PhD researcher · Power electronics

Engineering intelligence for power electronics.

I explore how artificial intelligence can make power electronic systems more efficient, reliable, and autonomous.

SYSTEM / 01
OPTIMIZE → VALIDATE → DEPLOY
f(x) minimize
η98.4%efficiency
Δ−24%design loss

Research at the intersection of

Power conversion×Computational intelligence×Engineering design

01 / Research

Three connected
lines of inquiry.

From physical systems to algorithms, each area informs the others.

01

Power Electronics

High-frequency converters, modulation strategies, and compact, efficient power architectures.

  • Converter design
  • Modulation
  • Digital twins
02

Artificial Intelligence

Physics-aware learning, predictive control, and data-driven models grounded in engineering reality.

  • PINNs
  • Reinforcement learning
  • Predictive control
03

Optimization

Single- and multi-objective methods for navigating complex engineering trade-offs.

  • PSO & GA
  • NSGA-II
  • Design automation

02 / Selected projects

Ideas made testable.

View GitHub
Research codeActive
PINNSIMO converter

Physics-informed Modulation

Learning converter behavior while preserving physical structure and system constraints.

View repository
Learning series12 notes
AIControlPython

Reinforcement Learning Notes

Practical explanations from tabular Q-learning through PPO and continuous-control methods.

Browse the series

03 / Latest notes

Working in public.

Technical writing on algorithms, engineering systems, and the ideas connecting them.

View all notes

04 / About

Curiosity, translated into engineering.

I obtained my PhD from KU Leuven and EnergyVille in Belgium in 2025. My work brings together power electronics, computational intelligence, and design optimization—with an emphasis on methods that remain useful outside the lab. I now work in the power electronics industry as a postdoctoral researcher, solving design challenges ranging from high-voltage systems to high-power-density power converters.