Summary
Ruiyang Wang is a Ph.D. student in Electrical and Computer Engineering at Duke University with 12 years of experience applying AI and control to multi-robot systems, motion planning, and learning-based control. He develops practical, scalable solutions—such as LLM-MCoX, which fuses LiDAR frontier clustering, doorway detection, and multimodal LLM reasoning to semantically guide team exploration and cut search time by over 22% while improving efficiency by 50% in real-world tests with quadruped and drone platforms. His work blends neural-symbolic reasoning (NSDR) and control-theoretic heuristics to resolve deadlocks and ensure feasibility at scale, often generalizing from small-training scenarios to larger heterogeneous teams. Ruiyang pairs strong theoretical contributions with hands-on systems builds (from autonomous maze robots to VAE/SINDy latent controllers), and his background shows a pattern of improving robustness and constraint satisfaction in safety-critical planning and control. He is based in Durham, NC and brings an interdisciplinary approach that leverages natural language cues to make robotic autonomy more semantic and adaptable.
12 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Robotics, 3.79/4.00, Master of Science - MS, Robotics, 3.79/4.00 at University of Michigan College of Engineering
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at Duke University Pratt School of Engineering
GPA 3.93/4.0, GPA 3.93/4.0 at Delone Catholic High School