Jicong Fan

Jicong Fan

Assistant Professor, CUHK-Shenzhen

About Me

樊继聪老师现为香港中文大学(深圳)数据科学学院助理教授、博士生导师、校长青年学者。在此之前，他是康奈尔大学的博士后(advisor:Madeleine Udell)。他在香港城市大学电子工程系取得博士学位(导师:Tommy W.S. Chow)，在北京化工大学获得控制科学与工程硕士学位(导师:王友清)和自动化学士学位。他曾是美国威斯康星大学麦迪逊分校访问学生和香港大学研究助理。他的研究方向是人工智能和机器学习，他在矩阵/张量方法、聚类算法、图学习、异常检测和推荐系统等方面做了大量研究工作，在知名学术期刊和国际会议上，如IEEE TSP/TNNLS/TII、NeurIPS、ICLR、ICML、CVPR、KDD、AAAI，发表论文60余篇。 他是IEEE高级会员，目前担任国际期刊《Pattern Recognition》（中科院一区）、《Neural Processing Letters》、《Transactions on Machine Learning Research》的副编辑/执行编辑，担任国际人工智能/机器学习会议ICML、NeurIPS和ICLR领域主席，主持国家自然科学基金青年项目、面上项目、广东省面上项目等，获得2023年中国自动化学会自然科学奖一等奖，入选斯坦福大学/爱思唯尔2023、2024、2025(年度/终身)“全球Top 2% 科学家”榜单。

樊老师课题组目前主要研究以下问题：1)图机器学习; 2)异常检测; 3)自动机器学习; 4)人工智能在生命科学、化学和化工中的应用; 5)基于大数据的健康监测和疾病诊断系统。

樊老师课题组目前招收博士生、博士后、研究助理，感兴趣的同学请发送简历及研究计划(如有)到邮箱。一些要求如下：

  • 博士生 为人诚实守信，工作认真负责，优先考虑满足以下任一条件的同学（本科或硕士）：1)本科毕业于985高校且成绩专业排名不低于前百分之二十；2)本科成绩专业排名前百分之五；3)以第一作者身份发表过CCFA/B类论文或者电子工程/自动化等领域同等水平论文（如果是硕士毕业，该条件必须满足)。本课题组为所有博士生提供全额奖学金和生活补贴。
  • 博士后 为人诚实守信，工作认真负责，即将或者已经获得博士学位，以第一作者身份发表过两篇CCFA/B类论文或者电子工程/自动化等领域同等水平论文。待遇面议。
  • 研究助理 为人诚实守信，工作认真负责，本硕在读或者已毕业均可，优先考虑满足以下任一条件的同学：1)本科毕业于985高校且成绩专业排名不低于前百分之三十；2)本科成绩专业排名前百分之十；3)以第一作者身份发表过CCFA/B类论文或者电子工程/自动化等领域同等水平论文。

I am an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Before joining CUHK-Shenzhen, I was a Postdoctoral Associate (advisor: Madeleine Udell) at the School of Operations Research and Information Engineering, Cornell University, Ithaca, USA. I completed my PhD at City University of Hong Kong in Electronic Engineering under the supervision of Prof. Tommy W.S. Chow. During my PhD, I was a visiting scholar at the Department of Electrical and Computer Engineering, University of Wisconsin-Madison, USA. I obtained my Bachelor’s (Automation) and Master’s (Control Science and Engineering, supervisor: Youqing Wang) degrees from Beijing University of Chemical Technology. After that, I was a Research Assistant at The University of Hong Kong. My research focuses on artificial intelligence and machine learning, with extensive work in matrix/tensor methods, clustering algorithms, graph learning, anomaly detection, and recommender systems. I have published over 60 papers in prestigious academic journals and international conferences, including IEEE TSP, TNNLS, TII, NeurIPS, ICLR, ICML, CVPR, KDD, and AAAI. I’m a Senior Member of IEEE and currently serve as an Associate/Action Editor for international journals such as Pattern Recognition, Neural Processing Letters, and Transactions on Machine Learning Research. I also act as an Area Chair for international AI/machine learning conferences including ICML, NeurIPS, and ICLR. I have led research projects funded by the National Natural Science Foundation of China (Young Scientists Fund and General Program) and the Guangdong Provincial Natural Science Foundation (General Program). I was awarded the First Prize of the Natural Science Award by the Chinese Association of Automation in 2023.

I am looking for PhD students, Postdocs, and Research Assistants. If you are interested in these positions, please send me your CV/resume. For PhD or Postdoc, please also send me your research plan.

Preprints

  • Qi Feng, Jicong Fan*. Training A Foundation Model to Represent Graphs as Vectors. arXiv 2026. [PDF]
  • Jicong Fan. An Interdisciplinary and Cross-Task Review on Missing Data Imputation. arXiv 2025. [PDF]
  • Feng Xiao, Jicong Fan*. Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding. arXiv 2025. [PDF]
  • Dong Qiao, Jicong Fan*. Mutual Regressin Distance. arXiv 2025. [PDF]
  • Zixiao Wang, Dong Qiao, Jicong Fan*. Spectral Clustering for Discrete Distributions. arXiv 2024. [PDF]

Selected Publications (* indicates corresponding author)

  • Dazhi Fu, Jicong Fan*. UniOD: A Universal Model for Outlier Detection across Diverse Domains. ICLR 2026. [PDF]
  • Wei Dai, Jicong Fan*. AutoDV: An End-to-End Deep Learning Model for High-Dimensional Data Visualization. ICLR 2026. [PDF]
  • Chao Ouyang, Haijun Zhang*, Jicong Fan*. BOGK: Bayesian Optimization-Driven Graph Kernel Ensemble for Graph-level Clustering. IEEE TKDE 2026.
  • Feng Xiao, Xiaoying Tang, Jicong Fan*. Fairness-aware Anomaly Detection via Fair Projection. NeurIPS 2025. [PDF]
  • Feng Xiao, Youqing Wang, S Joe Qin, Jicong Fan*. Semi-Supervised Anomaly Detection Using Restricted Distribution Transformation. IEEE TNNLS 2025. [PDF]
  • Jinyu Cai, Yunhe Zhang, Jicong Fan*. Self-Discriminative Modeling for Anomalous Graph Detection. ICML 2025. [PDF]
  • Jicong Fan. Graph Minimum Factorization Distance and Its Application to Large-Scale Graph Data Clustering. ICML 2025. [PDF]
  • Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan*. Explainable Graph Representation Learning via Graph Pattern Analysis. IJCAI 2025.
  • Wei Dai, Jicong Fan*. AutoUAD: Hyper-parameter Optimization for Unsupervised Anomaly Detection. ICLR 2025. [PDF]
  • Liangqi Xie, Jicong Fan*. Multi-Subspace Matrix Recovery from Permuted Data. AAAI 2025. [PDF]
  • Tongle Wu, Ying Sun*, Jicong Fan*. Non-Convex Tensor Recovery from Local Measurements. AAAI 2025. [PDF]
  • Dong Qiao, Xinxian Ma, Jicong Fan*. Federated t-SNE and UMAP for Distributed Data Visualization. AAAI 2025. [PDF]
  • Wei Dai, Kai Hwang, Jicong Fan*. Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation. AAAI 2025. [PDF]
  • Tongle Wu, Jicong Fan*. Smooth Tensor Product for Tensor Completion. IEEE Transactions on Image Processing, 2024. [PDF]
  • Zixiao Wang, Jicong Fan*. Graph Classification via Reference Distribution Learning: Theory and Practice. NeurIPS 2024. [PDF]
  • Feng Xiao, Jicong Fan*. Unsupervised Anomaly Detection in The Presence of Missing Values. NeurIPS 2024. [PDF]
  • Ziheng Sun, Xudong Wang, Chris Ding, Jicong Fan*. Learning Graph Representation via Graph Entropy Maximization. ICML 2024. [PDF]
  • Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan*. Deep Orthogonal Hypersphere Compression for Anomaly Detection. ICLR 2024. (Spotlight, acceptance rate=5%) [PDF]
  • Yan Sun, Jicong Fan*. MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy. ICLR 2024. (Spotlight, acceptance rate=5%) [PDF]
  • Jicong Fan, Rui Chen, Zhao Zhang, Chris Ding. Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice. ICLR 2024. [PDF]
  • Dazhi Fu, Zhao Zhang, Jicong Fan*. Dense Projection for Anomaly Detection. AAAI 2024. [PDF]
  • Ziheng Sun, Chris Ding, Jicong Fan*. Lovász Principle for Unsupervised Graph Representation Learning. NeurIPS 2023. [PDF]
  • Zhihao Wu, Zhao Zhang, Jicong Fan*. Graph Convolutional Kernel Machine versus Graph Convolutional Networks. NeurIPS 2023. [PDF]
  • Dong Qiao, Chris Ding, Jicong Fan*. Federated Spectral Clustering via Secure Similarity Reconstruction. NeurIPS 2023. [PDF]
  • Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell. Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. Transactions on Machine Learning Research 2023. [PDF]
  • Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang. A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. [PDF]
  • Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022. [PDF]
  • Jinyu Cai, Jicong Fan*, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang. Efficient Deep Embedded Subspace Clustering. CVPR 2022. [PDF]
  • Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. [PDF]
  • Jicong Fan. Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation. AAAI 2022. (acceptance rate=15%) [PDF]
  • Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%) [PDF]
  • Jicong Fan*, Tommy WS Chow, S Joe Qin. Kernel Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data. IEEE TII 2022. [PDF]
  • Jicong Fan*, Chengrun Yang, Madeleine Udell. Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE TSP 2021. [PDF]
  • Jicong Fan, Yuqian Zhang, Madeleine Udell. Polynomial matrix completion for missing data imputation and transductive learning. AAAI 2020. (Oral, acceptance rate=6%) [PDF]
  • Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell. Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%) [PDF]
  • Jicong Fan, Tommy W.S. Chow. Exactly robust kernel principal component analysis. IEEE TNNLS 2020. [PDF]
  • Jicong Fan, Madeleine Udell. Online high-rank matrix completion. CVPR 2019 (Oral, acceptance rate=5.6%). [PDF]