Zhanke Zhou (Andrew)

Zhanke Zhou (Andrew)

PhD student at HKBU.

Hi there, I am (Andrew) Zhanke Zhou, a Ph.D. student at TMLR group of Hong Kong Baptist University, advised by Prof. Bo Han and working with Prof. Jiangchao Yao. Currently, I am a visiting student at STAIR lab of Stanford University, working with Prof. Sanmi Koyejo. Prior to that, I was a visiting student at LARS group of Tsinghua University, working with Prof. Quanming Yao and Prof. Yongqi Zhang.

My research focuses on trustworthy machine reasoning with foundation models (LLMs, VLMs) to solve complex problems such as mathematics and coding, as well as to accelerate scientific discovery and application in fields like biology, chemistry, and healthcare. I believe that reasoning is the essential pathway to achieving AGI. Trustworthy machine reasoning encompasses properties such as reasoning power, robustness, safety, and explainability. My work involves developing methodologies, benchmarks, and theoretical foundations to advance these areas.

I am always open for possible collaborations. Please feel free to email me for research, collaborations, or a casual chat.

📖 Educations

  • 2022.09 - present, Hong Kong Baptist University (HKBU), Ph.D. in Computer Science.
  • 2017.09 – 2021.06, Huazhong University of Science and Technology (HUST), B.E. in Electronics and Information Engineering (SeedClass).

📝 Selected Publications on Trustworthy Language-model Reasoning

* Co-first author, ✉️ Corresponding author.

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Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models.
Zhanke Zhou*, Zhaocheng Zhu*, Xuan Li*, Mikhail Galkin, Xiao Feng, Sanmi Koyejo, Jian Tang, Bo Han✉️
In ICLR 2025 Workshop on Reasoning and Planning for Large Language Models. [paper] [code]

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Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han✉️.
In NeurIPS 2024. [paper] [code] [slides] [poster] [EN-video] [CN-video] [CN-blog] [twitter]

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DeepInception: Hypnotize Large Language Model to Be Jailbreaker.
Xuan Li*, Zhanke Zhou*, Jianing Zhu*, Jiangchao Yao, Tongliang Liu, Bo Han✉️
In NeurIPS 2024 SafeGenAI Workshop. [paper] [code] [slides] [twitter] [CN-video] [CN-blog] [DeepTech]

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Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection.
Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han✉️
In ICML 2024. [paper] [code] [slides] [poster] [CN-video] [CN-blog]

📝 Selected Publications on Trustworthy Graph-model Reasoning

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On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation.
Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao✉️, Quanming Yao, Bo Han✉️.
In ICML 2023. [paper] [code] [slides] [poster] [EN-video] [CN-video] [CN-blog]

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Combating Bilateral Edge Noise for Robust Link Prediction.
Zhanke Zhou, Jiangchao Yao✉️, Jiaxu Liu, Xiawei Guo, Quanming Yao,
Li He, Liang Wang, Bo Zheng, Bo Han✉️.
In NeurIPS 2023. [paper] [code] [slides] [poster] [EN-video] [CN-video] [CN-blog]

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Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs.
Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han✉️.
In ICLR 2024. [paper] [code] [slides] [poster] [EN-video]

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Neural Atoms: Propagating Long-range Interaction in Molecular Graphs
through Efficient Communication Channel.
Xuan Li*, Zhanke Zhou*, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han✉️.
In ICLR 2024. [paper] [code] [slides] [poster] [EN-video] [CN-video]

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Adaprop: Learning Adaptive Propagation for Graph Neural Network Based Knowledge Graph Reasoning.
Yongqi Zhang*, Zhanke Zhou*, Quanming Yao✉️, Xiaowen Chu, Bo Han.
In KDD 2023. [paper] [code] [slides] [poster] [EN-video] [CN-video]

🎖 Awards

  • 2025.03, Madam Hui Tang Shing Yan Fellowship (only two awardees in HKBU).
  • 2024.11, Research Performance Award by COMP of HKBU.
  • 2024.10, Excellent Research Gold Award of TMLR Group.
  • 2024.06, Best Poster Award by COMP of HKBU.
  • 2024.05, Best Research Performance Award by COMP of HKBU.
  • 2023.11, Research Excellence Award by COMP of HKBU.
  • 2021.06, Honorary degree of HUST (Top 2%, highest honour for undergrad).
  • 2021.06, Outstanding Graduate Award of HUST.

💬 Talks

  • 2024.11, Seminar on Trustworthy Machine Learning and Foundation Models @AI Time, Online. [Video]
  • 2023.11, Seminar on Trustworthy Machine Learning with Imperfect Data @TechBeat, Online. [Video]
  • 2023.11, Youth PhD Talk on Trustworthy Machine Learning @AI Time, Online. [Video]

💻 Services

  • Conference Reviewer for ICML, NeurIPS, ICLR, AISTATS, ACML, AAAI, IJCAI, COLM, ARR, CIKM, SIGKDD.
  • Journal Reviewer for TPAMI, TMLR, NEUNET, TNNLS, TKDE.

🏫 Teaching

  • Teaching Assistant for COMP7250: Machine Learning.
  • Teaching Assistant for COMP3015: Data Communications and Networking.
  • Teaching Assistant for COMP7070: Advanced Topics in Artificial Intelligence and Machine Learning.

📖 Experiences

  • 2022.09 - present, PhD student @HKBU-TMLR Group, advised by Prof. Bo Han.
  • 2025.01 - present, Visiting student @Stanford-STAIR Lab, advised with Prof. Sanmi Koyejo.
  • 2022.02 - 2022.09, Research assistant @HKBU-TMLR Group, advised by Prof. Bo Han and Prof. Jiangchao Yao.
  • 2021.01 - 2024.05, Visiting student @THU-LARS Group, advised by Prof. Quanming Yao and Prof. Yongqi Zhang.
  • 2020.06 - 2020.09, Research intern @SJTU-MVIG Group, advised by Prof. Cewu Lu and Prof. Yonglu Li.
  • 2018.03 - 2021.01, Core Member @HUST-Dian Group, advised by Prof. Yayu Gao, Prof. Chengwei Zhang, and Prof. Xiaojun Hei.

💻 Resources

I hold that life’s best resources, like air, should be free.
Hence, I champion open-source research and hope the following resources can benefit you :)

Projects

Source Files of My Talks or Posters