Zhuoyuan Jacob Wang

Hi, I’m a final year PhD in Electrical and Computer Engineering at Carnegie Mellon University, advised by Prof. Yorie Nakahira. Prior to joining CMU, I obtained my Bachelor degree at Tsinghua University, advised by Prof. Gao Huang and Prof. Yilin Mo.

My research interests include safety-critical control, physics-informed learning, stochastic systems and robotics.

Contact: zhuoyuaw [at] andrew.cmu.edu
Follow: Google Scholar | LinkedIn | jacobwang925

I'm on the 2025 – 2026 job market!

My research agenda is centered on theoretically grounded safe and efficient control systems via integration of physics and learning. My research philosophy is to leverage physics model structure to enable learning-based control methods with high efficiency and theoretical guarantees. The key thrusts of my research include:

1. Myopically verifiable long-term safe control under uncertainty: proposing ‘forward-invariance’ on probability space, and designing one-step verifiable real-time online control strategies to guarantee long-term safety, despite unbounded noise, unknown system dynamics and latent risk from human interaction / occlusions. (ACC 22, IV 22, IFAC 23, ICRA 24, TAC 26)

2. Physics-informed optimal and safe control: deriving mathematical structure to connect risk probabilities and value functions in stochastic systems with partial differential equations (PDEs), and designing physics-informed learning framework for efficient estimation. (L4DC 23, AAAI 24, TAC 25)

3. Scalable and generalizable learning for control: developing dimensionality reduction techniques, efficient representation structures, and neural operator learning mechanisms for learning system designs that are scalable to high dimensions and are generalizable to varying system instances. (L-CSS 23, AAAI 24, CDC 25)