Machine learning researcher working on reinforcement learning, robotics and physical intelligence as a whole.
Improved MPC objectives for sample-efficient continuous control. The work improved the sample efficiency of state-of-the-art methods by approximately 2× on robotics benchmarks including HumanoidBench and the DeepMind Control Suite.
Submitted to NeurIPS 2026.
2023–2026
Delft University of Technology
Thesis in model-based reinforcement learning, graded 9.5/10.
2023–2024
Enthusiastech
Built control, computer vision, optimization, and embedded systems for robotics and sensing applications.
2021–2022
Da Vinci Satellite Team, TU Delft
Led six electrical engineers in the design and testing of nanosatellite electronics.
2018–2021
The Smart Make
Developed a robotics education platform consisting of hardware and software used by more than 100 students.
2020–2023
Delft University of Technology
Electrical engineering, signal processing, embedded systems, and computing.
Python, C++, CUDA, PyTorch, VHDL, Node.js
Reinforcement learning, model-based RL, optimization, computer vision, transformers
Electrical engineering, embedded systems, FPGA, PCB design, robotics, sensor systems
Git, Docker, Linux, LaTeX, Nsight Systems, Nsight Compute, SURF / Snellius
I am lucky enough to see life as the great adventure that it is. My thinking is that the greatest way to experience this adventure is to work hard toward cool goals with great people that want the best for each other and the world. I currently do this in the form of research in machine learning and physical intelligence. It may be the greatest puzzle humanity will ever solve and I am very happy I get to be a small part of it.