Daniel S. Brown

Daniel S. Brown is an Assistant Professor at the University of Utah’s Robotics Center and School of Computing. His research focuses on robot learning under uncertainty, reward inference from human input, and AI safety. He completed his PhD in Computer Science at UT Austin (2020) under Prof. Scott Niekum and was a postdoc at UC Berkeley working with Anca Dragan and Ken Goldberg.

He leads the Aligned, Robust, and Interactive Autonomy (ARIA) Lab, which explores human-robot collaboration, safe AI systems, and emergent swarm behaviors. His work emphasizes practical methods for ensuring robustness and safety in autonomous systems.

• Education: PhD in CS (UT Austin, 2020)

Research interests include: Bayesian methods for reward learning, interactive imitation learning, and scalable human-swarm interaction. His NIH-funded projects integrate AI with assistive technologies like powered neck exoskeletons.

Key publications focus on Bayesian inverse reinforcement learning, demonstration sufficiency, and robust policy optimization. His NIH Trailblazer Award (2023) supports adaptive control systems for medical exoskeletons.

• Awards: RSS Pioneer (2021), NeurIPS Workshop Best Paper (2020)

Teaching includes courses like 'Human-AI Alignment' and collaborative work on interdisciplinary projects combining robotics with game design and healthcare.