I am currently pursuing PhD in Operations Research at the University of Illinois at Urbana Champaign (UIUC). I completed my undergraduate degree in Production and Industrial Engineering from the Indian Institute of Technology (IIT) Delhi in 2018. I have worked at Schlumberger as a field operations engineer for 2 years. If you would like to learn more about me, 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 solving convex optimization problems.

Note

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