Welcome
I am currently pursuing a Ph.D. in Computer Science with a minor in Statistics at Iowa State University, under the guidance of Dr. Ravikumar Gelli and Dr. Christopher Quinn. My research focuses on developing intelligent, secure, and adaptable AI systems for smart energy applications. I explore a broad range of methodologies, including single-agent and multi-agent reinforcement learning (DRL), Large Language Models (LLMs), graphical models, and adversarial robustness, to enhance the control, resilience, and adaptability of cyber-physical infrastructure.
At the core of my work is the application of physics-informed reinforcement learning, where domain-specific constraints are embedded directly into the learning process to ensure safety, interpretability, and compliance with real-world systems. I design and evaluate AI systems capable of operating under uncertainty, detecting adversarial behavior, and making reliable decisions in dynamic environments. My research on adversarial robustness aims to strengthen the security of AI agents against manipulation, supporting safe deployment in high-stakes applications.
To support cross-domain generalization, I develop transfer learning techniques that allow reinforcement learning agents to adapt across diverse topologies, environmental dynamics, and operational conditions. I also lead the design of a Python-based control and simulation framework, integrating real-time co-simulation platforms like OpenDSS, enabling rapid development, testing, and validation of intelligent control algorithms in realistic smart energy settings.
Beyond smart energy systems, I maintain active research interests in probabilistic reasoning, statistical machine learning, and robotics, as well as the integration of LLMs with simulation and control frameworks. I am particularly excited about the potential of LLMs to improve human-AI interaction, enhance contextual awareness, and drive more interpretable and adaptive behavior in intelligent systems.
News
| [Jun 2024] | Our paper on Transfer Learning Enhanced Deep Reinforcement Learning for Volt-Var Control in Smart Grids has been accepted to IEEE PES Grid Edge Technologies Conference & Exposition 2025. |
| [Apr 2024] | Our paper on Workshop for High Performance Computing has been accepted at Pittsburgh Supercomputing Center 2024. |
| [Feb 2024] | Our paper on Bayesian Optimization for Deep Reinforcement Learning in Robust Volt-Var Control has been accepted to IEEE PES General Meeting 2024. |
| [Nov 2023] | Our paper Deep RL-based Volt-VAR Control and Attack Resiliency for DER-Integrated Distribution Grids was accepted to IEEE ISGT 2024. |
| [Aug 2022] | Participated in the Oxford Machine Learning Summer School, completing tracks in MLx Health and MLx Finance. |
| [Apr 2022] | Our paper on Pattern-Based Multivariate Regression using Deep Learning (PBMR-DP) was accepted to ICMLA 2022. |
Affiliations
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| Iowa State University (2020–Present) |
Oxford Machine Learning (August 2022) |
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| Pittsburgh Supercomputing (2024–Present) |
Machine Learning Scientist (2024–Present) |