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.
- Personal website
- jpauphilet@london.edu
- +44 (0)20 7000 8822
- Healthcare operations
- Machine learning
- Discrete optimisation
- Optimisation under uncertainty
2023
A new perspective on low-rank optimization
Bertsimas D; Cory-Wright R; Pauphilet J
Mathematical Programming 2023 Online first
Minkowski Centers via Robust Optimization: Computation and Applications
den Hertog D; Pauphilet J; Soali M Y
Operations Research 2023 In Press
Robust combination testing: methods and application to COVID-19 detection
Jain S; Jonasson J O; Pauphilet J; Ramdas K
Management Science 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
Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
Pauphilet J
Manufacturing & Service Operations Management 2022 In Press
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.