Panos Toulis
University of Chicago Booth School of Business
Panagiotis (Panos) Toulis studies causal inference in complex settings (e.g., networks) using methods of structured inference, such as permutation tests. He is also interested in the interface of statistics and optimization, particularly in inference problems on large data sets through stochastic gradient descent.
Publications
Robust Inference for High-Dimensional Linear Models via Residual Randomization
Proceedings of the 38th International Conference on Machine Learning (ICML) 2021