NeMo.bil
Responsible for technical coordination and system development of the autonomous driving system. Second university to get L4 testing approval on public streets.
I work at the intersection of machine learning based planning and hardware acceleration for autonomous vehicles — with a particular interest in how heterogeneous SoCs can make real-time autonomy both efficient and reliable.
Working toward a doctorate on planning-oriented autonomous driving beyond observed futures.
Received the Best Poster Award at the ELLIS Summer School on Autonomous Driving.
Successfully completed the NeMo.bil project.
Joined UTN (University of Technology Nuremberg) as a PhD student in the Artificial Intelligence and Robotics Lab.
Poster presentation of the THAccelerated Motion Planner at the RoboARCH workshop, IEEE ICRA 2025. ↗ abstract
Responsible for technical coordination and system development of the autonomous driving system. Second university to get L4 testing approval on public streets.
FPGA-accelerated motion planner for autonomous vehicles, validated in hardware-in-the-loop and real-world testing.
Object detection comparison between Jetson TX2 and UltraScale MPSoC — notable power efficiency advantage on FPGA.
Traction inverter for Formula Student EV — EnDat encoder interface, CAN-AXI bridge, custom embedded Linux stack.