About me

My name is Jean Pouget-Abadie, but non-French speakers can call me John. I am a fourth-year PhD candidate in Computer Science at Harvard University and currently working part-time at Google NY with Vahab Mirrokni. I am advised by Edoardo Airoldi and Salil Vadhan. Before Harvard, I was an undergraduate at Ecole Polytechnique, Paris. My recent research interests focus on causal inference and experimental design, particularly when network interference is present. I am a 2017-2018 Siebel scholar. You can find my CV (and a way to contact me) here. A link to my Google Scholar profile can be found here.

Quick links

  • Link to recent arXiv paper here.

  • An introduction to non-independent A/B tests can be found here.

Mini-CV

  • Part-time student researcher at Google
    Research internship with Vahab Mirrokni in the algorithms research group

  • Spotify summer 2017 internship
    Research internship with the Discovery Weekly team (music recommendation)

  • Facebook summer 2015 internship
    Research internship with Udi Weinsberg on the Core Data Science Team

  • Harvard University
    PhD program in Computer Science (Expected: 2018)

  • MILA, Université de Montréal
    Research internship with Yoshua Bengio in Deep Learning

  • École Polytechnique
    Diplôme d'ingénieur

Publications

  • Optimizing cluster-based randomized experiments under a monotonicity assumption
    Jean Pouget-Abadie, David C. Parkes, Vahab Mirrokni, Edoardo M. Airoldi. KDD 2018. [arXiv, KDD]

  • Testing for arbitrary interference on experimentation platforms
    Jean Pouget-Abadie, Martin Saveski, Guillaume Saint-Jacques, Weitao Duan, Ya Xu, Souvik Ghosh, and Edoardo M. Airoldi. In submission to Biometrika. [arXiv]

  • Detecting Network Effects: Randomizing over Randomized Experiments
    Martin Saveski, Jean Pouget-Abadie, Guillaume Saint-Jacques, Weitao Duan, Ya Xu, Souvik Ghosh, and Edoardo M. Airoldi. KDD 2017. [KDD]

  • Inferring Graphs from Cascades: A Sparse Recovery Framework
    Jean Pouget-Abadie and Thibaut Horel. ICML 2015 [arXiv, ICML]

  • Generative Adversarial Networks
    Ian Goodfellow, Jean Pouget-Abadie, Medhi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. NIPS 2014 [arXiv, NIPS]

  • Segmentation for Neural Machine Translation
    Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merriënboer, Kyung Hyun Cho, and Yoshua Bengio. SSST-8 @ EMNLP 2014 [pdf]

Slow links

  • Reading notes on submodular maximization can be found here.

  • Course website (for Harvard students) for CS181 "Intro to Machine Learning".