mathematical optimization
• Economics
• Business
• Microeconomics

Selected publications

• End-to-End Learning for Optimization via Constraint-Enforcing Approximators

  Proceedings of the AAAI Conference on Artificial Intelligence · 2023-06-26 · 10 citations

  articleOpen access

  In many real-world applications, predictive methods are used to provide inputs for downstream optimization problems. It has been shown that using the downstream task-based objective to learn the intermediate predictive model is often better than using only intermediate task objectives, such as prediction error. The learning task in the former approach is referred to as end-to-end learning. The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore,…

  PublisherOA PDFDOI

• Learning the Minimal Representation of a Continuous State-Space Markov Decision Process from Transition Data

  Management Science · 2024-09-26 · 2 citations

  article

  This paper proposes a framework for learning the most concise Markov decision process (MDP) model of a continuous state-space dynamic system from observed transition data. This setting is encountered in numerous important applications, such as patient treatment, online advertising, recommender systems, and estimation of treatment effects in econometrics. Most existing methods in offline reinforcement learning construct functional approximations of the value or the transition and reward functions…

  PublisherDOI

• Introduction: Frontiers in Operations

  Manufacturing & Service Operations Management · 2024-07-01 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Tight mixed-integer optimization formulations for prescriptive trees

  Machine Learning · 2025-05-29 · 1 citations

  articleOpen accessSenior author

  Abstract We focus on modeling the relationship between an input feature vector and the predicted outcome of a trained decision tree using mixed-integer optimization. This can be used in many practical applications where a decision tree or a tree ensemble is incorporated into an optimization problem to model the predicted outcomes of a decision. We propose novel tight mixed-integer optimization formulations for this problem. Existing formulations can be shown to have linear relaxations that have…

  PublisherOA PDFDOI

• Inter-Series Transformer: Attending to Products in Time Series Forecasting

  arXiv (Cornell University) · 2024-08-07 · 1 citations

  preprintOpen access

  Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited. In this work, we explore the application of Transformer-based models to supply ch…

  PublisherOA PDFDOI

Recent grants

• Price of Anarchy and Its Applications

  NSF · $260k · 2008–2012

• Tractable Markdown Optimization for an E-tailer

  NSF · $235k · 2012–2017

• Alleviating Travel Delay Uncertainties in Traffic Assignment and Traffic Equilibrium

  NSF · $315k · 2008–2012

Frequent coauthors

• Maxime C. Cohen

  35 shared

• Hongqiao Chen

  Nanjing University

  26 shared

• Ming Hu

  26 shared

• Pavithra Harsha

  22 shared

• Lennart Baardman

  Ross School

  20 shared

• Divya Singhvi

  20 shared

• Retsef Levi

  18 shared

• Anna Papush

  IBM Research - Thomas J. Watson Research Center

  18 shared

Labs

• Food Supply Chain Analytics and Sensing InitiativePI

Awards & honors

• Manufacturing and Service Operations Management Distinguishe…
• Salzburg Medallion from the Whitman School of Management at…
• INFORMS Best Paper of the Service Science Section Cluster Aw…
• INFORMS honors (2018)

Similar researchers at Massachusetts Institute of Technology

• Dimitris Bertsimash-91
• James B. Orlinh-60
• Thomas Magnantih-44