I’m a master’s student pursuing dual degrees in Computational Science and Engineering (CSE) and Electrical Engineering and Computer Science (EECS) at MIT. I am fortunate to be working in Professor Munther Dahleh Research Group at LIDS.
My research interests are at the intersection of statistical machine learning, optimization, and control. My current research focuses on developing and analyzing new data-efficient and robust reinforcement learning methods for online decision-making, motivated by applications in energy systems.
Previously, I was part of the Center for Complex Engineering Systems (CCES) at KACST and MIT, where I tackled research questions related to time series forecasting, anomaly detection, and robust application of reinforcement learning for economic dispatch. Beyond research outcomes, my work at CCES led to multiple successful collaborations with high-ranking public and private stakeholders.
- Sample Efficient Reinforcement Learning with Partial Dynamics KnowledgePreprint - Under Review, 2023
- Identifying Symbolic Communication in Simulated Teacher-Student Environment by Bayesian ModelingPreprint - In Preparation, 2023
- Electricity Non-Technical Loss Detection: Enhanced Cost-Driven Approach Utilizing Synthetic ControlIn IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2021
- Next-day Electricity Demand Forecast: A New Ensemble Recommendation System Using Peak and ValleyIn IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2021
- Automatic Modulation Classification: Investigation for Millimeter Wave Over Fiber ChannelsIEEE Photonics Technology Letters, 2019