Foundations of Data Science
Professor Samory Kpotufe graduated in 2010 from Computer Science at the University of California, San Diego and was advised by Sanjoy Dasgupta. He then was a researcher at the Max Planck Institute for Intelligent Systems working in the department of Bernhard Schoelkopf in the learning theory group of Ulrike von Luxburg. Following this, he was an Assistant Research Professor at the Toyota Technological Institute at Chicago. He then spent over 4 years at ORFE, Princeton University as Assistant Professor.
Professor Kpotufe’s work is in machine learning, with an emphasis on nonparametric methods and high-dimensional statistics. Generally, he is interested in understanding the inherent difficulty of high-dimensional problems, under practical constraints from real-world application domains. The nonparametric setting is attractive in that it captures scenarios where we have little domain knowledge, which is important as data sciences reach into a diverse range of applications.
His main practical aim is to design adaptive procedures, i.e., practical procedures that can self-tune to unknown structure in data (e.g., manifold, sparsity, clusters), while at the same time meeting the various constraints (e.g., time, space, labeling cost) of modern applications.