Eytan Bakshy I’m a Director and Research Scientist in Meta’s Applied AI organization, where I am helping build the organization and its approach to improving AI agents through expert-level data. Previously, I led Adaptive Experimentation in Meta’s Central Applied Science organization, where my team developed methods and systems for efficient learning and optimization across domains including AI, recommender systems, hardware design, and physical modeling. My research interests include preference learning, Bayesian optimization, multi-armed bandits, and active learning. I care deeply about translating advances in learning and optimization into durable shared infrastructure, including open-source tools such as Ax and BoTorch.

Selected publications

Here are a few of my recent publications. For a more complete list of publications and activities, see my Google Scholar page and likely outdated CV.

Bayesian optimization, active learning, and reinforcement learning

• Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning.
  Hong Namkoong, Sam Daulton, Eytan Bakshy. TMLR 2026.
• Informed Initialization for Bayesian Optimization and Active Learning.
  Carl Hvarfer, David Eriksson, Eytan Bakshy, Max Balandat. NeurIPS 2025.
• α-PFN: In-Context Learning Entropy Search.
  Tom Viering, Steven Adriaensen, Heri Rakotoarison, Samuel Müller, Carl Hvarfner, Frank Hutter, Eytan Bakshy. Frontiers in Probabilistic Inference 2025.
• Ax: A Platform for Adaptive Experimentation.
  Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy. AutoML 2025.
• Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments.
  Qing Feng, Sam Daulton, Ben Letham, Max Balandat, Eytan Bakshy. KDD 2025.
• Scalable Gaussian Processes with Latent Kronecker Structure.
  Andy Lin, Sebastian Ament, Max Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy. ICML 2025.
• Robust Gaussian Processes via Relevance Pursuit.
  Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Max Balandat, Eytan Bakshy. NeurIPS 2024.
• Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes.
  Syrine Belakaria, Ben Letham, Janna Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy Bakshy. NeurIPS 2024
• Joint Composite Latent Space Bayesian Optimization.
  Natalie Maus, Jerry Lin, Maximilian Balandat, Eytan Bakshy. ICML 2024.
• Response Time Improves Gaussian Process Models for Perception and Preferences.
  Michael Shvartsman, Ben Letham, Eytan Bakshy, Stephen Keeley. UAI 2024.
• Unexpected Improvements to Expected Improvement for Bayesian Optimization.
  Sebastian Ament, Sam Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy. NeurIPS 2023.
• Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information.
  Sam Daulton, Max Balandat, Eytan Bakshy. ICML 2023.
• Sparse Bayesian Optimization.
  Sulin Liu, Qing Feng, David Eriksson, Ben Letham, Eytan Bakshy. AISTATS 2023.
• qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization.
  Raul Astudillo, Jerry Lin, Eytan Bakshy, Peter Frazier. AISTATS 2023.
• Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings.
  Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson. AISTATS 2023.
• Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization.
  Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, Eytan Bakshy. NeurIPS 2022.
• Robust Multi-Objective Bayesian Optimization Under Input Noise.
  Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A Osborne, Enlu Zhou, Eytan Bakshy. ICML 2022.
• Interpretable Personalized Experimentation.
  Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy. KDD 2022.
• Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces.
  Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy. UAI 2022.
• Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes.
  Jerry Lin, Raul Astudillo, Peter Frazier, Eytan Bakshy. AISTATS 2022.
• Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation.
  Benjamin Letham, Eytan Bakshy, Michael Shvartsman. AISTATS 2022.
• Bayesian Optimization with High-Dimensional Outputs.
  Wesley Maddox, Max Balandat, Andrew Gordon Wilson, Eytan Bakshy. NeurIPS 2021.
• Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs.
  Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier. NeurIPS 2021.
• Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement.
  Samuel Daulton, Max Balandat, Eytan Bakshy. NeurIPS 2021.
• Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization.
  Samuel Daulton, Max Balandat, Eytan Bakshy. NeurIPS 2020.
• Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization.
  Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy. NeurIPS 2020.
• BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization.
  Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy. NeurIPS 2020.
• High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization.
  Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy. NeurIPS 2020.
• Preference Learning for Real-World Multi-Objective Decision Making.
  Jerry Lin, Eytan Bakshy. ICML 2020 Workshop on Real World Experiment Design and Active Learning.
• Bayesian Optimization for Policy Search via Online-Offline Experimentation.
  Ben Letham & Eytan Bakshy. JMLR 2019.
• Constrained Bayesian Optimization with Noisy Experiments.
  Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy. Bayesian Analysis 2019.
• Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints.
  Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy. NeurIPS 2019 Workshop of Safe Reinforcement Learning.
• Real-world Video Adaptation with Reinforcement Learning.
  Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy.
  ICML 2019 Workshop on Reinforcement Learning for Real Life.
• AE: A domain-agnostic platform for adaptive experimentation.
  Eytan Bakshy, Lili Dworkin, Brian Karrer, Konstantin Kashin, Ben Letham, Ashwin Murthy, Shaun Singh. NeurIPS 2018 Systems for ML Workshop.
• Scalable Meta-Learning for Bayesian Optimization.
  Matthias Feurer, Benjamin Letham, Eytan Bakshy. ICML 2018 AutoML Workshop.

Causal Inference and Experimentation

• Interpretable Personalized Experimentation.
  Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy. KDD 2022.
• PlanAlyzer: assessing threats to the validity of online experiments.
  Emma Tosch, Eytan Bakshy, Emery D Berger, David D Jensen, J Eliot B Moss. Communications of the ACM, 2021.
• Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects.
  Dean Eckles & Eytan Bakshy. Journal of the American Statistical Association, 2020.
• Shrinkage Estimators in Online Experiments.
  Drew Dimmery, Eytan Bakshy, Jasjeet Sekhon. KDD 2019.
• Design and Analysis of Benchmarking Experiments for Distributed Internet Services.
  Eytan Bakshy & Eitan Frachtenberg. WWW 2015.
• Designing and Deploying Online Field Experiments.
  Eytan Bakshy, Dean Eckles, Michael Bernstein. WWW 2014.
• Uncertainty in Online Experiments with Dependent Data: An Evaluation of Bootstrap Methods.
  Eytan Bakshy & Dean Eckles. KDD 2013.

Field experiments

• Social Influence and Political Mobilization: Further Evidence from a Randomized Experiment in the 2012 U.S. Presidential Election.
  Jason Jones, Robert Bond, Eytan Bakshy, Dean Eckles, James Fowler. PLoS ONE, 2017.
• Estimating Peer Effects with Peer Encouragement Designs.
  Dean Eckles, Rene Kizilcec, Eytan Bakshy. PNAS 2016.
• Selection Effects in Online Sharing: Consequences for Peer Adoption.
  Sean J. Taylor, Eytan Bakshy, Sinan Aral. EC 2013.
• Social Influence in Social Advertising: Evidence from Field Experiments.
  Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn. EC 2012.
• The Role of Social Networks in Information Diffusion.
  Eytan Bakshy, Itamar Rosenn, Cameron A. Marlow, Lada A. Adamic. WWW 2012.

Observational studies

• Exposure to Ideologically Diverse News and Opinion on Facebook.
  Eytan Bakshy, Solomon Messing, Lada Adamic. Science, 2015.
  Additional materials: Technical notes on paper and future research, replication materials and additional data.
• Quantifying the Invisible Audience in Social Networks.
  Michael Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer. CHI 2013.
• Center of Attention: How Facebook Users Allocate Attention across Friends.
  Lars Backstrom, Eytan Bakshy, Jon Kleinberg, Thomas M. Lento, Itamar Rosenn. ICWSM 2011.
• Everyone's an Influencer: Quantifying Influence on Twitter.
  Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts. WSDM 2011.
• Social Influence and the Diffusion of User-Contributed Content
  Eytan Bakshy, Brian Karrer, Lada A. Adamic. EC 2009.

Open source

The Adaptive Experimentation team at Meta maintains two open-source projects

• Ax: An open-source platform for adaptive experimentation, developed and used in production at Meta. It manages the execution and state of experiments—trial generation, tracking, and storage—with support for failure recovery in deployed settings. Ax is designed to make advanced optimization methods accessible to practitioners, while enabling researchers to evaluate new algorithms and bring them into production with minimal boilerplate.
• BoTorch: A PyTorch library for Bayesian optimization and related sequential decision methods, including active learning and bandits. Its modular design supports rapid prototyping of new models and acquisition functions, with built-in support for Monte Carlo methods, auto-differentiation, integration with deep learning models, and multi-objective and preference-based optimization.

I am also the creator of PlanOut. It's an open-source framework for designing and implementing complex behavioral science experiments. I find PlanOut to be helpful for thinking about and planning experiments, and hope that people can use it to run interesting experiments. Visit the PlanOut homepage and give it a try. For more on designing and deploying online field experiments with PlanOut, check out our paper on the subject.