Nakul Verma

Nakul Verma is a senior teaching faculty member at Columbia University in the City of New York, specializing in Machine Learning, Algorithms, and Theory. His research primarily focuses on Machine Learning and High-dimensional Statistics, particularly on understanding and exploiting the intrinsic structures in data to develop effective learning algorithms. This includes work on manifold learning and sparse structures in the context of big data.123

Educational Background and Career

Dr. Verma earned his PhD in Computer Science from the University of California, San Diego (UCSD) in 2012, where he specialized in Machine Learning. He also holds a Bachelor of Science degree from UCSD, awarded in 2004. Prior to his current role, he worked as a Research Specialist at the Janelia Research Campus of the Howard Hughes Medical Institute, where he developed statistical techniques for analyzing neuroscience data. Additionally, he served as a Research Scientist at Amazon, focusing on risk assessment models for real-time fraud detection.1234

Teaching and Research Interests

At Columbia, Dr. Verma teaches courses such as Machine Learning and Methods in Unsupervised Learning. His research contributions include advancements in approximate distance-preserving embeddings for manifolds and improved sample complexity results in various learning paradigms.34 He is affiliated with the Foundations of Data Science and Data, Media and Society centers at Columbia.2

Professional Affiliations and Honors

Nakul Verma is a member of professional organizations such as the Institute of Electrical and Electronics Engineers (IEEE) and the International Machine Learning Society (IMLS). He has received several accolades, including a Janelia Teaching Fellowship in 2015 and an ICML Reviewer Award in the same year.34

For more detailed insights into his work, you can visit his LinkedIn profile or his Columbia University faculty page for a list of publications and additional information.