I am a final-year PhD candidate in the Intelligent Electrical Power Grids (IEPG) group at Delft University of Technology, and a member of the Delft AI Energy Lab, supervised by Dr. Jochen Cremer.

Research

My research develops machine learning (ML) methods for the operation of electrical power grids, with a focus on generalization of ML models. Power grids are naturally graphs — buses, lines, and transformers — and graph neural networks (GNNs) can exploit that structure to learn models that generalize across operating conditions and network topologies. I apply these ideas to computational problems in power system operation:

• AC optimal power flow — GNNs that generalize across grid topologies and operating points (Energy and AI, 2026; ISGT Europe 2025)
• Time-domain simulation and Dynamic security assessment — machine learning surrogates that accelerate the simulations grid operators rely on (SEGAN, 2025; preprint)
• Stochastic scheduling and optimization — learning-accelerated optimization method for stochastic power system scheduling (IEEE Transactions on Sustainable Energy, 2025)

Before my PhD, I received an MS in Electrical and Computer Engineering from Carnegie Mellon University (2022) and a B.Sc. in Electrical and Electronic Engineering from University of Ibadan (2020). I completed a research internship at the Max Planck Institute for Biological Cybernetics in Tübingen, where I worked on data-driven tracking algorithms for a novel microscope in the RoLi Lab.

I am on the job market

I will complete my PhD soon and am open to research scientist, machine learning engineer, and postdoctoral positions in both industry and academia — anywhere machine learning meets energy systems, simulation, or large-scale optimization. My day-to-day toolkit is Python, PyTorch, and PyTorch Geometric, alongside power-system simulation tools.

Please reach out via email or LinkedIn, and see my CV and publications.