Academics
My academic background spans computer science, machine learning, and applied mathematics — areas that inform my current work in quantitative finance.
Education
University of Wisconsin-Madison — MS, Computer Science (2023)
- Focus: Robustness in regression and classification, high-dimensional probability
- Completed PhD coursework and research rotation before transitioning to industry
Indian Institute of Technology Kanpur — M.Tech, Computer Science (2018)
- Thesis: Robust algorithms for regression (manuscript)
- Published at AISTATS 2019
- Advisors: Prof. Purushottam Kar and Dr. Prateek Jain
- Ranked 1st in program, 10.0 GPA
Indian Institute of Technology Kanpur — B.Tech, Aerospace Engineering (2015)
Relevant Coursework
Courses with direct applications to quantitative finance and algorithmic decision-making:
- Optimization Techniques — convex optimization, duality, gradient methods
- Online Learning — sequential decision-making, regret minimization
- Bayesian Machine Learning — probabilistic inference, uncertainty quantification
- Randomized Algorithms — probabilistic analysis, streaming algorithms
- Design and Analysis of Algorithms — complexity, data structures
Teaching
Machine Learning: Practice and Theory — ACA Summer School, IIT Kanpur (2017)
Taught a course covering both theoretical foundations and practical applications of machine learning at the ACA Summer School. Course materials available here.