Rahul Mazumder

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Rahul Mazumder

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Rahul Mazumder is the Nanyang Technological University Associate Professor of Operations Research and Statistics and an Associate Professor of Operations Research and Statistics at the MIT Sloan School of Management.

Prior to joining MIT, he was an Assistant Professor in the Department of Statistics, Columbia University from Fall 2013 through June 2015, and was also affiliated with the Data Science Institute, Columbia University.  Prior to that, Rahul was a PostDoctoral Associate at MIT from 2012 - 2013.

His research interests are in data science, statistical machine learning, large scale optimization, mathematical programming; and in particular, their interplay. He is also interested in "big data" applications in environmental and climate studies, social science, and recommender systems. He has published in a variety of journals:  Journal of Machine Learning Research, Annals of Statistics, Journal of the American Statistical Association,and Annals of Applied Statistics, among others.

Rahul completed his BS and MS in statistics from the Indian Statistical Institute, Kolkata in 2007. He received his PhD in statistics from Stanford University in 2012.

Honors

Mazumder wins 2024 Leo Breiman Junior Award

March 8, 2024

Mazumder wins early career award

February 23, 2024

Mazumder’s research honored by Office of Naval Research

Mazumder wins INFORMS prize

INFORMS honors Mazumder

Publications

"Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach."

Wang, Haoyue, Shibal Ibrahim, and Rahul Mazumder. SIAM Journal on Mathematics of Data Science. Forthcoming. arXiv Preprint.

"Predicting Census Survey Response Rates with Parsimonious Additive Models and Structured Interactions."

Ibrahim, Shibal, Peter Radchenko, Emanuel Ben-David, and Rahul Mazumder. Annals of Applied Statistics. Forthcoming. arXiv Preprint.

"A New Computational Framework for Log-Concave Density Estimation."

Chen, Wenyu, Rahul Mazumder, and Richard J. Samworth. Mathematical Programming Computation Vol. 16, (2024): 185-228. arXiv Preprint.

"ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models."

Xiang Meng, Kayhan Behdin, Haoyue Wang, and Rahul Mazumder. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 24), 2024. arXiv Preprint.

"End-to-end Feature Selection Approach for Learning Skinny Trees."

Shibal Ibrahim, Kayhan Behdin, and Rahul Mazumder. In International Conference on Artificial Intelligence and Statistics (AISTATS ’24), 2024. arXiv Preprint.

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