Scholar
Zhuoyuan Wang
Google Scholar ID: O46T3uUXCH4C
PhD student, Carnegie Mellon University
Citations & Impact
Citations
153
H-index
6
i10-index
2
Publications
15
Co-authors
0
Publications
Resume
Academic Achievements
- 1. Publications:
- - “Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems” accepted by IEEE Transactions on Automatic Control (TAC)
- - “Neural Spline Operators for Risk Quantification in Stochastic Systems” accepted to CDC 2025
- - Completed Thesis Prospectus titled “Bridging Physics and Learning: Safe and Efficient Control Systems with Theoretical Guarantees”
- 2. Conference papers:
- - CDC 2025, AAAI 2024, ICRA 2024, L4DC 2023, ACC 2022, L-CSS 2023, TPAMI 2020
Research Experience
- 1. Research agenda: Theoretically grounded safe and efficient control systems via integration of physics and learning.
- 2. Key research thrusts include:
- - Myopically verifiable long-term safe control under uncertainty
- - Physics-informed optimal and safe control
- - Scalable and generalizable learning for control
Education
- 1. PhD - Electrical and Computer Engineering, Carnegie Mellon University, Advisor: Yorie Nakahira
- 2. Bachelor's Degree - Tsinghua University, Advisors: Gao Huang, Yilin Mo
Background
- Research interests include safety-critical control, physics-informed learning, stochastic systems, and robotics. Currently a final year PhD student in Electrical and Computer Engineering at Carnegie Mellon University, advised by Prof. Yorie Nakahira. Previously obtained a Bachelor's degree from Tsinghua University, advised by Prof. Gao Huang and Prof. Yilin Mo.
Miscellany
- Contact: zhuoyuaw [at] andrew.cmu.edu
- Follow: Google Scholar, LinkedIn, jacobwang925