Vasilis Syrgkanis
Assistant Professor at Stanford University
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVasilis Syrgkanis is an Assistant Professor at Stanford University with a decade of experience at the intersection of machine learning, econometrics, and algorithmic game theory. His work blends rigorous academic research with practical engineering, including significant contributions to Microsoft Research’s EconML toolkit where he improved Double Machine Learning and Orthogonal Random Forest implementations and added performance and tuning features. He has hands-on experience implementing and testing ML algorithms for game-theoretic simulations and computer vision, reflecting a comfort moving between theory and applied code. Trained with a PhD in Computer Science from Cornell and engineering roots from the National Technical University of Athens, he combines deep theoretical grounding with production-minded optimizations. Based in Palo Alto, he leverages both academic and open-source channels to push causal inference tools toward scalable, real-world use. An understated throughline in his profile is a knack for improving estimator robustness and speed—work that often unlocks wider practical adoption.
10 years of coding experience
5 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Cornell UniversityNational Technical University of Athens
Github Skills (10)
scikit-learn10
machine-learning10
econometrics10
python10
game-theory10
causal-inference10
numpy10
scikit10
computer-vision9
pandas9
Programming languages (5)
Github contributions (5)
py-why/EconML
Sep 2020 - Aug 2021
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Role in this project:
ML Engineer
ML EngineerContributions:416 reviews, 28 commits, 72 PRs in 10 months
Contributions summary:Vasilis primarily contributed to the development and improvement of the Double Machine Learning (DML) and Orthogonal Random Forest (ORF) estimators within the econml library. Their work included adding features such as feature importances and doubly robust ATE calculation. The user also focused on performance improvements, specifically speeding up the ORF implementation. They also added hyperparameter tuning to the causal forest models.
decision-makingfairness-mlpythonoutcomeproblems
msr-ds3/coursework
Jul 2015 - Jul 2015
summer school coursework
Role in this project:Data Scientist
Data ScientistContributions:12 commits, 11 pushes in 1 day
Contributions summary:Vasilis primarily worked on implementing and testing machine learning algorithms within the context of a coursework repository. Their contributions centered on the application of fictitious play dynamics to different game theory scenarios such as matching pennies, rock-paper-scissors, and prisoner's dilemma, coded in Python. The user also made modifications to existing computer vision code for image classification.
spring-bootjavasummer-schoolsummer
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.