Samory Kpotufe

Samory Kpotufe is an Associate Professor of Statistics at Columbia University's Faculty of Arts and Sciences, affiliated with the Data Science Institute (DSI) as a Foundations of Data Science Co-Chair. He holds additional affiliations in Cybersecurity, Health Analytics, and Smart Cities. His academic journey includes a PhD in Computer Science from UC San Diego (2010), followed by research roles at the Max Planck Institute, Toyota Technological Institute at Chicago, and Princeton University's ORFE department.

His research focuses on nonparametric methods and high-dimensional statistics, emphasizing adaptive procedures that self-tune to unknown data structures (e.g., manifolds, sparsity) while addressing modern application constraints like computational efficiency and labeling costs. Key themes include transfer learning, active learning, and online algorithms.

Notable contributions span theoretical guarantees for nearest-neighbor methods, covariate shift adaptation, and contextual bandits. His work often bridges statistical theory and practical machine learning challenges, with applications in IoT, cybersecurity, and anomaly detection. He has led collaborative grants, such as the NSF CPS project on data augmentation for IoT systems.

As a DSI member and Foundations Co-Chair, he contributes to advancing data science foundations through interdisciplinary collaboration. His lab's research frequently explores the interplay between algorithmic performance and intrinsic data properties.