Maxim Fedotov

Maxim Fedotov is an instructor for “Introductory and Intermediate Computing for Data Science,” a brush-up course for incoming students in the Data Science Methodology Program at the Barcelona School of Economics. He is a PhD student in the Statistics, Probability, and Machine Learning Group at Universitat Pompeu Fabra, advised by David Rossell and Gábor Lugosi. Before beginning his PhD, he completed the Master of Research in Economics, Finance and Management at UPF, after earning a Master’s in Data Science Methodology from the Barcelona School of Economics in 2022.

His research lies at the intersection of statistical methodology, theory, and computation, with a particular focus on high-dimensional inference, variable selection, sparse estimation, overparameterized models, and optimization in statistical learning.

From May 2023 to February 2024, he also worked as a research assistant on the project “Econometrics for Macroeconomic Policy Evaluation,” supervised by Geert Mesters. In this role, he developed Python implementations of data structures, estimation and forecasting routines, and data-visualization tools for policy evaluation.