My name is Yulun Zhang (张宇伦). I am a second year Ph.D. student in Robotics Institute at the Carnegie Mellon University, advised by Professor Jiaoyang Li. Currently I am focusing on environment optimization for Multi-Agent Path Finding (MAPF) algorithms using Quality-Diversity (QD) optimization and generative AI methods. As a long-term goal, I would like to bring QD optimization and Evolutionary Optimization to Robotics, expanding their applicability and scalability.
Previously, I was a master/undergrad student majoring in Computer Science at the University of Southern California. I was working in ICAROS lab at USC with Professor Stefanos Nikolaidis and Matt Fontaine on surrogate assisted QD optimization and scenario generation for human-robot coordination. I was also working in RESL at USC with Dr. Ryan Julian and K.R. Zentner on transfer learning for robotics.
In addition, I was working in the USC Interaction Lab with Dr. Matt Rueben on socially assistive robotics as well as Professor William Halfond’s group on record and replay tools for Android.
Here is my most recent CV (updated May 2023).
I am also enthusiastic about photography, especially scenery photography. Check out my photography portfolio and instagram for some of my works.
Ph.D. in Robotics
Carnegie Mellon University
MSc in Computer Science, 2022
University of Southern California
BSc in Computer Science, 2021
University of Southern California
[2023/10] Honored to receive the Quality of Life Tech Center Student Research Fund!
[2023/10] Our paper Arbitrarily Scalable Environment Generators via Neural Cellular Automata was accepted to NeurIPS 2023!
[2023/08] Physically attended IJCAI 2023 in Macao, S.A.R., China!
[2023/04] Our paper pyribs: A Bare-Bones Python Library for Quality Diversity Optimization was accepted to GECCO 2023!
[2023/04] Our paper Multi-Robot Coordination and Layout Design for Automated Warehousing was accepted to IJCAI 2023!
[2022/10] Joined ARCS Lab at CMU Robotics Institute as a PhD student!
[2022/10] Our paper Efficient Multi-Task Learning via Iterated Single-Task Transfer was accepted to IROS 2022!
[2022/07] Physically attended GECCO 2022 in Boston, MA, USA!
[2022/04] Our paper Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding was accepted to GECCO 2022!