Foundations of Data Science
Data, Media and Society
Nakul Verma is a teaching faculty member at Columbia University, focusing on Machine Learning, Algorithms and Theory. His primary area of research is Machine Learning and High-dimensional Statistics, and is especially interested in understanding and exploiting the intrinsic structure in data (eg. manifold or sparse structure) to design effective learning algorithms in the big data regime.
Before joining Columbia, Dr. Verma worked at Janelia Research Campus, HHMI as a Research Specialist developing statistical techniques to quantitatively analyze neuroscience data. He has also worked at Amazon as a Research Scientist developing risk assessment models for real-time fraud detection. Dr. Verma received his PhD in Computer Science from UC San Diego specializing in Machine Learning.