Summary
Yulun Zhang is a fourth-year PhD student at Carnegie Mellon’s Robotics Institute specializing in environment optimization for Multi-Agent Path Finding using quality-diversity optimization, evolutionary computation, and generative modeling. With eight years of research and internship experience across academic labs and industry—Including Amazon Fulfillment Technologies, DiDi, and Symbotic—he bridges theoretical algorithm design with applied robotics and warehouse-scale systems. His work in the ARCS Lab focuses on sculpting environments that stress-test and improve MAPF algorithms, a niche that combines simulation-driven evaluation with creative optimization. Yulun’s background includes multiple research roles from USC labs to large-scale production-facing internships, giving him strong experimental rigor and systems intuition. He holds CS degrees from USC and is pursuing a PhD in Mechatronics, Robotics, and Automation at CMU, blending formal methods with practical deployment considerations. An understated strength is his experience as a course producer and educator, which sharpens his communication of complex robotics concepts to diverse audiences.
8 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Southern California
Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering, Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering at Carnegie Mellon University
Chinese, English