Vasilis Syrgkanis

Timeline

1. 2009–2014

   Degrees in Athens and Cornell

   He earns a diploma in electrical engineering and computer science from the National Technical University of Athens in 2009. In 2014, he completes a PhD in Computer Science at Cornell University, advised by Eva Tardos. His doctoral thesis is Efficiency of Mechanisms in Complex Markets.

2. 2015

   Learning in games and auctions

   In 2015, he coauthors Econometrics for Learning Agents with Denis Nekipelov and Eva Tardos; their work on online auctions receives the ACM Economics and Computation best paper award. Fast Convergence of Regularized Learning in Games, written with Alekh Agarwal, Haipeng Luo and Robert E. Schapire, also wins a best paper award at NIPS in 2015. The paper studies how learning algorithms with a bias toward recent observations reach efficient outcomes and game equilibria faster.

3. 2016

   A Microsoft postdoctoral position

   In 2016, he works as a postdoctoral researcher at Microsoft Research in New York City, in the Algorithmic Economics and Machine Learning groups. His research connects theoretical computer science, machine learning and economics.

4. 2019

   Orthogonal random forests

   With Miruna Oprescu and Zhiwei Steven Wu, he coauthors Orthogonal Random Forest for Causal Inference at ICML in 2019. The method estimates how treatment effects vary across people or cases from observational data, allowing researchers to account for many other variables.

5. 2019

   A COLT best paper

   In 2019, he and Dylan J. Foster coauthor Statistical Learning with a Nuisance Component, which receives the COLT best paper award. The work gives error guarantees for learning tasks in which the target model depends on another model estimated from data.

6. By 2022

   ALICE and EconML at Microsoft

   By 2022, he is a principal researcher at Microsoft Research New England, where he co-leads Automated Learning and Intelligence for Causation and Economics (ALICE). He leads development of EconML, an open-source library for causal machine learning. His Microsoft role ends in August 2022.

7. September 2022

   Joining Stanford MS&E

   In September 2022, he joins Stanford University as an assistant professor in the Department of Management Science and Engineering. His research develops methods for causal analysis of observational and experimental data, with applications in operations management, healthcare and technology companies.

8. 2024

   An NSF CAREER Award

   In 2024, he receives an NSF CAREER Award for Automated and Robust Causal Inference for Data-Driven Decisions. The project aims to automate parts of causal analysis and develop robust procedures for estimating causal effects from data.

9. October 2025

   A Balakrishnan Early Career Award

   In October 2025, he receives the 2025 A.V. Balakrishnan Early Career Award at the University of Southern California. His award lecture, From Predictive Models to Causal AI: Automating Inference for Data-Driven Decisions, covers methods for causal inference and their use in open-source software.

10. As of October 2026

    Leading Stanford Causal AI Lab

    As of October 2026, he is an assistant professor in Stanford’s Department of Management Science and Engineering, with courtesy appointments in Computer Science and Electrical Engineering. He leads the Stanford Causal AI Lab and serves as associate director of the Stanford Causal Science Center. His research covers causal inference, machine learning, econometrics, online and reinforcement learning, and game theory.