数学科学研究所
Insitute of Mathematical Science

曾鹏程 Pengcheng Zeng


Career

  2021.12-present,  Assistant Professor, ShanghaiTech University

  2019.9-2021.11, Postdoctoral Fellow, The Chinese University of Hong Kong

 • 2018.5-2019.8,  Data Scientist/Senior Manager, Headquarters of Country Garden Group


Education

July 2018, Ph.D. in Statistics, Newcastle University, United Kingdom

July 2014, M.S. in Probability and Statistics, University of Science and Technology of China

July 2011, B.S. in Mathematics, Hefei University ofTechnology


  

Research Interest

My research area includes Statistics, Data Science and Bioinformatics. I am also interested in Machine Learning and Deep Learning. The focus of my current research work is on developing statistical methods and computational tools for analyzing cutting-edge large-scale genomic data and healthcare data.

  

Selected Publications

[1] Zeng, P.# and Lin, Z. (2023) scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI : 10.1109/TCBB.2023.3305989. (#:corresponding author)

[2] Zeng, P. and Lin, Z. (2023) scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data. Bioinformatics, 39(1), btac739.

[3] Ma, Y., Sun, Z., Zeng, P., Zhang, W. and Lin, Z. (2022) JSNMF enables effective and accurate integrative analysis of single-cell multi-omics data. Briefings in Bioinformatics. 23(3):bbac105. 

[4] Tang, L.*, Zeng, P.*, Shi, J.Q. and Kim, W-S. (2022) Model-based joint curve registration and classification. Journal of Applied Statistics. https://doi.org/10.1080/02664763.2021.2 023118.  (*:co-first authors)

[5] Zeng, P. and Lin, Z. (2021) coupleCoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data. PLoS Computational Biology. 17(6): e1009064.

[6] Zeng, P., Wangwu, J. and Lin, Z. (2021) Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data. Briefings in Bioinformatics. 22(4), bbaa347.

[7] Zeng, P. and Lin, Z. (2020) Elastic Coupled Co-clustering for Single-Cell Genomics Data. Preprint. https://arxiv.org/abs/2003.12970.

[8] Zeng, P., Shi, J.Q. and Kim, W-S. (2019) Simultaneous Registration and Clustering for Multi-dimensional Functional Data. Journal of Computational and Graphical Statistics. 4(28), 943-953.

[9] Kim, W-S.*, Zeng, P.*, Shi, J.Q., Lee, Y. and Paik, N-J. (2017) Semi-automatic Tracking, Smoothing and Segmentation of Hyoid Bone Motion from Videofluoroscopic Swallowing Study. PLoS ONE. 12(11): e0188684. (*: co-first authors)














Email:

 zengpch@shanghaitech.edu.cn

Office: S512, School of Creativity & Arts




地址:上海市浦东新区华夏中路393号
邮编:201210
上海市徐汇区岳阳路319号8号楼
200031(岳阳路校区)