PhD Student at Univesity of Texas at Austin
Operations Research and Industrial Engineering Program
Advised by Prof. Raghu
Bollapragada
Nonlinear Optimisation Algorithms, Adaptive Algorithms, Stochastic and Constrained Optimization
Hi, I am a 4th year PhD Student at the University of Texas at Austin advised by Prof Raghu Bollapragada focusing on designing Nonlinear Optimization Algorithms. We delve 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.
Awarded UT Austin Continuing Student Fellowship (2024 - 25)
Awarded Professional Development Award (Spring 2024)
Awarded Reimagining Professional Development Award (Spring 2024)
Albert Berahas, Raghu Bollapragada, Shagun
Gupta
(2023, Under Review) [BibTex]
Albert Berahas, Raghu Bollapragada, Shagun
Gupta
(2023, Under Review) [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
2022 IEEE Power & Energy Society General Meeting (PESGM) [BibTex]
Texas 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.
