Curriculum Vitae

The detailed PDF verison of my CV can be found here - Curriculum Vitae

Research Interests

I am interested in machine learning and optimization broadly. Particularly in characterizing fundamental quantities, phenomena, and general laws that arise due to scale.

• Research Interests - Machine Learning, Large Scale Optimization, Probability theory
• Other Learning Interests - Geometry and Analysis, Modern Finance

Education

• Ph.D. candidate at Paul G. Allen School of Computer Science & Engineering, 2019 - Present
  University of Washington
  Advisor: Prof. Sewoong Oh
  Ph.D. Thesis: Scaling Limits of Algorithms on Large Matrices
• M.S. in Computer Science and Engineering, 2022
  Paul G. Allen School of Computer Science and Engineering, University of Washington
  Supervisor: Prof. Sewoong Oh
• B.Tech. in Mathematics and Computing, 2013 - 17
  Indian Institute of Technology Guwahati

Work experience

• September 2024 - Present: Quantitative Analyst
  • The D. E. Shaw Group
• June 2023 - September 2023: Quantitative Analyst Ph.D. Intern
  • The D. E. Shaw Group
  • Group: Systematic Equities
• September 2019 - August 2024: Graduate Research Assistant
  • Advisor: Prof. Sewoong Oh
  • Machine Learning Lab
• July 2017 - July 2019: Research Fellow
  • Microsoft Research Lab - India
  • Mentors: Dr. Praneeth Netrapalli and Dr. Prateek Jain
  • Project group: Provable Non-convex Optimization for Machine Learning Problems
• Summer 2016: Research Intern
  • Microsoft Research Lab - India
  • Supervisor: Dr. Sreangsu Acharyya
  • Project: Clustered Monotone Transforms for Rating Factorization
• Summer 2015: Summer Research Intern
  • CAFRAL - RBI
  • Supervisor: Dr. N. R. Prabhala
  • Project: Economic Policy Uncertainty Index - Intern report

Selected Publications

Complete list at Google Scholar, or dblp.

Gradient Flows on Graphons: Existence, Convergence, Continuity Equations - Journal of Theoretical Probability and the Optimal Transport and Machine Learning (OTML) workshop, Neural Information Processing Systems (NeurIPS), 2021 [paper] [arXiv] [bib]

Robust Meta-learning for Mixed Linear Regression with Small Batches - Neural Information Processing Systems (NeurIPS), 2020 [paper] [arXiv] [bib] [code] [video]

Meta-learning for Mixed Linear Regression - International Conference on Machine Learning (ICML), 2020 [paper] [arXiv] [bib] [code] [video]

Non-Gaussianity of Stochastic Gradient Noise - Science meets Engineering of Deep Learning (SEDL) workshop, Neural Information Processing Systems (NeurIPS), 2019 [arXiv] [bib]

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds - Neural Information Processing Systems (NeurIPS), 2018 [paper] [bib] [video]

Clustered Monotone Transforms for Rating Factorization - Web Search and Data Mining (WSDM), 2019 [paper] [arXiv] [bib] [code] [video]

Research Projects

Scaling Limits of Algorithms on Large Matrices - University of Washington

Scaling laws of optimization algorithms for Deep Learning - the Graphon perspective - University of Washington

Robust Mixed Linear Regression using heterogeneous batches - University of Washington

Connections between Stochasticity of SGD and Generalizability - Microsoft Research Lab - India

Universality Patterns in the Training of Neural Networks - Microsoft Research Lab - India

Sparse Regression and Support Recovery bounds for Orthogonal Matching Pursuit - Microsoft Research Lab - India

Some Approaches of Building Recommendation Systems - Department of Mathematics, IIT Guwahati

Professional services

• Reviewed for NeurIPS ‘19, NeurIPS ‘20, ICML ‘20, JMLR.

Teaching

• Teaching assistant for CSE 446/546 Spring 2021.
• Teaching assistant for CSE 446 Spring 2024.