Professor Nakul Verma is the Program Director of the Master’s program in Data Science, and a senior teaching faculty member in the Computer Science Department at Columbia Engineering. His primary area of research is machine learning and high-dimensional statistics, particularly focusing on understanding and exploiting the intrinsic structures in data to develop effective learning algorithms in the big data regime. His work has produced the first provably correct approximate distance-preserving embeddings for manifolds from finite samples, and has provided improved sample complexity results in various learning paradigms such as metric learning and multiple-instance learning.

Professor Verma is equally passionate about teaching machine learning to the next generation of industry leaders, and finding ways to improve pedagogy. He has won multiple exploratory grants to design pilot teaching systems that thoughtfully incorporate emerging technologies to improve student learning.

Previously, Professor Verma has worked at Janelia Research Campus of the Howard Hughes Medical Institute 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.

Professor Verma received his PhD in Computer Science from UC San Diego, specializing in Machine Learning.