My name is Yulun Zhang (张宇伦). I am a third 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, evolutionary computation, and generative modeling methods.
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 Oct 2024).
I am also enthusiastic about photography, especially scenery photography. Check out my photography portfolio and instagram for some of my works.
Contact me: yulunzhang [at] cmu [dot] edu
Ph.D. in Robotics, 2022 - Present
Carnegie Mellon University
MSc in Computer Science, 2020 - 2022
University of Southern California
BSc in Computer Science, 2017 - 2021
University of Southern California
[2024/08] Physically attended IJCAI 2024 in Jeju Island, South Korea!
[2024/06] Physically attended SoCS 2024 in Kananaskis, Alberta, Canada!
[2024/05] Started working as an Applied Scientist Intern in Amazon Robotics in Boston, MA, USA!
[2024/04] Two papers (GGO and MAPF Mechanism) were accepted to IJCAI 2024!
[2024/03] One MAPF position paper and three extended abstracts were accepted to SoCS 2024!
[2023/12] Physically attended NeurIPS 2023 in New Orleans, LA, USA!
[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!