Sergio A. Dorado-Rojas
Welcome! I am Sergio (en: /sɛɹdʒioʊ/, es: /ˈseɾxjo/, it: /ˈsɛrd͡ʒo/), a Ph.D. student in electrical engineering at Georgia Institute of Technology. I am an advisee of Dr. Daniel K. Molzahn.
My research addresses critical challenges in modern power systems, driven by integrating new players like intermittent generation and data centers. I tackle core issues such as power system oscillations, frequency regulation, and congestion management.
My unique approach bridges problems traditionally handled by classical dynamic analysis with those addressed by modern optimization techniques. This involves exploring the intrinsic connections between adaptive paradigms and convex relaxations, focusing on approximation methods and model adaptation when dealing with imperfect representations of such complex systems.
Education
M.Sc. in Computer & Systems Engineering (2022)
Rensselaer Polytechnic Institute
Troy, NY
M.Sc. in Electrical Engineering (2020)
Rensselaer Polytechnic Institute
Troy, NY
M.Eng. in Industrial Automation (2018)
Universidad Nacional de Colombia
Bogotá D.C., Colombia
B.Sc. in Electrical Engineering (2017)
Universidad Nacional de Colombia
Bogotá D.C., Colombia
About my (long) academic name
For most purposes, I use Sergio Dorado as my first and last names. In academic publications, I go by Sergio A. Dorado-Rojas, paying respect to my hispanic heritage and forcing the use of my maternal last name.
If you are not familiar with the naming conventions in hispanic countries, this Wikipedia article provides an excellent overview.
News
May 2025: I was awarded the 2025-2026 Chateaubriand STEM Fellowship by the Embassy of France in the United States. I will complete a nine-month research stay at Université Paris-Saclay (CentraleSupélec) under the supervision of Prof. Sorin Olaru 🎉!
May 2025: Our work on adaptive internal models for converter control under extreme frequency conditions has been accepted for publication in IEEE Transactions of Power Delivery. Check the early access here.
October 2024: presentation at the INFORMS Annual Meeting about Approximate Internal Models, an approach to exploit the internal model principle of control theory using online estimators.