Professor
Alpha Lab
School of Information Science and Technology
University of Sicence and Technology of China (USTC)

Email: an.zhang3.14 at gmail.com
• Google Scholar Page • OpenReview Profile

Biography

I am currently a Professor at University of Science and Technology of China (USTC), where I lead the Alpha Lab. Prior to that, I was a postdoctoral research fellow in NExT++ Lab, National University of Singapore, working with Prof. Tat-Seng Chua. I obtained my Ph.D. from the National University of Singapore in 2021 and joined USTC in 2025. With my colleagues, students, and collaborators, we strive to develop foundation models (large language models & diffusion models) and AI Agents, with an emphasis on understanding the capabilities and properties of next-generation general AI models. - Capabilities: complex reasoning, self-reflection, self-improving, scaling law, and multi-modal integration. - Key properties: interpretability, trustworthiness, efficiency. Our research is motivated by, and contributes to, applications in information retrieval (e.g., recommender system), education (e.g., smart campus), and security (e.g., AI Safety). I have over 30 publications appeared in top-tier conferences (e.g., NeurIPS, ICLR, WWW, KDD, ICML, SIGIR) and journals (e.g., TPAMI, TOIS). Moreover, I have served as the PC member for top-tier conferences including NeurIPS, SIGIR, ICLR, KDD, EMNLP, WWW, AAAI, ICML, and WSDM.

Prospective Intern Students

I am looking for highly motivated interns (visiting Ph.D., master, and undergraduate students) to work together on the trustworthiness and robustness of graphs, especially pre-training, interpretability, generalization, and causality, and their applications in real-world scenarios. Please feel free to send me your CV and transcripts, if you have interest. We are also actively looking for opportunities in research, partnership and commercialization in exciting data science projects.

News and Highlights

  • [New!] 2023/10 One paper is accepted by TOIS'23! Thank all Collaborators!
         Robust Collaborative Filtering to Popularity Distribution Shift.

  • 2023/10 One paper is accepted by EMNLP'23 Findings! Big congrats to Yaorui Shi and Other Collaborators!
         ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction.

  • 2023/09 Three papers are accepted by NeurIPS 23! Big congrats to Zhiyuan Liu, Junfeng Fang, and Other Collaborators!
         Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss.
         Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis.
         Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules.

  • 2023/07 Two papers are accepted by ACM MM'23! Big congrats to Yicong Li, Wei Ji, and Other Collaborators!
         Redundancy-aware Transformer for Video Question Answering.
         Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation.

  • 2023/05 One paper is accepted by KDD'23! Big congrats to Fangfu Liu and Other Collaborators!
         Discovering Dynamic Causal Space for DAG Structure Learning.

  • 2023/01 One paper is accepted by WWW'23! Thank All Collaborators!
         Invariant Collaborative Filtering to Popularity Distribution Shift.

  • 2023/01 One paper is accepted by ICLR'23! Thank All Collaborators!
         Boosting Causal Discovery via Adaptive Sample Reweighting.

  • 2022/10 One paper is accepted by WSDM'23! Big congrats to Junfeng Fang and Other Collaborators!
         Cooperative Explanations of Graph Neural Networks.

  • 2022/09 One paper is accepted by NeurIPS'22! Thank All Collaborators!
         Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering.

  • 2022/05 One paper is accepted by ICML'22! Big congrats to Sihang Li and Other Collaborators!
         Let invariant Rationale Discovery inspire Graph Contrastive Learning.

  • 2022/05 One papers is accepted by KDD'22! Big congrats to Yunshan Ma and Other Collaborators!
         CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation.

  • 2022/04 One paper is accepted by TPAMI'22! Big congrats to Xiang Wang and Other Collaborators!
         Reinforced Causal Explainer for Graph Neural Networks.

  • 2022/01 One paper is accepted by ICLR'22! Big congrats to Yinxin Wu and Other Collaborators!
         Discovering invariant rationales for graph neural networks.

    • Professional Services

  • Review for ICML 2022, SIGKDD 2022, NeurIPS 2022, AAAI 2022, WSDM 2023, CVPR 2023, ICCV 2023, KDD 2023, ACM MM 2023, NeurIPS 2023, AAAI 2024, EMNLP 2024, WSDM 2024.

    • Honors and Awards

  • Full Research Scholarship, National University of Singapore, 2016-2021
  • Excellent Graduates, Southeast University, 2016
  • Merit Student in Jiangsu Province, 2015
  • Su Bote Outstanding Undergraduate Scholarship, Southeast University, 2014
  • Merit Student in Southeast University, Southeast University, 2015/2014/2013

    • Background

  • 2023-Present: Research Fellow, NExT++, National University of Singapore
         Supervisor: Prof Tat-Seng Chua & Prof Zhenkai Liang
  • 2021-2023: Research Fellow, NExT++, National University of Singapore
         Supervisor:     Prof Tat-Seng Chua
  • 2016-2021: PhD in Statistics, National University of Singapore
         Supervisor: Prof Zehua Chen
  • 2012-2016: Bachelor in Mathematics and Applied Mathematics, Southeast University
         Supervisor: Prof Jinde Cao & Prof Wenwu Yu