Yang Zheng

alt text 

Yang Zheng

Assistant Professor
Department of Electrical and Computer Engineering
Department of Computer Science & Engineering (by Courtesy)
Contextual Robotics Institute
University of California San Diego

Office: #3308 Franklin Antonio Hall
Email: zhengy@ucsd.edu
Phone: 858-534-5687

ORCID · Google Scholar · GitHub · Twitter

Office hours

  • Tuesday, 11:00 am - 1:00 pm

  • Feel free to email me to schedule office hours if needed.

Prospective students: I am actively seeking highly motivated students with a strong background in mathematics, theory, and computation to join my research group. If you have a strong interest in optimization and control, I encourage you to apply. Application instructions can be found here.

Prospective students and visiting interns are welcome to get in touch. Feel free to drop me an email (zhengy@ucsd.edu) with your CV, transcripts, and a brief overview of your interest in joining my group.

Check here: Join us!

Brief Biography

I am an Assistant Professor in Electrical and Computer Engineering at the University of California San Diego. I am affiliated with the CSE department and Contextual Robotics Institute.

Before joining UCSD, I was a research associate in the Verification of Autonomous Systems group at Imperial College London. From 03/2019 to 08/2020, I was a postdoctoral scholar in SEAS and CGBC at Harvard University, working with Prof. Na Li and Prof. Ali Malkawi. I received the B.S. and M.E. degrees in the Department of Automotive Engineering from Tsinghua University, Beijing, China, in 2013 and 2015, respectively. In Feb. 2019, I received the DPhil (Ph.D.) degree in Control from the University of Oxford for my work on Chordal Sparsity in Control and Optimization of Large-scale Systems under the supervision of Prof. Antonis Papachristodoulou.

I am broadly interested in learning, optimization, and control of network systems, and their applications to autonomous vehicles and traffic systems. My current research interests include:

  • Convex and non-convex optimization for control; see e.g., this project.

  • Principled data-driven and learning-based control; see e.g., this project

  • Structures and algorithms for large-scale semidefinite optimization; see e.g.,this project.

  • Applications in mixed traffic control, and certifiable robustness of machine learning (e.g., deep neural networks); see e.g., this project.

Check out some exciting research in the SOC lab at UCSD.