PeopleofSuperintelligence

Researcher

Nevin Lianwen Zhang

Also known as 張連文, Nevin L. Zhang

Nevin Lianwen Zhang is a computer scientist at the Hong Kong University of Science and Technology whose research covers latent tree models, clustering and topic detection. He studied applied mathematics at Beijing Normal University, where he earned a PhD in 1989, and completed a computer science PhD at the University of British Columbia in 1994. His papers include work on Bayesian network inference in 1996, hierarchical latent class models in 2004 and hierarchical topic detection in 2016. In 2017, he and colleagues published a data-driven method for identifying syndrome types in traditional Chinese medicine, and in 2024 he coauthored TCM-FTP, which fine-tunes large language models to predict herbal prescriptions. He is a professor of computer science and engineering at HKUST, where he works on explainable AI, domain generalization, natural language processing and machine learning for drug discovery.

Timeline

  1. 1983–1994

    Applied math and a UBC PhD

    He earns a BSc in applied mathematics from the University of Electronic Science and Technology of China in 1983, then an MSc in 1986 and a PhD in 1989 at Beijing Normal University. In 1994, he completes a PhD in computer science at the University of British Columbia.

  2. 1996–2001

    Bayesian networks and POMDPs

    In 1996, he coauthors a Journal of Artificial Intelligence Research paper on exploiting causal independence in exact Bayesian network inference, tested on the CPCS medical-diagnosis networks. In 2001, he coauthors a paper in the same journal on speeding up the convergence of value iteration for partially observable Markov decision processes.

  3. 2004

    Hierarchical latent class models

    In 2004, he publishes "Hierarchical Latent Class Models for Cluster Analysis," a framework that addresses the local dependence problem of latent class models, together with a search-based algorithm for learning such models from data.

  4. 2008

    Latent trees for approximate inference

    In 2008, he coauthors a method with Yi Wang and Tao Chen for approximate inference in Bayesian networks. It samples data from a network, learns a latent tree model from that data offline, and makes inference with the tree model online, where inference takes linear time.

  5. 2016

    Topic detection with latent trees

    In 2016, he and Peixian Chen, Tengfei Liu and colleagues present a topic detection method that models document collections with hierarchical latent tree models, where each latent variable gives a soft partition of the documents. With Leonard K. M. Poon, he also writes the overview "Latent Tree Analysis" in 2016, covering cluster analysis, topic detection and deep probabilistic modeling.

  6. 2017

    Syndrome types in Chinese medicine

    In 2017, he and colleagues publish in the Journal of Integrative Medicine a data-driven method that identifies traditional Chinese medicine syndrome types from unlabeled symptom survey data and generalizes latent class analysis. A software package called Lantern supports the method.

  7. 2024

    Herbal prescriptions with LLMs

    In 2024, he coauthors TCM-FTP with Xingzhi Zhou and colleagues, a method that fine-tunes pre-trained large language models on DigestDS, a new dataset of medical records on digestive system diseases. TCM-FTP reaches an F1-score of 0.8031 for herbal prescription prediction.

  8. As of October 2026

    Professor at HKUST

    He is a professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology, where he works with students and collaborators on explainable AI, domain generalization, natural language processing and machine learning for drug discovery. He is an Associate Editor of Artificial Intelligence Journal and Co-Director of the Dual Degree Program in Technology and Management.

Career

  1. NowHong Kong University of Science and TechnologyProfessor

Education

  1. 1994University of British ColumbiaPhD
  2. 1989Beijing Normal UniversityPhD
  3. 1986Beijing Normal UniversityMSc
  4. 1983University of Electronic Science and Technology of ChinaBSc

From the biography.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Hong Kong 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.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Hong Kong 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.
  3. Google has the highest combined h-index among AI organizationsIncludes Hong Kong University of Science and Technology. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Hong Kong University of Science and Technology. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.

Sources and credits

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  1. Nevin Lianwen ZHANG | HKUST CSE cse.hkust.edu.hk. Checked 22 October 2026.
  2. Nevin L. Zhang cse.hkust.edu.hk. Checked 22 October 2026.
  3. [cs/9612101] Exploiting Causal Independence in Bayesian Network Inference arxiv.org. Checked 22 October 2026.
  4. [1106.0251] Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes arxiv.org. Checked 22 October 2026.
  5. Hierarchical Latent Class Models for Cluster Analysis jmlr.org. Checked 22 October 2026.
  6. [1401.3429] Latent Tree Models and Approximate Inference in Bayesian Networks arxiv.org. Checked 22 October 2026.
  7. [1605.06650] Latent Tree Models for Hierarchical Topic Detection arxiv.org. Checked 22 October 2026.
  8. [1610.00085] Latent Tree Analysis arxiv.org. Checked 22 October 2026.
  9. A data-driven method for syndrome type identification and classification in traditional Chinese medicine - The Hong Kong University of Science and Technology Research Portal researchportal.hkust.edu.hk. Checked 22 October 2026.
  10. [2407.10510] TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction arxiv.org. Checked 22 October 2026.
  11. Nevin Lianwen ZHANG 張連文 | HKUST School of Engineering seng.hkust.edu.hk. Checked 22 October 2026.