Professor Nakul Verma is a faculty member in the Computer Science Department 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 (e.g. manifold or sparse structure) to design 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.

Before joining Columbia, Dr. Verma worked at Janelia Research Campus of the Howard Hughes Medical Institute (HHMI) as a research specialist developing statistical techniques to analyze neuroscience data, where he collaborated with neuroscientists to quantitatively analyze social behavior in model organisms using various unsupervised and weakly-supervised machine learning techniques. Verma has also worked at Amazon Inc. as a research scientist developing risk assessment models for real-time fraud detection.

Dr. Verma received his PhD in 2012 and his BS in 2004, both from the University of California San Diego.

He was awarded Provost Honors from University of California San Diego from 2001-2004, and was awarded Janelia Teaching Fellowship in 2015.