PeopleofSuperintelligence

Researcher

Qiang Xu

Also known as 徐强

Qiang Xu is a computer scientist whose work at the Chinese University of Hong Kong joins AI and chip design, from the security of neural networks to large circuit models for electronic design automation (EDA). After studying telecommunication engineering in Beijing, he earned a Ph.D. at McMaster University in 2005 and then joined CUHK. A paper he co-wrote on approximate adder design won the ICCAD Ten Year Retrospective Most Influential Paper Award in 2022, and in 2024 he co-initiated the large circuit model concept for AI-native EDA. He also served as Chief Scientist of China's National Technology Innovation Center for EDA. At CUHK, he is a professor of computer science and engineering and director of the CURE Lab.

Badges

Timeline

  1. 1997–2005

    Degrees and a DATE best paper

    He earns a B.E. in 1997 and an M.E. in 2000, both in telecommunication engineering at Beijing University of Posts and Telecommunications. In 2004, he receives the Best Paper Award at the IEEE/ACM Design, Automation and Test in Europe conference (DATE). After one and a half years at a start-up integrated circuit design house, he completes a Ph.D. in electrical and computer engineering at McMaster University in 2005 and then joins CUHK.

  2. By 2015

    Leading the CURE Lab

    By 2015, he leads the CUhk REliable computing laboratory (CURE Lab) and CUHK MakerLab, with research in fault-tolerant computing, trusted computing and smart hardware design. He is the director of the CURE Lab.

  3. 2015

    ApproxANN for neural networks

    In 2015, he coauthors ApproxANN, an approximate computing framework for artificial neural networks, with Qian Zhang, Ting Wang, Ye Tian and Feng Yuan. The paper appears at the Design, Automation & Test in Europe conference (DATE) and ranks among his top-cited publications.

  4. 2017–2020

    Security of neural networks

    He coauthors papers on the security of deep neural networks: a fault injection attack at ICCAD in 2017, the power side-channel attack "I Know What You See" on convolutional neural network accelerators at ACSAC in 2018, and DeepDyve, a dynamic verification method for deep neural networks, at ACM CCS in 2020.

  5. 2022

    An ICCAD retrospective award

    In 2022, "On Reconfiguration-Oriented Approximate Adder Design and Its Application," which he wrote with Rong Ye, Ting Wang, Feng Yuan and Rakesh Kumar, receives the William J. McCalla ICCAD Ten Year Retrospective Most Influential Paper Award. The award goes to the ICCAD paper judged most influential on research and industry practice over its first ten years.

  6. March 2024

    The Dawn of AI-native EDA

    In March 2024, he is one of 49 authors of "The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models," which calls for a shift from AI4EDA to AI-native EDA built on multimodal large circuit models. He co-initiates the large circuit model concept in 2024.

  7. September 2025

    An ICCAD panel on large models

    In September 2025, he is first author of "Revolution or Hype? Seeking the Limits of Large Models in Hardware Design," the foundation paper for an ICCAD 2025 panel. His co-authors come from Southeast University, IBM, TU Darmstadt, the University of Bremen and Synopsys, and the paper introduces him as a CUHK professor who previously served as Chief Scientist of the National Technology Innovation Center for EDA in China.

  8. As of October 2026

    Professor and CURE Lab director

    He is a professor of computer science and engineering at CUHK and director of the CURE Lab. His research interests include AI safety and security, learning-based testing, and motion analysis.

Career

  1. Since 2005Chinese University of Hong KongProfessor of Computer Science and Engineering
  2. National Technology Innovation Center for EDA in ChinaChief Scientist

Education

  1. 2005McMaster UniversityPh.D.
  2. 2000Beijing University of Posts and TelecommunicationsB.E. and M.E.

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Qiang Xu. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes Chinese University of Hong Kong. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. The United States employs an estimated 48% of people shaping AIIncludes Chinese University of Hong Kong. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Chinese University of Hong Kong. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Qiang Xu yet. Send us a free-licensed photo.

  1. Welcome to Qiang Xu's Homepage cse.cuhk.edu.hk. Checked 22 October 2026.
  2. Qiang Xu – CUHK CSE cse.cuhk.edu.hk. Checked 22 October 2026.
  3. History - ICCAD 2022 2022.iccad.com. Checked 22 October 2026.
  4. Professor Kumar wins 4th 'Most Influential' award | Coordinated Science Laboratory | Illinois csl.illinois.edu. Checked 22 October 2026.
  5. [2403.07257] The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models arxiv.org. Checked 22 October 2026.
  6. Revolution or Hype? Seeking the Limits of Large Models in Hardware Design arxiv.org. Checked 22 October 2026.