Ph.D. Electronic Engineering, City University of Hong Kong, 2015-2018
M.S. Control Science and Engineering, Beijing University of Chemical Technology, 2010-2013
B.S. Automation, Beijing University of Chemical Technology, 2006-2010

Jicong Fan is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. Professor Fan previously obtained his Ph.D. from the Department of Electronic Engineering, City University of Hong Kong in 2018, and his Master’s degree in Control Science and Engineering and Bachelor’s degree in Automation from Beijing University of Chemical Technology in 2013 and 2010 respectively. Prior to joining CUHK-Shenzhen, he was a postdoc associate at Cornell University. He held research positions at The University of Wisconsin-Madison and The University of Hong Kong in 2018 and 2015 respectively.

Professor Fan's research interests are Artificial Intelligence and Machine Learning. Particularly, he has done a lot of work on matrix/tensor methods, clustering algorithms, anomaly/outlier/fault detection, deep learning, and recommendation system. His research has been published on prestigious journals and conferences such as IEEE TSP/TNNLS/TII, KDD, NeurIPS, CVPR, ICML, ICLR, AAAI, and IJCAI. He is a senior member of IEEE and is serving as an associate editor for two international journals including Pattern Recognition and Neural Processing Letters. He won the first prize of the Natural Science Award of Chinese Association of Automation in 2023.

Professor Fan is looking for PhD student, Postdoc, research assistant, and visiting student. Please contact via email: fanjicong[AT]cuhk[DOT]edu[DOT]cn

Selected publications:

Zixiao Wang, Jicong Fan*. Graph Classification via Reference Distribution Learning: Theory and Practice. NeurIPS 2024

Feng Xiao, Jicong Fan*. Unsupervised Anomaly Detection in The Presence of Missing Values. NeurIPS 2024.

Ziheng Sun, Xudong Wang, Chris Ding, Jicong Fan*. Learning Graph Representation via Graph Entropy Maximization. ICML 2024.

Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan*. Deep Orthogonal Hypersphere Compression for Anomaly Detection. ICLR 2024 (Spotlight, acceptance rate=5%).

Yan Sun, Jicong Fan*. MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy. ICLR 2024 (Spotlight, acceptance rate=5%).

Jicong Fan, Rui Chen, Zhao Zhang, Chris Ding. Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice. ICLR 2024.

Ziheng Sun, Chris Ding, Jicong Fan*. Lovász Principle for Unsupervised Graph Representation Learning. NeurIPS 2023. (acceptance rate=26%)

Zhihao Wu, Zhao Zhang, Jicong Fan*. Graph Convolutional Kernel Machine versus Graph Convolutional Networks. NeurIPS 2023. (acceptance rate=26%)

Dong Qiao, Chris Ding, Jicong Fan*. Federated Spectral Clustering via Secure Similarity Reconstruction. NeurIPS 2023. (acceptance rate=26%)

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. Accepted by Transactions on Machine Learning Research. 2023.

Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang.  A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. (acceptance rate=25.6%)

Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022. (acceptance rate=25.6%)

Jinyu Cai, Jicong Fan*, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang.  Efficient Deep Embedded Subspace Clustering. CVPR 2022. (acceptance rate=24%)

Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. (acceptance rate=32.3%)

Jicong Fan. Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation. AAAI 2022. (acceptance rate=15%)

Jicong Fan, Tommy W.S. Chow,  S.  Joe Qin.  Kernel Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data.  IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4477-4487, July 2022.

Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%)

Jicong Fan, Chengrun Yang, Madeleine Udell. Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE Transactions on Signal Processing, 2021(69): 1755-1770.

Jicong Fan, Yuqian Zhang, Madeleine Udell (2019). Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning. AAAI 2020. (Oral, acceptance rate=6%).

Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell (2019). Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%)

Jicong Fan, Madeleine Udell (2019). Online high-rank matrix completion. CVPR 2019. (Oral, acceptance rate=5.6%).

Jicong Fan, Tommy W.S. Chow (2019). Exactly robust kernel principal component analysis. IEEE Transactions on Neural Networks and Learning System 31 (3), 749-761.