معرفی
Rahul Mazumder is the NTU Associate Professor and Robert G. James Career Development Associate Professor of Operations Research and Statistics at MIT Sloan School of Management, affiliated with LIDS, the MIT Institute for Data, Systems, and Society, and the MIT Center for Statistics. Prior roles include Assistant Professor at Columbia University and Postdoctoral Associate at MIT. His research bridges statistics and optimization, focusing on computational statistics, machine learning, and large-scale algorithms with applications in finance, healthcare, and AI.
Education: BStat and MStat from Indian Statistical Institute (2007), PhD in Statistics from Stanford University (2012). Awards include the 2024 Leo Breiman Junior Award, IISA Early Career Award, and ONR Young Investigator Award.
Research Interests:
- Statistical machine learning and mathematical optimization
- High-dimensional statistics and sparsity
- Applications in recommender systems, computational finance, and computational biology
- Neural network pruning and efficient AI systems
Key Article Trends: Focus on optimization frameworks for LLMs, sparse learning, and scalable algorithms for high-dimensional problems. Recent work addresses privacy-preserving fine-tuning, efficient neural network deployment, and statistical methodologies in genomics.
Awards: Extensive recognition for contributions to optimization, machine learning, and statistics, including student paper awards as advisor. Funded by NSF, ONR, IBM, and Google Research.
Advising & Grants: Supervised over 40 graduate and undergraduate students. Research supported by grants from NSF, ONR, and industry partners. Active in editorial roles for top journals like Annals of Statistics and Operations Research.
Labs/Teams: Mazumder Lab develops software like L0Learn and COMET, emphasizing open-source tools for sparse learning and optimization.
