About me

My name is Jean Pouget-Abadie, but non-French speakers can call me John. I am a research scientist at Google Research New York on the Algorithms & Optimization team, led by Vahab Mirrokni. Before joining Google, I was a PhD student in Computer Science at Harvard University, advised by Edoardo Airoldi and Salil Vadhan. Prior to that, 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. In the past, I looked into using neural networks to generate distributions. I was a 2017-2018 Siebel scholar.

Resume Google Scholar Google Research Profile


  • Senior Research Scientist at Google
    with Vahab Mirrokni in the algorithms research group

  • Spotify summer internship
    with the Discover Weekly team (music recommendation)

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

  • Harvard University
    PhD program in Computer Science (2018)

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

  • École Polytechnique
    Diplôme d'ingénieur (2014)


  • Design and analysis of bipartite experiments under a linear exposure-response model
    Christopher Harshaw, Fredrik Savje, David Eisenstat, Vahab Mirrokni, Jean Pouget-Abadie. forthcoming [arXiv]

  • Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
    Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens. NeurIPS 2021 [NeurIPS]

  • Variance reduction of bipartite experiments through Correlation Clustering
    Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni. NeurIPS 2019 [NeurIPS]

  • Randomized Experimental Design via Geographic Clustering
    David Rolnick, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab Mirrokni, Amir Najmi. KDD 2019 [KDD]

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

  • 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. Biometrika 2019. [arXiv, Biometrika]

  • 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. NeurIPS 2014 [arXiv, NeurIPS]

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

Misc. blog posts

  • [2015] Reading notes on submodular maximization can be found here.

  • [2017] An introduction to non-independent A/B tests can be found here.

  • [2020] An introduction to bipartite randomized experiments is in progress.