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 Rahul Mazumder
   NTU Associate Professor 
Operations Research and Statistics group
 MIT Sloan School of Management
 Operations Research Center
 LIDS
 MIT Center for Statistics and Data Science
 Massachusetts Institute of Technology
 
 I am the NTU Associate Professor of Operations Research and Statistics, in the Operations Research and Statistics group at MIT Sloan School of Management. I am affiliated with the Operations Research Center (ORC), Laboratory for Information & Decision Systems (LIDS), MIT Center for Statistics. Outside MIT, I am also an Academic Scholar 
at  LinkedIn AI  (Algorithms and Foundations Group).
 Before joining MIT, I spent a couple of wonderful years as an Assistant Professor at Columbia University (Dept of Statistics), where I
was also affiliated with the Data Science Institute. Prior to Columbia, I was a PostDoctoral Associate at MIT. 
I completed my PhD in Statistics from Stanford University under the supervision of 
Trevor Hastie . I completed my BStat, MStat from Indian Statistical Institute, Kolkata.
 
 Service
 I am currently serving/have recently served as an Associate Editor/Action Editor for the 
	Annals of Statistics, Bernoulli, Operations Research (Optimization; and Machine Learning and Data Science), and Journal of Machine Learning Research. 
 I am also serving as the series editor (Algorithms) of Cambridge University Press (Institute of Mathematical Statistics, TextBooks and Monographs) 
 I was the founding cluster chair of Machine Learning within INFORMS Optimization Society  Research
I am a statistician working at the interface of statistics and operations research (mathematical optimization). I study problems in 
computational/algorithmic statistics using tools from mathematical optimization (convex and discrete optimization). I am interested in the applications of statistics/OR tools in problems arising in industry (e.g, computational finance, insurance, recommender systems etc), 
government (US Census Bureau) and the sciences (biostatistics, biomedical sciences). In the past I have worked on applications of statistics in 
the social sciences, oceanography, and turbulence.  
Please take a look at the publications tab .
 Principal Fields of Interest
 Statistics, Machine Learning, Mathematical Optimization (Convex optimization, Mixed Integer optimization), Large Scale Optimization Algorithms.   High dimensional statistics and sparsity, combinatorial statistical modeling & computation, 
nonparametric function estimation (e.g., shape constrained inference).   Applications of the above in recommender systems, computational finance, computational biology & healthcare, survey research, insurance pricing, etc. Neural Network Compression/Pruning, conditional computing in neural networks.   Recent News
 Papers accepted to NeurIPS 2025. Two papers on LLM compression under structural constraints, led by students from my group: Mehdi Makni and Xiang Meng. 
	Third paper on differential privacy for best-subset selection using integer programming (with Petros Prastakos and Kayhan Behdin). Congratulations to Mehdi, Xiang, Petros and Kayhan.   Excited to be a part of a collaboration with LinkedIn AI on algorithmic approaches for large model compression --- paper accepted to EMNLP '25 (Oral, Industry Track). The paper documents an industry application/deployment that uses a structured pruning algorithm designed by my group (Meng, Behdin,..,Mazumder, ICML '24)   Some algorithms on foundation model compression developed my members of my research group have been implemented and open-sourced by LinkedIn AI at
 Github. Specifically, this repo features algorithms: ALPS (Meng et al NeurIPS '24 on unstructured, semi-structured pruning of LLMs), OSSCAR (Meng et al ICML '24; structured pruning) and Quantease (Behdin et al; quantization). Grateful for the collaboration! Two papers appeared in KDD '25 (main conference track): (i) Differential privacy for LLM finetuning (Makni et al and collaborators from 
	Google), (ii) Stable rule extraction for interpretable machine learning led by Brian Liu. 
	Additionally, two workshop papers: (iii) Error detection in Image-Caption pairs (Afriat et al and collaborators from IBM), (iv) Improving LLMs for tabular data using rules (led by Brian Liu)
 Paper on Sparse PCA (joint with Kayhan Behdin) using integer programming under a statistical model was accepted to the Annals of Statistics.   Contact Information     MIT Sloan School of Management 
Building E62-583
 100 Main Street
 Cambridge, MA 02142
 Ph: 617-253-2652
 Email: rahulmaz "at" mit "dot" edu
 
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