Daniel
De Brun

Building a benchmark for evaluating state-of-the-art multi-robot collaboration methods in NVIDIA Isaac Lab.

Led the re-platforming of an analytics platform serving 60 divisions and 3,500+ production lines onto a Databricks lakehouse: data refreshes every 5 minutes instead of hourly, at under one tenth of the monthly cost.

Owned the production GraphQL API through the migration, and led the micro-frontend platform that split a monolithic portal into six independently released applications.

Sole author of an agentic pipeline that maps supplier spreadsheets into one schema: deterministic lookup resolves 75% of columns before any LLM call, and nothing publishes without expert approval.

Shipped production OCR for stamped and engraved IDs on metal parts and built two MCP servers adopted across the company. Kept on past the initial term and moved to the corporate function with the core team.

Built multi-agent RL agents in JAX that adapt to a partner’s skill in Overcooked (CoGrid, JaxMARL); the project won the People’s Choice Award at CUCAI 2026.

Led experimental integration for the team’s paper submission on learned communication in multi-robot teams: implemented its baselines (MAPPO, IPPO and three learned-communication methods) and found bugs that would have invalidated results, including deliveries credited to the wrong order and a biased best-seed metric.

Founded the university’s autonomous scale-racing team in second year and led it for two years with 30 members each year. Designed the car’s autonomy software and mentored 15+ developers.

A JAX library for multi-agent reinforcement learning that runs environments and training end to end on the GPU. Used to study when agents that must split the work, like cooks in an Overcooked kitchen, learn to coordinate and communicate.

Autonomy software for the team’s scale race car on an NVIDIA Jetson. Camera, SLAM, TensorRT segmentation, control and actuation run as separate processes over lock-free shared memory; pure pursuit, LQR, MPC and learned controllers sit behind a safety supervisor that cuts throttle on any command older than 150 ms.

GPU-accelerated ORB-SLAM3 by moving its per-frame tracking work to CUDA: feature extraction, stereo matching and the image pyramid. Tracking runs about 40% faster across stereo, monocular and RGB-D cameras.