Successfully defended my Ph.D. dissertation titled 'AI-Driven Anomaly Detection in Modern Power Systems' at the University of Michigan; presented a paper titled 'Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations' at the 2025 IEEE Power and Energy Society General Meeting (PESGM); published a paper titled 'An Advanced Generative AI-Based Anomaly Detection in IEC61850-Based Communication Messages in Smart Grids' in IEEE Access; received 2025 IEEE PES General Meeting Student Housing Support (4 nights) and a poster presentation opportunity; awarded the Rackham Professional Development Diversity, Equity, and Inclusion (DEI) Certificate; received the CECS Doctoral Student Conference Travel Grant to attend the 2025 IEEE Power and Energy Society (PES) General Meeting (GM).
Research Experience
Ph.D. research focused on AI-based anomaly detection in IEC61850-based communications messages and energy management systems (EMSs).
Education
Received Ph.D. in Electrical, Electronics, and Computer Engineering (EECE) from the University of Michigan's College of Engineering and Computer Science under the supervision of Prof. Junho Hong.
Background
As a power system researcher with over a decade of experience, I combine traditional engineering principles with cutting-edge AI algorithms to tackle complex challenges in smart grid technology and power system security. My research interests include security of smart grids, AI in energy, energy management systems, anomaly detection, DER integration, load forecasting, security of autonomous vehicles, energy efficiency, transportation electrification, and power quality.
Miscellany
Serves as a reviewer for several prestigious journals and conferences in the fields of smart grids, transportation electrification, and autonomous driving.