
Boyuan Chen
Associate Director for High Performance Computing (HPC), Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science
Department: MEMS, ECE, CS
Home School: Pratt School of Engineering
Email Address: boyuan.chen@duke.edu
Phone Number: 919-684-3410
Physical Address:
North Building, Rm 135
Dr. Boyuan Chen is the Dickinson Family Assistant Professor at Duke University, where he directs the General Robotics Lab. He is affiliated with the Departments of Mechanical Engineering and Materials Science, Electrical and Computer Engineering, and Computer Science. He also serves as the Strategic Advisor for Robotics and Autonomy in the Dean’s Office at Duke’s Pratt School of Engineering.
Dr. Chen received his Ph.D. in Computer Science from Columbia University in 2022. Dr. Chen received his Ph.D. in Computer Science from Columbia University in 2022. His research focuses on building Discovery Machines — machines that learn, act, and collaborate by discovering how the world works. His lab develops the arc of Embodied Intelligence in which machines sense through multiple modalities, adapt by discovering and designing their own bodies and capabilities, and connect by understanding how humans and other machines think and act. By taking a full-stack approach that spans both the “body” and “brain” of intelligent systems, his group advances robotics, artificial intelligence, human–AI teaming, and AI for scientific discovery. Inspired by natural intelligence in humans and animals, his work explores new frontiers in adaptive, multimodal, and interactive autonomy.
Dr. Chen was named to ASME’s Watch List in 2025. His work has received numerous media reports and has been featured in outlets such as the New York Times, Forbes, Fox, Fortune, Science, and the National Science Foundation. His research has been published in top-tier venues including Science Robotics, Nature Computational Science, NeurIPS, ICRA, and CoRL.
Research Overview:
My research advances embodied intelligence by understanding and designing intelligent systems that learn, act, reason, and adapt through perception and interaction. My research involves multiple areas including Robotics. Artificial Intelligence, Dynamical Systems, and Human-AI Teaming.
Publications:
- Geopolymer Adhesives for DUV LED Packaging: Synthesis and Bonding Mechanism
- MetaCipher: A General and Extensible Reinforcement Learning Framework for Obfuscation-Based Jailbreak Attacks on Black-Box LLMs
- CREW-WILDFIRE: Benchmarking Agentic Multi-Agent Collaborations at Scale
- SymMatika: Structure-Aware Symbolic Discovery
- Physical artificial intelligence in nursing: Robotics
- Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
- Sym2Real: Symbolic Dynamics with Residual Learning for Data-Efficient Adaptive Control
- Scensory: Automated Real-Time Fungal Identification and Spatial Mapping
- How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
- Scientific judgment drifts over time in AI ideation
- TumorMap: A Laser-based Surgical Platform for 3D Tumor Mapping and Fully-Automated Tumor Resection
- Time-Aware Policy Learning for Adaptive and Punctual Robot Control
