Presenter Bio:

Andrés Gómez received his B.S. in Mathematics and B.S. in Computer Science from the Universidad de los Andes (Colombia). He then obtained his M.S. and Ph.D. in Industrial Engineering and Operations Research from the University of California Berkeley. From 2017 to 2019, Dr. Gómez worked as an Assistant Professor in the Department of Industrial Engineering at the University of Pittsburgh, and since 2019 he is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Southern California. Dr. Gómez research focuses on developing new theory and tools for challenging optimization problems arising in finance, machine learning and statistics. His research is funded my multiple grants and gifts from the National Science Foundation, the Air Force Office of Scientific Research, Google and Meta.

Abstract

Common statistical techniques fail if the data used to train the model is corrupted by gross errors or outliers. In fact, even the presence of a single outlier may cause estimators to result in arbitrarily large errors. Several robust estimators have been proposed in the statistical literature, which automatically detect and discard outliers before fitting a model using the remaining data. Unfortunately, the resulting training problem is NP-hard and challenging to solve, even with modern optimization techniques. Thus, practitioners typically resort to heuristics, which have inferior statistical properties and may result in low-quality solutions unless stringent assumptions on the data-generation process are made.