Chao Huang's Webpage

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Chao Huang Lecturer (Assistant Professor) Department of Computer Science University of Liverpool Liverpool, UK Email: chao.huang2 [AT] liverpool [DOT] ac [DOT] uk

I am now a lecturer in the department of computer science in the University of Liverpool,UK. My research interests include design and verification of various cyber physical systems, e.g. hybrid systems, neural-network controlled systems. Before joining Liverpool, I worked with Prof. Qi Zhu as a postdoc fellow, in the ECE department at Northwestern University. Prior to Northwestern, I received my B.S. and PhD in mathematics and applied mathematics and computer science from Nanjing University respectively. I also visited the Department of Computer Science at Aalborg University and was pleased to work with Prof. Kim G. Larsen.

For Prospective Students

I am looking for highly motivated students, who are interested in at least one of the following topics:

  • cyber-physical systems,

  • design automation,

  • computational proof methods,

  • machine learning. The students are expected to have a strong background in at least one of the following areas:

  • computer science/engineering,

  • mathematics,

  • statistics,

  • physics.

Recent News

  • 2022.03 Our paper Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding has been accepted by DATE 2022 and received Best Paper Award!

  • 2022.02 Our paper Design-while-Verify: Correct-by-Construction Control Learning with Verification in the Loop has been accepted by DAC 2022.

  • 2022.01 Our paper Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner has been accepted by ICCPS 2022.

  • 2021.07 Our paper Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping has been accepted by EMSOFT 2021.

  • 2021.02 Our paper Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation has been accepted by DAC 2021.


Design and Verification of Learning-Enabled Systems

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Learning-enabled systems have been receiving numerous attractions from both academia and industry due to promising applications. They often leverage machine learning techniques in their perception of the environment, and increasingly also in the consequent decision making process for planning, navigation, control, etc. With no doubt, safety is one of the key issues before such systems are applied in practice. In this project, we conduct research on safety verification and design of learning-enabled systems with respect to perception, adaptation and control. We also develop tools for demonstration. Details of this project can be found in the project page.

Verification of Weakly-hard Systems

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Timing uncertainty is one major type of uncertainty during the operation of autonomous systems, i.e., how much time it takes for certain function to complete and whether that meets the deadline. We consider the weakly-hard systems, where deadline misses are allowed in a bounded manner. A common example is that such deadline misses are described by (m,K) constraints, which simply specifies that among any K consecutive executions, at most m instances can miss their execution deadlines.. One of the most important functional properties is safety. In our weakly-hard framework, we consider whether the system with (m,K) constraints will ever enter a pre-specified unsafe state set (see the figure below). We develop several approaches to formally verify the safety of weakly-hard systems. We also develop tools for demonstration. Details of this project can be found in the project page.


  • Nanjing University: PhD, Computer science, 2011 - 2018

  • Nanjing University: B.S., Mathematics, 2007 - 2011


  • Northwestern University, Evanston: Postdoc fellow, ECE, 2018 - present