POSITION/TITLE
Research Scientist of DFR
Assistant Professor of CUHKSZ
RESEARCH FIELD
Machine Learning, Computer Vision, Optimization, Statistical Process Control, Neural Signal Processing
fanjicong@cuhk.edu.cn
EDUCATION BACKGROUND
Ph.D. Electronic Engineering, City University of Hong Kong, 2013-2018
M.S. Control Science and Engineering, Beijing University of Chemical Technology, 2010-2013
B.S. Automation, Beijing University of Chemical Technology, 2006-2010
PERSONAL INTRODUCTION
Professor Jicong Fan is a Research Scientise of SRIBD, 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, ICLR, and AAAI. He is a senior member of IEEE and is serving as an associate editor for the journal Neural Processing Letters.
ACADEMIC PUBLICATIONS
[1] 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. 2023.01.
[2] Jicong Fan, Yiheng Tu, Zhao Zhang, Mingbo Zhao, Haijun Zhang. A Simple Approach to Automated Spectral Clustering. NeurIPS 2022. (acceptance rate=25.6%)
[3] Jinyu Cai, Jicong Fan*. Perturbation Learning Based Anomaly Detection. NeurIPS 2022.
[4] Jicong Fan. Multi-Mode Deep Matrix and Tensor Factorization. ICLR 2022. (acceptance rate=32.3%)
[5] Jicong Fan. Large-Scale Subspace Clustering via k-Factorization. KDD 2021. (acceptance rate=15.4%)
[6] 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.
[7] Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell. Factor group sparse regularization for efficient low-rank matrix recovery. NeurIPS 2019. (acceptance rate=21.1%)
[8] Jicong Fan, Madeleine Udell. Online high-rank matrix completion. CVPR 2019. Oral Presentation. (acceptance rate=5.6%)
POSITION
Research Scientist, Division of Fundamental Research, Shenzhen Research Institute of Big Data
RESEARCH INTEREST
High-Dimensional Statistics, Dimensionality Reduction, Statistical Learning, Applied Random Matrix Theory
statzyc@sribd.cn
EDUCATION BACKGROUND
Hong Kong Baptist University, PhD in Statistics
Zhejiang University, M.S. in Statistics
Tianjin University, B.S. in Math
BIOGRAPHY
Dr. Yicheng Zeng is currently a research scientist in Shenzhen Research Institute of Big Data. Before that, he was a postdoctoral fellow in Statistics at the Department of Statistical Sciences, University of Toronto, from 2019 to 2022, hosted by Prof. Qiang Sun and Prof. Yuying Xie. He earned his Ph.D. in Statistics from Hong Kong Baptist University in 2019, under the supervision of Prof. Lixing Zhu and Prof. Heng Peng. Prior to that, he received his B.S. from Tianjin University in 2014, and his M.S. from Zhejiang University in 2016, where he was supervised by Prof. Zhonggen Su. His research interests include high-dimensional statistics, statistical learning, statistical applications of random matrix theory, dimensionality reduction, and order determination, etc. He has published several peer-reviewed papers on statistical journals and machine learning conferences including Statistica Sinica, JMVA, CSDA, ICML and UAI. He also serves as an anonymous reviewer for statistics journals and conferences, including Biometrics, Communications in Statistics - Theory and Methods, AISTATS (2022&2023). Dr. Zeng is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up) from Shenzhen government.
SELECTED PUBLICATIONS
# denotes co-first authors，*denotes corresponding author
[1] Xin Chen#, Yicheng Zeng#, Siyue Yang and Qiang Sun*. (2023) Sketched ridgeless linear regres- sion: the role of downsampling. The 40th International Conference on Machine Learning (ICML). Accepted.
[2] Fangchen Yu, Yicheng Zeng, Jianfeng Mao and Wenye Li*. (2023) Online estimation of similarity matrices with incomplete data. The 39th Conference on Uncertainty in Artificial Intelligence (UAI). Accepted.
[3] Yicheng Zeng and Lixing Zhu*. (2023) Order determination for spiked type models with a divergent number of spikes. Computational Statistics & Data Analysis, 182, 107704.
[4] Yicheng Zeng and Lixing Zhu*. (2022) Order determination for spiked type models. Statistica Sinica, 32, 1633-1659.
[5] Junshan Xie#, Yicheng Zeng# and Lixing Zhu*. (2021) Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes. Journal of Multivariate Analysis, 184: 104742.
POSITION
Research Scientist, Division of Fundamental Research, Shenzhen Research Institute of Big Data
RESEARCH INTEREST
High-Dimensional Statistics, Dimensionality Reduction, Statistical Learning, Applied Random Matrix Theory
statzyc@sribd.cn
EDUCATION BACKGROUND
Hong Kong Baptist University, PhD in Statistics
Zhejiang University, M.S. in Statistics
Tianjin University, B.S. in Math
BIOGRAPHY
Dr. Yicheng Zeng is currently a research scientist in Shenzhen Research Institute of Big Data. Before that, he was a postdoctoral fellow in Statistics at the Department of Statistical Sciences, University of Toronto, from 2019 to 2022, hosted by Prof. Qiang Sun and Prof. Yuying Xie. He earned his Ph.D. in Statistics from Hong Kong Baptist University in 2019, under the supervision of Prof. Lixing Zhu and Prof. Heng Peng. Prior to that, he received his B.S. from Tianjin University in 2014, and his M.S. from Zhejiang University in 2016, where he was supervised by Prof. Zhonggen Su. His research interests include high-dimensional statistics, statistical learning, statistical applications of random matrix theory, dimensionality reduction, and order determination, etc. He has published several peer-reviewed papers on statistical journals and machine learning conferences including Statistica Sinica, JMVA, CSDA, ICML and UAI. He also serves as an anonymous reviewer for statistics journals and conferences, including Biometrics, Communications in Statistics - Theory and Methods, AISTATS (2022&2023). Dr. Zeng is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up) from Shenzhen government.
SELECTED PUBLICATIONS
# denotes co-first authors，*denotes corresponding author
[1] Xin Chen#, Yicheng Zeng#, Siyue Yang and Qiang Sun*. (2023) Sketched ridgeless linear regres- sion: the role of downsampling. The 40th International Conference on Machine Learning (ICML). Accepted.
[2] Fangchen Yu, Yicheng Zeng, Jianfeng Mao and Wenye Li*. (2023) Online estimation of similarity matrices with incomplete data. The 39th Conference on Uncertainty in Artificial Intelligence (UAI). Accepted.
[3] Yicheng Zeng and Lixing Zhu*. (2023) Order determination for spiked type models with a divergent number of spikes. Computational Statistics & Data Analysis, 182, 107704.
[4] Yicheng Zeng and Lixing Zhu*. (2022) Order determination for spiked type models. Statistica Sinica, 32, 1633-1659.
[5] Junshan Xie#, Yicheng Zeng# and Lixing Zhu*. (2021) Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes. Journal of Multivariate Analysis, 184: 104742.
POSITION
Research Scientist, Division of Fundamental Research, Shenzhen Research Institute of Big Data
RESEARCH INTEREST
High-Dimensional Statistics, Dimensionality Reduction, Statistical Learning, Applied Random Matrix Theory
statzyc@sribd.cn
EDUCATION BACKGROUND
Hong Kong Baptist University, PhD in Statistics
Zhejiang University, M.S. in Statistics
Tianjin University, B.S. in Math
BIOGRAPHY
Dr. Yicheng Zeng is currently a research scientist in Shenzhen Research Institute of Big Data. Before that, he was a postdoctoral fellow in Statistics at the Department of Statistical Sciences, University of Toronto, from 2019 to 2022, hosted by Prof. Qiang Sun and Prof. Yuying Xie. He earned his Ph.D. in Statistics from Hong Kong Baptist University in 2019, under the supervision of Prof. Lixing Zhu and Prof. Heng Peng. Prior to that, he received his B.S. from Tianjin University in 2014, and his M.S. from Zhejiang University in 2016, where he was supervised by Prof. Zhonggen Su. His research interests include high-dimensional statistics, statistical learning, statistical applications of random matrix theory, dimensionality reduction, and order determination, etc. He has published several peer-reviewed papers on statistical journals and machine learning conferences including Statistica Sinica, JMVA, CSDA, ICML and UAI. He also serves as an anonymous reviewer for statistics journals and conferences, including Biometrics, Communications in Statistics - Theory and Methods, AISTATS (2022&2023). Dr. Zeng is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up) from Shenzhen government.
SELECTED PUBLICATIONS
# denotes co-first authors，*denotes corresponding author
[1] Xin Chen#, Yicheng Zeng#, Siyue Yang and Qiang Sun*. (2023) Sketched ridgeless linear regres- sion: the role of downsampling. The 40th International Conference on Machine Learning (ICML). Accepted.
[2] Fangchen Yu, Yicheng Zeng, Jianfeng Mao and Wenye Li*. (2023) Online estimation of similarity matrices with incomplete data. The 39th Conference on Uncertainty in Artificial Intelligence (UAI). Accepted.
[3] Yicheng Zeng and Lixing Zhu*. (2023) Order determination for spiked type models with a divergent number of spikes. Computational Statistics & Data Analysis, 182, 107704.
[4] Yicheng Zeng and Lixing Zhu*. (2022) Order determination for spiked type models. Statistica Sinica, 32, 1633-1659.
[5] Junshan Xie#, Yicheng Zeng# and Lixing Zhu*. (2021) Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes. Journal of Multivariate Analysis, 184: 104742.
POSITION
Research Scientist, Division of Fundamental Research, Shenzhen Research Institute of Big Data
RESEARCH INTEREST
High-Dimensional Statistics, Dimensionality Reduction, Statistical Learning, Applied Random Matrix Theory
statzyc@sribd.cn
EDUCATION BACKGROUND
Hong Kong Baptist University, PhD in Statistics
Zhejiang University, M.S. in Statistics
Tianjin University, B.S. in Math
BIOGRAPHY
Dr. Yicheng Zeng is currently a research scientist in Shenzhen Research Institute of Big Data. Before that, he was a postdoctoral fellow in Statistics at the Department of Statistical Sciences, University of Toronto, from 2019 to 2022, hosted by Prof. Qiang Sun and Prof. Yuying Xie. He earned his Ph.D. in Statistics from Hong Kong Baptist University in 2019, under the supervision of Prof. Lixing Zhu and Prof. Heng Peng. Prior to that, he received his B.S. from Tianjin University in 2014, and his M.S. from Zhejiang University in 2016, where he was supervised by Prof. Zhonggen Su. His research interests include high-dimensional statistics, statistical learning, statistical applications of random matrix theory, dimensionality reduction, and order determination, etc. He has published several peer-reviewed papers on statistical journals and machine learning conferences including Statistica Sinica, JMVA, CSDA, ICML and UAI. He also serves as an anonymous reviewer for statistics journals and conferences, including Biometrics, Communications in Statistics - Theory and Methods, AISTATS (2022&2023). Dr. Zeng is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up) from Shenzhen government.
SELECTED PUBLICATIONS
# denotes co-first authors，*denotes corresponding author
[1] Xin Chen#, Yicheng Zeng#, Siyue Yang and Qiang Sun*. (2023) Sketched ridgeless linear regres- sion: the role of downsampling. The 40th International Conference on Machine Learning (ICML). Accepted.
[2] Fangchen Yu, Yicheng Zeng, Jianfeng Mao and Wenye Li*. (2023) Online estimation of similarity matrices with incomplete data. The 39th Conference on Uncertainty in Artificial Intelligence (UAI). Accepted.
[3] Yicheng Zeng and Lixing Zhu*. (2023) Order determination for spiked type models with a divergent number of spikes. Computational Statistics & Data Analysis, 182, 107704.
[4] Yicheng Zeng and Lixing Zhu*. (2022) Order determination for spiked type models. Statistica Sinica, 32, 1633-1659.
[5] Junshan Xie#, Yicheng Zeng# and Lixing Zhu*. (2021) Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes. Journal of Multivariate Analysis, 184: 104742.
