• I am a Professor in the School of Artificial Intelligence and Data Science at the University of Science and Technology of China. My research focuses on large-scale data mining, trustworthy and secure AI & systems, with an emphasis on machine learning techniques and real-world applications, including graph mining and learning, ML privacy, foundation models, FinTech, and learning systems. My work has been published in top conferences (e.g., ICML, NeurIPS, ICDE, WWW, ACM MM) and journals (e.g., TKDE, TKDD, TOSEM). Additionally, I serve as a program committee member for leading international conferences such as ICML, CVPR, WWW, SIGKDD, NeurIPS, and IJCAI, and as a reviewer for prestigious journals including TKDE, TDSC, TKDD, KAIS, and TNNLS.

• I earned my Ph.D. in Computer Science from the Institute of Computing Technology, Chinese Academic of Sciences, under the supervision of Prof. Xueqi Cheng and in collaboration with Prof. Shenghua Liu. I received my B.Eng in Computer Science and Technology at Beijing Jiao Tong University. Previously, I was a research fellow at the Institute of Data Science (IDS), National University of Singapore, working with Prof. Bryan Hooi and Prof. See-Kiong NG.

• [Curriculum Vitae] and [Google Scholar]

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1. General Hiring Requirements:
   • Passion, determination, and preservation to conduct high-quality research.
   • Good communication and collaboration skills.
   • Strong coding ability (C/C++ or Python).
2. Hiring tenure-track faculties and postdocs in DM/ML:
   • With PhD degree (or graduate soon)
   • At least three first-author papers on top-tier conferences
3. Hiring PhD students from USTC and masters:
   • English (CET-6 score 500+, or equal levels)
   • Strong mathematics foundations (calculus, linear algebra, optimization, information theory, probability and statistics, etc.)
4. Hiring master students and undergraduate interns:
   • Good foundation in mathematics (calculus, linear algebra, probability and statistics)
   • Experience in high-level competitions (e.g., ACM-ICPC, KDD-Cup, ) or top-tier publications will be preferred.

I am looking for self-motivated undergraduate interns, master, and Ph.D. students with solid mathematical backgrounds and coding skills. Feel free to drop me an email with your CV if interested. I am also open to discussions and collaborations on the topics mentioned above!

What’s New

• Our paper “Interrelated Dense Subgraph Detection in Multilayer Networks” is accepted to ICDE 2025
• Our paper “ID3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition” is accepted to NeurIPS 2024
• Our paper “One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity” is accepted to ACM MM 2024
• Our paper “Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks” is accepted to NAACL 2024
• Our paper “Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering” is accepted to ICML 2023
• Our paper “Unified Dense Subgraph Detection: Fast Spectral Theory based Algorithms” is accepted to IEEE TKDE.
• March 2023, Our demo & poster paper, “EasySpider: Visual Code-Free Web Crawler/Spider” is accepted to The WebConf 2023. Welcome to try our user-friendly tool EasySpider, which also contains detailed instructions for usage.
• Our paper “Hierarchical Dense Pattern Detection in Tensors” is accepted to TKDD.

Experiences

• Professor, University of Science and Technology of China, Feb. 2025 - Present
• Postdoc Research Fellow, National University of Singapore, Oct. 2020 - Jan. 2025.

Last updated on March 2025.