Jayanth Bhargav
Hello and welcome!
I am a PhD candidate at the Elmore Family School of Electrical and Computer Engineering, Purdue University working with Prof. Shreyas Sundaram and Prof. Mahsa Ghasemi. I am a part of the Institute for Control, Optimization and Networks (ICON) at Purdue. My research focuses on developing efficient algorithms backed by theoretical guarantees for decision-making under uncertainity in large-scale systems. I derive unique connections between optimization, game theory, machine learning, and information theory to design efficient solutions for complex decision-making problems.
I graduated with a Master of Science from the Department of Electrical and Systems Engineering, University of Pennsylvania in May 2021. At Penn, I was a graduate research assistant at the xLab for Safe Autonomous Systems, where I was advised by Prof. Rahul Mangharam. I developed learning-based algorithms tailored for decision-making in high-speed scenarios, specifically aimed at achieving successful overtaking maneuvers in autonomous racing. This work was based on the RoboRacer platform.
Prior to this, I received my bachelor's degree in Electrical and Electronics Engineering (Gold Medal) from RV College of Engineering, Bengaluru, India.
Research Interests
- Sequential Decision-Making Under Uncertainty: Exploring algorithms and models for making optimal decisions in dynamic and uncertain environments.
- Multi-Agent Interactions and Reinforcement Learning: Developing learning-based approaches for collaborative and competitive multi-agent systems.
- Resource-Constrained and Task-Oriented Decision-Making: Designing efficient decision frameworks for systems with limited resources and task-specific constraints.
- Robust Strategies for Adversarial Environments: Studying robust control and learning techniques for agents operating in adversarial or strategic settings.
- Optimization for Online Learning: Developing scalable optimization techniques to improve online learning and adaptation in non-stationary environments.
- Game-Theoretic Approaches to Resource Allocation: Applying game theory to optimize resource allocation and coordination in large-scale distributed systems.
Experience
| Jun '22 - Aug '22 |
PhD Intern - Machine Learning Pacific Northwest National Laboratory, Richland WA USA |
Education
| 2021 - Present |
Doctor of Philosophy (PhD) - Electrical & Computer Engineering Purdue University, West Lafayette IN USA |
| 2019 - 2021 |
Master of Science in Engineering (MSE) - Electrical Engineering University of Pennsylvania, Philadelphia PA USA |
| 2015 - 2019 |
Bachelor of Engineering (BE) - Electrical & Electronics Engineering RV College of Engineering, Bengaluru KA India |
Awards
| Feb 2024 | Best Poster Award, ICON Student Research Conference (SRC) 2024 |
| Apr 2022 | Outstanding Graduate Mentor - Elmore Family School of Electrical and Computer Engineering, Purdue University |
| May 2021 | School of Engineering and Applied Science - Outstanding Award for Service, University of Pennsylvania |
| Aug 2019 | Gold Medal in Electrical and Electronics Engineering, RV College of Engineering |
| May 2019 | Sorroco Award for Best Outgoing Student of Graduating Batch of 2019, RV College of Engineering |
| Aug 2015 | Hindustan Aeronautics Limited (HAL) Scholarship for Merit and Excellence |
Contact
| jbhargav@purdue.edu | |
| Office | 501 Northwestern Ave., MSEE 280, West Lafayette IN 47907, USA |