Understand the principle behind complex systems

Logo

Fudan University

Bao Lab

Feng BAO 鲍峰

I am a Principal Investigator at the College for Future Information Technology, Fudan University, Shanghai. My research develops statistical learning and AI methods to answer fundamental biological questions and advance medicine — spanning self-supervised representation learning, multi-modal information fusion, causal inference in complex biological systems, and deep generative models for spatial omics.

We are looking for highly motivated PhD students and postdocs with backgrounds in machine learning, medicine, clinical research, or biology.

Previously, I was a Postdoc (2019–2024) at Altschuler and Wu Lab, University of California, San Francisco, and a visiting scholar at Dana-Farber Cancer Institute, Harvard Medical School (2018–2019). I obtained my PhD from Tsinghua University (2019).

Contact: fbao@fudan.edu.cn

Career Path

2024–present
Principal Investigator
College for Future Information Technology, Fudan University, Shanghai
2019–2024
Postdoctoral Researcher
Department of Pharmaceutical Chemistry, University of California, San Francisco
Advisors: Prof. Steven J. Altschuler and Prof. Lani F. Wu
2018–2019
Visiting Scholar
Department of Data Science, Dana-Farber Cancer Institute, Harvard Medical School
Advisors: Prof. Guo-Cheng Yuan and Prof. Long Cai
2014–2019
Ph.D. in Control Science and Engineering
Department of Automation, Tsinghua University
Advisor: Academician Qionghai Dai
2010–2014
B.Eng. in Electronic Engineering
School of Electronic Engineering, Xidian University

Awards & Honors

2025
Shanghai Science and Technology Leadership U35 Nomination
Xiaomi Young Scholar
2023
National High-level Overseas Young Talent
Shanghai High-level Overseas Young Talent
2021
Cell Press China Most Popular Article
Germany DAAD AInet Fellowship
CICAI International Conference on Artificial Intelligence, Best Paper Finalist
2020
IEEE CIS Transactions on Fuzzy Systems Outstanding Paper Award
World Artificial Intelligence Conference Outstanding Young Paper Award
2019
Beijing Outstanding Doctoral Dissertation
Tsinghua University Outstanding Doctoral Dissertation

Research Interests

  • AI Methods
    1. Self-supervised representation learning for biological data
    2. Multi-modal information fusion (transcriptomics, proteomics, spatial data, imaging)
    3. Causal inference in gene regulatory networks and drug perturbation experiments
  • Biomedical Applications
    1. Quantify interactions across multiple omics types
    2. Improve throughput of spatially resolved transcriptomics statistically
    3. Causal drug target discovery and transferable prediction of small-molecule function

Full publication list on Google Scholar

Selected Publications

  1. Molecular and cellular cartography of the laboratory mouse using whole-body sections.
    Cell. 2026. https://doi.org/10.1016/j.cell.2026.03.006

  2. Transitive prediction of small molecule function through alignment of high-content screening resources.
    Nature Biotechnology. 2025. https://doi.org/10.1038/s41587-025-02729-2

  3. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.
    Nature Communications. 2024, 15(1): 6541.

  4. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE.
    Nature Biotechnology. 2022, 1–10.

  5. Scalable analysis of cell type composition from single-cell transcriptomics using deep recurrent learning.
    Nature Methods. 2019, 16: 311–314.

Academic Service

Reviewer for:

  • Cell · Nature Biotechnology · Nature Biomedical Engineering · Nature Communications
  • Cell Systems · Genome Biology · Briefings in Bioinformatics
  • IEEE Transactions on Neural Networks and Learning Systems · IEEE Transactions on Fuzzy Systems
  • IEEE Journal of Selected Topics in Signal Processing

Last update: April 2026