AI Research scientist at Meta
PhD Operations Research and Industrial Engineering Program
Advised by Prof. Raghu
Bollapragada
Nonlinear Optimization Algorithms, Machine Learning Training Algorithms, Adaptive Algorithms, Stochastic and Constrained Optimization, Distributed Optimization
Hi, I am a Research Scientist at Meta working in software hardware co-design to devolop efficient algorithms for machine learning training. Prior to that, my research has delved into a diverse array of domains, including subjects like stochastic, constrained, and decentralized optimization. My primary emphasis revolves around designing problem-specific algorithms that align with the primary costs of the given system. I am consistently open to engaging in discussions and potential collaborations on various projects.
Raghu Bollapragada, Shagun Gupta
(2025, Under Review) [BibTex]
Albert Berahas, Raghu Bollapragada, Shagun Gupta
Computational Optimization and Applications, 2025 [BibTex]
Albert Berahas, Raghu Bollapragada, Shagun Gupta
Journal of Optimization Theory and Applications, 2024 [BibTex]
Rachel Moglen, Julius Barth, Shagun Gupta,
Eiji Kawai, Katherine Klise, Benjamin D Leibowicz
Reliability Engineering & System Safety,
2023 [BibTex]
Brent Austgen, Shagun Gupta, Erhan
Kutanoglu, John Hasenbein
IEEE Power & Energy Society General Meeting (PESGM), 2022 [BibTex]
International Conference On Continuous Optimization - 2025 (Los Angeles, CA, USA)
Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear ConstraintsInforms Optimization Annual Meeting - 2024 (Seattle, WA, USA)
Retrospective Approximation for Stochastic Constrained Problems Using Sequential Quadratic ProgrammingTexas Career Engagement - 2024 (University of Texas at Austin)
A Guide to Using AI tools for daily workflows (Handout)Midwest Optimization Meeting - 2023 (University of Michigan, Ann Arbor)
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized OptimizationSIAM Optimization - 2023 (Seattle, WA, USA)
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized OptimizationInforms Annual Meeting - 2022 (Indianapolis, Indiana, USA)
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized OptimizationOpen source implementation to recrete numerical experiements for the works, Balancing communication and computation in Gradient Tracking Algorithms for Decentralized Optimization and A Flexible Gradient Tracking Algorithmic Framework for Decentralized Optimization . Also, the repository constains additional numerical experiments to supplement the works.
Open source implementation of various real and synthetic optimization problems in Python 3 to enable testing optimization Algorithms.
