Wayne Chi
Computer Science Ph.D. Student at Carnegie Mellon University
Previously, Applied Scientist at Amazon and NASA JPL
waynechi[AT]cs[DOT]cmu[DOT]edu
Gates Hillman Centers 7121
Hi, my name is Wayne. I am a third year Ph.D. Student in the Computer Science Department at CMU advised by Chris Donahue and Ameet Talwalkar. I am funded by the 2025 NDSEG Fellowship.
I work on applications of machine learning that support human creativity with a focus on game development. My work includes human-centric code evaluation (1, 2) and agentic game development (3). I have also worked on music (4, 5), healthcare, and space automation (6, 7).
I completed my undergraduate and master’s at the University of Southern California. Before starting my Ph.D., I spent around 7 years in industry working as an Applied Scientist at AWS (2019-2023) and NASA’s Jet Propulsion Laboratory (2016-2019). If you are interested in any of my research topics or considering a Ph.D. after industry feel free to reach out.
news
| Jun 11, 2026 | GameDevBench is accepted to ICML 2026! Results available here. |
|---|---|
| Feb 9, 2026 | EDIT-Bench is accepted to ICLR 2026 as an Oral (Top 1%) |
| May 1, 2025 | Started my internship at Microsoft Research |
| May 1, 2025 | Copilot Arena is accepted to ICML 2025 |
| Apr 1, 2025 | Awarded the NDSEG Fellowship |
publications
2026
2025
2024
2022
- Onboard planning for the Mars 2020 Perseverance rover
2021
- Enabling limited resource-bounded disjunction in scheduling
- Analyzing the effectiveness of rescheduling and flexible execution methods to address uncertainty in execution duration for a planetary rover
- Symbolic music generation with transformer-gans
- Music generation system
2020
- Scheduling with complex consumptive resources for a planetary rover
- Automated volcano monitoring using multiple space and ground sensors
- Using a model of scheduler runtime to improve the effectiveness of scheduling embedded in execution
2019
- Supporting automation in spacecraft activity planning with simulation and visualization
- Temporal brittleness analysis of task networks for planetary rovers
- Optimizing parameters for uncertain execution and rescheduling robustness
- Using Rescheduling and Flexible Execution to Address Uncertainty in Execution Duration for a Planetary Rover
2018
- Embedding a Scheduler in Execution for a Planetary Rover
- Using Squeaky Wheel Optimization to Derive Problem Specific Control Information for a One Shot Scheduler for a Planetary Rover