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Jean Pauphilet

Assistant Professor of Management Science and Operations

MSc (Ecole Polytechnique) PhD (MIT)

Dr Pauphilet’s research develops data analytics methods for positive-impact applications. His methodological work addresses the challenge of prediction-based decision-making by designing new algorithms for machine learning, large-scale optimization, and optimization under uncertainty. In addition, he has collaborated with several medical institutions, NGOs, and companies, for example, to improve hospital operations or reduce ocean plastic pollution using analytics. His work has been published in the likes of Operations Research, Manufacturing & Service Operations Management, Mathematical Programming, and Journal of Machine Learning Research. 

He holds a PhD in Operations Research from the Massachusetts Institute of Technology (MIT) and an MSc from Ecole Polytechnique (Paris). Dr Pauphilet has also consulted for various companies on their analytics strategies in the energy, IT, and healthcare sectors, alongside having worked as an analyst for the French venture capital fund, Ventech.

Jean Pauphilet talks about his research on analytics for hospital operations. Click here to read the paper




Jean Pauphilet and Baizhi Song talk about optimising the path towards plastic-free oceans

  • Healthcare operations
  • Machine learning
  • Discrete optimisation
  • Optimisation under uncertainty

2024

Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items

Pauphilet J

Manufacturing & Service Operations Management 2024 Vol 26:1 p 11-27

Robust combination testing: methods and application to COVID-19 detection

Jain S; Jonasson J O; Pauphilet J; Ramdas K

Management Science 2024 Vol 70:4 p 2661-2681

2023

A new perspective on low-rank optimization

Bertsimas D; Cory-Wright R; Pauphilet J

Mathematical Programming 2023 Vol 202 p 47-92

Minkowski Centers via Robust Optimization: Computation and Applications

den Hertog D; Pauphilet J; Soali M Y

Operations Research 2023 In Press

Robust convex optimization: a new perspective that unifies and extends

Bertsimas D; den Hertog D; Pauphilet J; Zhen J

Mathematical Programming 2023 Vol 200:2 p 877-918

2022

Predicting inpatient flow at a major hospital using interpretable analytics

Bertsimas D; Pauphilet J; Stevens J; Tandon M

Manufacturing & Service Operations Management 2022 Vol 24:6 p 2809-2824

Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality

Bertsimas D; Cory-Wright R; Pauphilet J

Journal of Machine Learning Research 2022 Vol 23:13 p 1-35 In Press

2021

A unified approach to mixed-integer optimization with logical constraints

Bertsimas D; Cory-Wright R; Pauphilet J

SIAM Journal on Optimization 2021 Vol 31:3 p 2340-2367

Direct optimization across computer generated reaction networks balances materials use and feasibility of synthesis plans for molecule libraries

Gao H; Pauphilet J; Struble T; Coley C; Jensen K F

Journal of Chemical Information and Modeling 2021 Vol 61:1 p 493-504

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

Bertsimas D; et al.

Health Care Management Science 2021 Vol 24 p 253-272

Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints

Bertsimas D; Cory-Wright R; Pauphilet J

Operations Research 2021 In Press

Probabilistic Guarantees in Robust Optimization

Bertsimas D; den Hertog D; Pauphilet J

SIAM Journal on Optimization 2021 Vol 31:4 p 2893-2920

Sparse Classification: A Scalable Discrete Optimization Perspective

Bertsimas D; Pauphilet J

Machine Learning 2021 Vol 110 p 3177-3209

2020

Certifiably optimal sparse inverse covariance estimation

Bertsimas D; Lamperski J; Pauphilet J

Mathematical Programming 2020 Vol 184:1-2 p 491-530

Sparse regression: scalable algorithms and empirical performance

Bertsimas D; Pauphilet J; Van Parys B

Statistical Science 2020 Vol 35:4 p 555-578


Teaching portfolio

Our teaching offering is updated annually. Faculty and programme material are subject to change.