ZENG, Yicheng
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.
