About

I am currently a Postdoctoral Fellow at Columbia Data Science Institute (DSI) and an Affiliate Assistant Professor at Georgia Tech ISyE. I will be starting in person at Georgia Tech in Fall 2025.

My research interests lie at the intersection of learnability in machine learning and statistics, online algorithms and optimization in decision-making. I received my PhD at the Operations Research Center and Laboratory for Information and Decision Systems at MIT, advised by Prof. Patrick Jaillet. Prior to my PhD, I graduated as the valedictorian of Ecole Polytechnique in 2019 with a M.S. degree in Applied Mathematics, and a B.S. degree in Mathematics, Computer Science and Physics. During my PhD and my undergraduate studies, I had the pleasure to work with Prof. Steve Hanneke, Prof. Aryeh Kontorovich, Prof. Alexandre Jacquillat, Prof. Jesús De Loera, Prof. Laurent Massoulié and Prof. Gabriel Peyré. Here is my Curriculum Vitae (CV).

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Research

Journal Publications

• Universal Regression with Adversarial Responses
  Moïse Blanchard and Patrick Jaillet
  Annals of Statistics, 51 (3), 2023
• Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal
  Moïse Blanchard, Junhui Zhang, and Patrick Jaillet
  Mathematics of Operations Research, 2024. Previous version appeared at the 36th Annual Conference on Learning Theory (COLT), 2023
  Prize for solving a COLT 2019 open problem in memory-constrained optimization [Link]
• Fréchet Mean Set Estimation in the Haussdorff Metric, via Relaxation
  Moïse Blanchard and Adam Jaffe
  Bernoulli, 2023
• Probabilistic bounds on the k-Traveling Repairman Problem and the k-Traveling Salesman Problem
  Moïse Blanchard, Alexandre Jacquillat, and Patrick Jaillet
  Mathematics of Operations Research, 2021 Additional Results and Extensions
  Winner of the INFORMS Transportation Science & Logistics (TSL) Best student paper award, 2023
• On the length of monotone paths in polyhedra
  Moïse Blanchard, Jesús A. De Loera, and Quentin Louveaux
  SIAM Journal on Discrete Mathematics 35 (3), 2021
  Winner of Rivot Medal for outstanding research, French Science Academia, 2019 [Link]

Conference Publications

• Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
  Moïse Blanchard
  65th Symposium on Foundations of Computer Science (FOCS), 2024
• Correlated Bernoulli Process
  Moïse Blanchard, Doron Cohen, and Aryeh Kontorovich
  37th Annual Conference on Learning Theory (COLT), 2024
• Tight Bounds for Local Glivenko-Cantelli
  Moïse Blanchard and Václav Voráček
  35th International Conference on Algorithmic Learning Theory (ALT), 2024
• Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes
  Moïse Blanchard, Junhui Zhang, and Patrick Jaillet
  37th Neural Information Processing Systems (NeurIPS), 2023
• Universal Online Learning: an Optimistically Universal Learning Rule
  Moïse Blanchard
  35th Annual Conference on Learning Theory (COLT), 2022
  Prize for solving COLT 2021 open problems in universal learning [Link]
  Best student paper runner-up, COLT 2022
• Universal Online Learning with Bounded Loss: Reduction to Binary Classification
  Moïse Blanchard and Romain Cosson
  35th Annual Conference on Learning Theory (COLT), 2021
• Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
  Moïse Blanchard and Amine Bennouna
  10th International Conference on Learning Representations (ICLR), 2022
• Universal Online Learning with Unbounded Losses: Memory Is All You Need
  Moïse Blanchard, Romain Cosson, and Steve Hanneke
  33rd International Conference on Algorithmic Learning Theory (ALT), 2022

Preprints

• Agnostic Smoothed Online Learning
  Moïse Blanchard
  2024
• Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers
  Moïse Blanchard and Patrick Jaillet
  Submitted, 2024
• Adversarial Rewards in Universal Learning for Contextual Bandits
  Moïse Blanchard, Steve Hanneke, and Patrick Jaillet
  Submitted, 2023
• Contextual Bandits and Optimistically Universal Learning
  Moïse Blanchard, Steve Hanneke, and Patrick Jaillet
  Submitted, 2023

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Teaching

• MIT 6.215/15.093J | Optimization Methods, Fall 2021
  Teaching Assistant
  
• MIT 15.072 | Advanced Analytics Edge, Fall 2020
  Teaching Assistant
  
• Classes Préparatoires, Mathematics, 2017-2019
  Instructor in mathematics for French undergraduate students
  

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Selected Talks

• Universal learning: consistency under minimal data assumptions
  Invited talk, Columbia University, Applied probability seminar, 2024
• Query-complexity/memory trade-offs in convex optimization and feasibility problems
  FOCS, 2024
  Invited talk, Google learning theory seminar, 2024
• Universal learning for decision-making
  Invited talk, Imperial College London, Analytics and Operations, 2024
  Invited talk, University of California San Diego, HDSI, 2024
  Invited talk, University of Southern California, ISE, 2024
  Invited talk, Duke University, Fuqua Business School, Decision Sciences, 2024
  Invited talk, New York University, Stern Business School, OM, 2024
  Invited talk, Georgia Tech University, ISyE, 2023
  Invited talk, Northwestern University, Kellogg Business School, Operations, 2023
  Cornell Young Researcher Workshop, 2023
  Artificial Intelligence in Operations, INFORMS Annual Meeting, 2023
• Probabilistic bounds on the k-TSP and TRP
  Transportation Science and Logistics Student Paper Prize, INFORMS Annual Meeting, 2023
• Memory-constrained convex optimization: Cutting-planes is Pareto-optimal
  Invited talk, TTIC (Toyota Technological Institute at Chicago), 2023
  COLT, 2023
• Universal regression with adversarial responses
  Invited talk, Université Pierre et Marie Curie, Laboratoire Jacques-Louis Lions GTT, 2022
  INFORMS Annual Meeting 2022
  MIT LIDS Conference, 2022
• Realizable universal learning
  ''Universal Online Learning: an Optimistically Universal Learning Rule'', COLT 2022 (Video)
  ''Reduction from Binary Classification for Universal Online Learning'', COLT 2022 (Video)
  ''Universal Online Learning with Unbounded Loss: Memory is All you Need'', ALT 2022
• Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
  MIT SIAM Seminar 2021 (Video)

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Awards and Honors

• Columbia DSI postdoctoral fellowship, 2024
• Winner of the INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023
• Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023
• COLT 2022 Best student paper runner-up, 2022
  Paper: Universal online learning: an Optimistically universal learning rule
• Best Presentation at LIDS Student Conference, MIT, 2022
  Paper: Universal Regression with Adversarial Responses (with Prof. Patrick Jaillet)
• Bronze medal, Alibaba Global Mathematics Competition, 2022
• Honorable Mention, Alibaba Global Mathematics Competition, 2021
• 2nd Prize, The East Coast Data Open by Citadel, 2020
• Laplace medal, French Science Academia, 2019
  Distinction as Valedictorian of Ecole Polytechnique
  
• Rivot medal, French Science Academia, 2019
  Best student research for the paper: On the length of monotone paths in polyhedra (with Prof. Jesus De Loera and Prof. Quentin Louveaux)
  
• Bronze Medal, 46th International Physics Olympiad (IPhO), 2015
• Bronze Medal, 55th International Mathematics Olympiad (IMO), 2014
• Silver Medal, 18th Junior Balkan Mathematics Olympiad (JBMO), 2014
• 1st Prize, Concours Général in Mathematics, 2014