PeopleofSuperintelligence

Researcher

Di Wang

Also known as 王帝

Di Wang is a computer scientist at KAUST whose research develops privacy-preserving and trustworthy machine learning. He earned mathematics degrees at Shandong University and Western University and completed a Ph.D. in computer science and engineering at SUNY Buffalo, where he studied differential privacy, adversarial and interpretable machine learning, robust estimation and optimization. He joined KAUST in 2021 as an assistant professor and principal investigator of the PART Lab, with research spanning machine unlearning, fairness, explainability and knowledge editing. In 2022, his paper on private halfspace learning won ACML's Best Paper Award; in 2025, he received a Best Paper Award from AAAI's Connecting Low-Rank Representations in AI workshop and an Honorable Mention Paper Award from USENIX Security. In September 2026, he presented on differentially private training for large language models; as of October 2026, he is an assistant professor at KAUST, an adjunct professor of statistics and principal investigator of the PART Lab.

Now at
Computer, Electrical and Mathematical Sciences and Engineering Division
Works
292
Citations
1,178
h-index
17

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2014–2015

    Mathematics degrees

    At Shandong University, he earns a B.S. in mathematics and applied mathematics in 2014. He earns an M.S. in mathematics at Western University in 2015.

  2. 2018–2020

    Three graduate awards

    At SUNY Buffalo, he receives the Best CSE Graduate Research Award in 2018, the SEAS Dean's Graduate Achievement Award in 2019 and the CSE Best Doctoral Dissertation Award in 2020.

  3. By 2020

    A Buffalo PhD

    He completes a Ph.D. in computer science and engineering at SUNY Buffalo in 2020 under Jinhui Xu. During his doctoral studies, he visits UC Berkeley, Harvard University and Boston University; his research covers differential privacy, adversarial and interpretable machine learning, robust estimation and optimization.

  4. 2021

    Joining KAUST

    In January 2021, he joins KAUST as an assistant professor of computer science and principal investigator of the PART Lab. His appointment in the CEMSE Division begins in Spring 2021.

  5. 2022

    Private halfspace learning

    At ACML 2022, the paper he coauthors with Jinyan Su and Jinhui Xu wins the Best Paper Award. It studies PAC learning halfspaces under non-interactive local differential privacy, using Massart-noise and self-supervised approaches with sample complexity linear in the dimension and polynomial in other terms.

  6. 2025

    Two 2025 paper awards

    He earns a Best Paper Award at AAAI's 2025 workshop on Connecting Low-Rank Representations in AI and an Honorable Mention Paper Award at USENIX Security in 2025.

  7. September 2026

    Differential privacy for LLMs

    At a KAUST graduate seminar in September 2026, he presents “Differentially Private Training in the LLM Era.” He discusses scalable private pretraining and fine-tuning for large language models and methods to strengthen privacy guarantees.

  8. As of October 2026

    Assistant professor at KAUST

    As of October 2026, he is an assistant professor in KAUST's Computer Science Program and an adjunct professor in its Statistics Program, within the CEMSE Division. He is principal investigator of the Privacy Awareness, Responsibility and Trustworthy Lab. His research spans differential privacy, machine unlearning, fairness, explainability and knowledge editing.

Career

  1. Since 2021King Abdullah University of Science and TechnologyAssistant Professor of Computer Science; Adjunct Professor of Statistics
  2. NowComputer, Electrical and Mathematical Sciences and Engineering DivisionAssistant Professor
  3. 2024–2026Tsinghua University
  4. 2018University of California, BerkeleyVisiting Student
  5. Harvard UniversityVisiting Student
  6. Boston UniversityVisiting Student

Education

  1. 2020University at Buffalo, State University of New YorkPh.D. in computer science and engineering
  2. 2015Western UniversityM.S. in mathematics
  3. 2014Shandong UniversityB.S. in mathematics and applied mathematics

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Di Wang. 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. The United States employs an estimated 48% of people shaping AIIncludes King Abdullah University of Science and Technology. 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.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes King Abdullah University of Science and Technology. 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.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes King Abdullah University of Science and Technology. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Colleagues

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

Sources and credits

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

  1. Di Wang | Assistant Professor, Computer Science | Computer, Electrical and Mathematical Sciences and Engineering cemse.kaust.edu.sa. Checked 22 October 2026.
  2. Di Wang's Homepage shao3wangdi.github.io. Checked 22 October 2026.
  3. CSE Graduate Student Award Recipients - Department of Computer Science and Engineering - University at Buffalo engineering.buffalo.edu. Checked 22 October 2026.
  4. How To Preserve Privacy In Learning? – CUHK CSE cse.cuhk.edu.hk. Checked 22 October 2026.
  5. CEMSE New Faculty: Di Wang, Assistant Professor of Computer Science | Computer, Electrical and Mathematical Sciences and Engineering cemse.kaust.edu.sa. Checked 22 October 2026.
  6. 20210630-17 总第243期 面向伦理与隐私保护的智能技术 - 活动通知 - VALSE valser.org. Checked 22 October 2026.
  7. 14th Asian Conference on Machine Learning acml-conf.org. Checked 22 October 2026.
  8. [2209.08319] On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data arxiv.org. Checked 22 October 2026.
  9. Differentially Private Training in the LLM Era | Computer, Electrical and Mathematical Sciences and Engineering cemse.kaust.edu.sa. Checked 22 October 2026.