Academic Profile

Dr. Tianwei Zhang received his Bachelor’s degree in physics at Peking University, in 2011, and the Ph.D degree in Electrical Engineering at Princeton University in 2017. He worked at Amazon as a software engineer from 2017 to 2019. He joined Nanyang Technological University as an assistant professor in 2019. He has published papers in top-tier architecture and security conferences and journals. He has served on the Program Committee of many conferences, and reviewers of journals.
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Asst Prof Zhang Tianwei
Assistant Professor, School of Computer Science and Engineering

Dr. Zhang's research focuses on computer system and architecture security. Specifically,

* Distributed system security: he is interested in building secure and flexible frameworks for attesting and protecting distributed systems, including cloud computing, IoT, robotic platforms, etc.
* Machine learning security: he is interested in exploring security threats in deep learning platforms, and defense solutions.
* Attacks and defenses: he is interested in designing system-level attacks, and methodologies of defeating sophisticated attacks.
* Security analysis: he is interested in designing novel methodologies of measuring and quantifying system and architecture security.
  • A Cloud-based Framework for Protecting Autonomous Vehicles

  • Building Security Tools for Investigating and Introspecting Applications in Trusted Execution Environment

  • Design and Evaluation of Cyber Attacks against AI Chips

  • Detecting and Preventing Robotic Attacks

  • General-purpose Frameworks for Quantifying Sidechannel Information Leakage using Deep Learning and Defeating Side-channel Attacks using Differential Privacy

  • Smart Safe and Robust Motion Control for Multi-Robot Systems

  • Trustworthy and Explainable AI “Safe, Fair and Robust AI System Development, Transparent or Explainable AI System Development, Explainability and Trust (Safe, Fair, Robust) Assessment”
  • Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu. (2020). Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning. Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020).