Academic Profile : Faculty

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Prof Liu Yang
Professor, School of Computer Science and Engineering
University Leadership Forum Chair in Computer Science and Engineering
External Links
Dr. Yang Liu obtained his bachelor and ph.d degree in the National University of Singapore in 2005 and 2010, respectively. In 2012, he joined Nanyang Technological University as a Nanyang Assistant Professor. He is currently a full professor, director of the cybersecurity lab, Program Director of HP-NTU Corporate Lab and Deputy Director of the National Satellite of Excellence of Singapore. In 2019, he received the University Leadership Forum Chair professorship at NTU.

Dr. Liu specializes in software verification, security and software engineering. His research has bridged the gap between the theory and practical usage of formal methods and program analysis to evaluate the design and implementation of software for high assurance and security. By now, he has more than 270 publications in top tier conferences and journals. He has received a number of prestigious awards including MSRA Fellowship, TRF Fellowship, Nanyang Assistant Professor, Tan Chin Tuan Fellowship, Nanyang Research Award (Young Investigator) 2018, NRF Investigatorship 2020 and 10 best paper awards and one most influence system award in top software engineering conferences like ASE, FSE and ICSE.
For cybersecurity, we are working at malware modeling, detection, classification and generation with the focus on Javascript malware, desktop malware and Android malware. We are developing tools for vulnerability modeling and detection using machine learning and (both static and dynamic) program analysis on binary code. In our Securify research project (2015 - 2020), we are performing formal verification on security system from hardware, hypervisor, programs to security protocol using different verification approaches. Recently, we embark on the research on Automotive Security and autonomous vehicle Security in their security design, runtime security monitoring and response, and also the security testing and certification.

For software engineering, we are working on the topics related to program specification learning and model learning, performance analysis, Android energy analysis, reliability analysis, code clone analysis, program debugging, program testing, automatic loop analysis, testing and validating deep learning algorithms using techniques like model checking, symbolic execution and machine learning. We are building tools related to these aspects. For Android system, we have been working on security analysis on App malware detection & classification, generation and data analytic, App vulnerability analysis, App testing, Android OS testing and fuzzing, and Automatic UI generation.

For multi-agent systems, we are working on the topics related to formal modeling of various multi-agent systems, particularly trust management systems and their analysis in correctness, security and robustness.

For big data, we are promoting the concept called event analytic based on behavior learning and analysis, and their applications in sports and finance systems.
  • 2019 University Leadership Forum Chair in Computer Science and Engineering
  • A Framework for Intellectual Property Protection of Deep Learning Applications
  • Automatic Open Source Component Detection and Security Risk Management for Automotive Systems
  • Building Security Tools for Investigating and Introspecting Applications in Trusted Execution Environment
  • CoE Innovator (Entrepreneurship) Award 2022
  • Emulation Based Fuzzing Technique
  • Enhancing Cyber Resilience of Deep Learning Models Against Adversarial Cyber Attacks
  • National Satellite Of Excellence In Trustworthy Software Systems
  • Smart Safe and Robust Motion Control for Multi-Robot Systems
  • The Science of Certified AI Systems
  • Thrust B: Artificial Intelligence and Software Engineering (IAF-ICP)
  • Thrust B: Artificial Intelligence and Software Engineering (RCA)
  • Towards Building Trustworthy and Robust Intelligent Systems and Its Application on Human Brain
  • Towards Semantic-Aware Multimodal and Multilingual Deep Learning Systems for E-Commerce Applications
  • TrustFUL: Trustworthy Federated Ubiquitous Learning
  • TrustFUL: Trustworthy Federated Ubiquitous Learning (SCSE)
  • Trustworthy and Explainable AI