Peiyan Zhang

I'm a fourth-year Ph.D. student at the Hong Kong University of Science and Technology (HKUST), supervised by Prof. Yangqiu Song and Prof. Sunghun Kim.

In 2020, I received my Bachelor's Degree in Computer Science and Technology, Beijing Institute of Technology with the Graduate Excellence Award of Beijing (2020) and National Scholarships (2017-2019).

From March 2022 to April 2023, I worked as a research intern at Microsoft Research Asia, supervised by Chaozhuo Li, and Xing Xie. My work on recommender systems won the Best Paper Award - Honorable Mention in WSDM 2023. I have won the Award of Excellence of Stars of Tomorrow Internship Program in Microsoft Research Asia (Top 10%).

Since August 2022, I am a visitor of Prof. Haohan Wang's group at University of Illinois Urbana-Champaign (UIUC), working on trustworthy machine learning systems.

From July 2023 to December 2023, I worked as a research intern at Beijing Academy of Artificial Intelligence (BAAI), supervised by Zheng Liu and focusing on large language models (LLM) & retrieval-oriented pre-training algorithms & end-to-end optimized information retrieval systems.

Currently, I am involved in pioneering efforts on Guard AI, an Automated AI Vulnerability and Defense Platform that secures AI systems for businesses. We specialize in monitoring, analyzing, and protecting LLMs without accessing internal data.

Email  /  CV  /  CV (Chinese)  /  Google Scholar  /  Github

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  • News (Aug. 2024): 🚀 Excited to release our recent survey on Graph Prompt Learning: "Towards Graph Prompt Learning: A Survey and Beyond"! 🚀
  • News (Jul. 2024): 🚀 Excited to release our recent survey on LLM & VLM Jailbreaking: "JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models"! 🚀
  • News (Mar. 2024): Our Paper "GPT4Rec: Graph Prompt Tuning for Streaming Recommendation" is accepted by SIGIR 2024.
  • News (Mar. 2024): Our Paper "TransGNN: Harnessing the Collaborative Power of Transformer and Graph Neural Network for Recommender Systems" is accepted by SIGIR 2024.
  • News (Jan. 2024): Our Paper "Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective" is accepted by WWW 2024 (Oral Presentation).
  • News (Jan. 2024): Our Paper "High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs" is accepted by WWW 2024 (Poster Presentation).
  • News (Jan. 2024): Our Paper "Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models" is accepted by ICLR 2024 (Poster Presentation).
  • News (Jul. 2023): Our Paper "A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking" is accepted by NeurIPS 2023 (Poster Presentation).
  • News (Jul. 2023): Our Paper "AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation" is accepted by CIKM 2023 (Oral Presentation).
  • News (Jul. 2023): We presented our work on Practical Content-aware Session-based Recommendation: Deep Retrieve then Shallow Rank at KDDCup 2023 (Oral Presentation).
  • News (Jul. 2023): We are recognized as Winners of Amazon KDD Cup 2023 Challenge. Rank: 3rd Place in the world!
  • News (Apr. 2023): I got Award of Excellence of Stars of Tomorrow in Microsoft Research Asia (Top 10%).
  • News (Apr. 2023): We presented our work on Continual Learning on Dynamic Graphs via Parameter Isolation at SIGIR 2023 (Oral Presentation).
  • News (Mar. 2023): We got "Best Paper Honorable Mention" at WSDM 2023 on our work Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network.
  • News (Oct. 2022): We presented our work on Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network. at WSDM 2023 (Oral Presentation).
  • News (Aug. 2022): We presented our work on Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation" at CIKM 2022 (Oral Presentation).
Research Interest

My research focuses on the development of trustworthy machine learning methods for e-commerce and financial applications, including time-series analysis, data mining, semi-/un-supervised learning, large language models and related applications such as recommender systems and financial quantitative trading. In my work, I use statistical analysis and deep learning methods, with an emphasis on data analysis using methods least influenced by spurious signals.

My research experience has equipped me with an in-depth understanding of how artificial intelligence can be used to solve complex, data-centric problems.

I am currently seeking a postdoctoral position starting in the second half of 2025. If you have any opportunities that align with my expertise, please feel free to reach out to me via email.

Selected Publications (* joint first authors)
Towards Graph Prompt Learning: A Survey and Beyond
Qingqing Long, Yuchen Yan, Peiyan Zhang*, Chen Fang, Wentao Cui, Zhiyuan Ning, Meng Xiao, Ning Cao, Xiao Luo, Lingjun Xu, Shiyue Jiang, Zheng Fang, Chong Chen, Xian-Sheng Hua and Yuanchun Zhou
Manuscript, 2024
Paper
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
Haibo Jin, Leyang Hu, Xinuo Li, Peiyan Zhang*, Chonghan Chen, Jun Zhuang, Haohan Wang
Manuscript, 2024
Paper / Website
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang*, Yuchen Yan*, Chaozhuo Li, Liying Kang, Xi Zhang, Feiran Huang, Senzhang Wang and Sunghun Kim
International ACM SIGIR Conference (SIGIR), 2024
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang*, Yuchen Yan*, Chaozhuo Li, Xi Zhang, Senzhang Wang, Xing Xie, Sunghun Kim
International ACM SIGIR Conference (SIGIR), 2024
Paper / Code
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs
Peiyan Zhang, Chaozhuo Li, Liying Kang, Feiran Huang, Senzhang Wang, Xing Xie, Sunghun Kim
International World Wide Web Conference (WWW), 2024
Poster Presentation
Paper
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Yuchen Yan, Peiyan Zhang, Zheng Fang, QingqingLong
International World Wide Web Conference (WWW), 2024
Oral Presentation
Paper
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Peiyan Zhang, Haoyang Liu, Chaozhuo Li, Xing Xie, Sunghun Kim, Haohan Wang
International Conference on Learning Representations (ICLR), 2024
Poster Presentation
Paper
A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking
Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Lichao Sun, Xing Xie, Senzhang Wang
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
Poster Presentation
Paper / Code
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang*, Peiyan Zhang*, Yingtao Luo, Chaozhuo Li, Jaeboum Kim, Kai Zhang, Senzhang Wang, Xing Xie and Sunghun Kim
International ACM CIKM Conference (CIKM), 2023
Oral Presentation
Paper / Code
Practical Content-aware Session-based Recommendation: Deep Retrieve then Shallow Rank
Yuxuan Lei, Xiaolong Chen, Defu Lian, Peiyan Zhang, Jianxun Lian, Chaozhuo Li, Xing Xie
International ACM KDD Conference (KDD) workshop, 2023
Oral Presentation
Paper
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang*, Yuchen Yan*, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim
International ACM SIGIR Conference (SIGIR), 2023
Oral Presentation
Paper / Code
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Peiyan Zhang*, Jiayan Guo*, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim
International ACM WSDM Conference (WSDM), 2023
Oral Presentation, Best Paper Award Honorable Mention
Paper / Code
A Survey on Incremental Update for Neural Recommender Systems
Peiyan Zhang, Sunghun Kim
Manuscript, 2023
Paper
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Jiayan Guo*, Peiyan Zhang*, Chaozhuo Li, Xing Xie, Yan Zhang, Sunghun Kim
ACM International ACM CIKM Conference (CIKM), 2022
Oral Presentation
Paper / Code
Word shape matters: Robust machine translation with visual embedding
Haohan Wang, Peiyan Zhang, Eric P Xing
Manuscript, 2020
Paper
Services
  • PC-Member
    • AAAI 2025
    • WWW 2024 Data-centric Artificial Intelligence Workshop
    • KDD 2024 Ethical Artificial Intelligence Workshop
  • Reviewer
    • Conference: NeurIPS, ICLR, ICML, KDD, WWW, CVPR, EMNLP, AAAI, IJCAI, PAKDD, AISTAT
    • Journal: IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, Neural Networks, Information Fusion, Neural Computing & Applications, Future Generation Computer Systems, Computers in Biology and Medicine
Selected Honors and Awards
  • ACM SIGIR Student Travel Grant for SIGIR 2024
  • Award of Excellence of Stars of Tomorrow Internship Program (Top 10%), 2023 (Microsoft Research Asia)
  • Winners of Amazon KDD Cup 2023 Challenge (3rd Place), 2023 (KDDCup 2023)
  • Best Paper Award - Honorable Mention, 2023 (WSDM 2023)
  • RedBird PhD Scholarship, 2020 (Hong Kong University of Science and Technology)
  • Graduate Excellence Award of Beijing (top 1%), 2020 (Ministry of Education, PRC)
  • National Scholarship, 2019 (Ministry of Education, PRC)
  • National Scholarship, 2018 (Ministry of Education, PRC)
  • National Scholarship, 2017 (Ministry of Education, PRC)

Thanks Dr. Jon Barron for sharing the source code of his personal page.