Last updated Oct. 2024
The Digitalized Electrical grid Innovations, Developments & Applications Laboratory (DEgIDAL) comprises high-school, undergraduate and graduate (MSc & PhD) researchers who are studying both at the City College of New York (CCNY) and other Institutions at the US and beyond.
CCNY Students
Ioannis (Giannis) Vourkas [MSc Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece, 2024] has worked on the optimal operation and structure of energy markets. He is a graduate researcher (PhD) in Electrical Engineering at the CCNY since Fall ’24, aiming to expand his work on energy markets, renewable energy and power system security with AI, machine learning and optimization methods.
Mobin Hajjafari [BE Electrical Engineering, City College of New York, 2025] has worked at the Ultrafast Spectroscopy lab and has also been awarded the 2024 TS3 summer research scholarship. He already follows the latest research at local conferences and symposia and is passionate about following a similar path himself. At DEgIDAL he has worked on electricity bill savings via peak shaving with battery storage systems at the residential and community levels.
Daniel Naunay [BE Electrical Engineering, City College of New York, 2025] has extensive electrical engineering programming and simulation skills. His recent projects include board game digitalization, AI-optimized data management, and audio engineering. He is an IEEE member and Dean’s List honoree, while also boasting experience in aircraft maintenance and operational support. His research at DEgIDAL has focused on testing varying approximations of electrical grid physics in power system optimization.
BaoDuy (Bao) Huynh [BE Electrical Engineering, City College of New York, 2025] is a first-generation college student with a research focus on Power Engineering and Software. He has already accumulated utility experience through his summer internships and aims to pursue career opportunities that will allow him to introduce more and significantly advanced digital tools across the power sector. His research at DEgIDAL has been on testing how to procure electricity bill savings via peak shaving with battery storage systems at the residential and community levels.
External Collaborators
Rahul Muppavarapu [BE, Statistics & Machine Learning, Carnegie Mellon University, Pittsburgh, USA 2028] is passionate about machine learning solutions in real-world problems. He was student developer at MIT Lincoln Laboratory as a part of the MIT BWSI program in diagnosing early stages of neurodegenerative diseases. He has been building software predicting winners of professional tennis matches and for poker AI competitions. His research at DEgIDAL has focused on diagnosing single-point attacks to wholesale electricity market bids with machine learning.
Alberto Darjazi Dolaby [ME Electrical Engineering, University of Pavia, Italy, 2024] has studied plasma engineering, and the analysis of power systems, electric machine and drives. In 2024 he was admitted for a quarter of nuclear reactor studies at the Nuclear Radiation lab at the University of Illinois Urbana-Champaign. During the same year, he also designed power converters for the Divertor Tokamak Test facility (nuclear fusion) as part of his thesis with ENEA. At DEgIDAL he has integrated varying approximations of electrical grid physics in power system optimization.
Muhammad Saad Iqbal [ME, Electrical Power Engineering, NU-FAST, Pakistan, 2018] has over 10 years of diversified Electrical Engineering experience in the power generation and distribution sectors across Asia and the UK. He is currently pursuing a PhD in Renewable Energy Systems at the University of Huddersfield, UK, focused on optimizing energy storage systems for grid support services. He also serves as a part-time Energy Analyst at U Energy. Saad has been awarded the Engineering Excellence and the Turing Scheme scholarship from the UK Government. At DEgIDAL he has worked on electricity bill savings via peak shaving with battery storage systems at the residential and community levels.