I completed my Ph.D. in Operations Research at the University of Illinois Urbana-Champaign (UIUC) in August 2025 and am a Senior Operations Research Scientist at Wayfair, where I work on optimization for transportation and delivery operations. I previously worked as a field operations engineer at Schlumberger for two years. I earned my undergraduate degree in Production and Industrial Engineering from the Indian Institute of Technology (IIT) Delhi in 2018. For more details, please see my resume.

My research interests lie at the intersection of high-performance computing and large-scale optimization. I mainly focus on utilizing Graphics Processing Units (GPUs) to accelerate discrete optimization and graph algorithms to develop scalable solutions for large instances. I have developed a unique combination of CUDA skills with focus on parallel optimization and graph search techniques. So far, I have worked on the following problems:

1. GPU Accelerated Subgraph Enumeration

BEEP: Balanced Efficient Enumeration in Parallel
Sources: paper, slides

2. GPU Accelerated solver for the Linear Assignment Problem

HyLAC: Hybrid Linear Assignment solver in CUDA
Sources: paper, slides

3. GPU Accelerated Multi-Target Tracking

Multi-Target Tracking with GPU-Accelerated Data Association Engine
Sources: paper, slides

4. GPU Accelerated Branch-and-Bound through Best First Search (In review)

A single kernel BnB framework in CUDA for solving combinatorial optimization problems.
Sources: slides, published-code

5. GPU Accelerated Crossovers Inspired by the Spiral Dynamics of PDHG (Ongoing)

A crossover algorithm inspired by the spiral dynamics of primal-dual hybrid gradient (PDHG) method for finding basic feasible solutions of LP from an interior solution. slides

Note

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