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 of Technology
Research Interest Our research group develops machine learning and statistical methods grounded in theory and interpretability to tackle cutting-edge challenges in biomedical big data, including but not limited to genomics and clinical data. Our current focus areas are: (a) AI and statistics, with an emphasis on explainable AI and uncertainty quantification; (b) Statistical genomics, encompassing single-cell multi-omics and spatial transcriptomics; (c) Functional data analysis, spanning methodology, theory, and applications; (d) Data science, with a focus on interdisciplinary collaboration across diverse fields.
Selected Publications Group members (current and past) are in bold; + : co-first authors; # : corresponding authors
• Siyuan Jiang+, Yihan Hu+, Wenjie Li and Pengcheng Zeng#. DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification. International Conference on Learning Representations (ICLR), 2026. • Tao Ding and Pengcheng Zeng#. GALA: A Unified Landmark-Free Framework for Coarse-to-Fine Spatial Alignment Across Resolutions and Modalities in Spatial Transcriptomics. Briefings in Bioinformatics(Accepted), 2026. • Hongyao Li+, Yunrui Liu+ and Pengcheng Zeng#. Guided Co-clustering Transfer Across Unpaired and Paired Single-cell Multi-omics Data. Bioinformatics; 41(12):btaf639, 2025. • Pengcheng Zeng and Zhixiang Lin#. scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data. Bioinformatics; 39(1):btac739, 2023. • Pengcheng Zeng, Jiaxuan Wangwu and Zhixiang Lin#. Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data. Briefings in Bioinformatics; 22(4):bbaa34, 2021. • Pengcheng Zeng, Jian Qing Shi# and Won-Seok Kim. Simultaneous registration and clustering for multi-dimensional functional data. Journal of Computational and Graphical Statistics; 4(28):943-953, 2019.
For an updated and complete list of publications and opportunities (including postdoctoral fellow and research assistant positions), please visit https://pengchengzeng.github.io. |
Email: Office: S512, School of Creativity & Arts |
