Artificial Intelligence Methods:

(1) Self-supervised representation learning methods

(2) Multi-modal information fusion methods

(3) Causal inference methods

AI for Science:

(1) Life Sciences: Design large-scale, multi-modal AI methods for single-cell omics, spatial omics, and multi-modal biological data, providing progressive analytical tools for life science research, serving studies on brain science, oncology, ageing, etc.

(2) Drug Science: Combine high-content image-based phenotypic screening platforms to design cross-cell-line, cross-imaging-platform, and cross-drug-library large-scale drug screening methods to accelerate small molecule drug discovery.

(3) Natural Sciences: Design AI methods for global long-term observational data to explore scientific value in meteorology, urban environments, and climate.

The laboratory is recruiting research assistants. Postdoctoral collaborations, direct-entry PhD, general PhD, Master’s applications, and undergraduate research practices are welcome. Current focus areas: spatial omics, oncology, and AI methods for natural sciences. Contact: fbao@fudan.edu.cn.

Editorial Board Member:

The Innovation Medicine

Editor:

PLOS Computational Biology (Guest Editor)

Peer Reviewer for journals including:

Cell

Nature Biotechnology

Nature Communications

Cell Systems

Genome Biology

IEEE Transactions on Fuzzy Systems

IEEE Transactions on Neural Networks and Learning Systems

Briefings in Bioinformatics

IEEE Journal of Selected Topics in Signal Processing

Xiaomi Young Scholar, 2025

National High-Level Young Talent, 2023

Shanghai High-Level Young Talent, 2023

Cell Press Most Popular Article in China, 2021

Germany DAAD AInet Fellowship, 2021

CICAI International Conference on Artificial Intelligence, Best Paper Finalist, 2021

IEEE CIS Transactions on Fuzzy Systems Outstanding Paper Award, 2020

World Artificial Intelligence Conference Outstanding Young Paper Award, 2020

Beijing Outstanding Doctoral Dissertation, 2019

Tsinghua University Outstanding Doctoral Dissertation, 2019

Fudan University / School of Information Science and Technology

Young Research Fellow, Nov. 2024 – present

University of California, San Francisco / Department of Pharmaceutical Chemistry

Postdoctoral Fellow, Nov. 2019 – Sept. 2024

Tsinghua University / Department of Automation

Ph.D. in Engineering, Sept. 2014 – Jul. 2019

Harvard University / Dana-Farber Cancer Research Centre / Department of Data Science

Visiting Scholar, Mar. 2018 – Jan. 2019

Xidian University / Electronic Information Engineering

B.Eng., Sept. 2010 – Jul. 2014

Spring 2025: Frontier Lectures (co-taught with Professor Chi Nan)

Autumn 2025: Fundamentals of Artificial Intelligence (AIB210002.07)

Representative papers (1st / corresponding author):

1. Transitive prediction of small molecule function through alignment of high-content screening resources.

Nature Biotechnology. 2025. doi: https://doi.org/10.1038/s41587-025-02729-2 .

2. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.

Nature Communications. 2024, 15(1): 6541.

3. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE.

Nature Biotechnology. 2022, 1-10.

4. Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning.

Patterns, Cell Press. 2020, 100057. ( Cover Article )

5. Scalable analysis of cell type composition from single-cell transcriptomics using deep recurrent learning.

Nature Methods. 2019, 16: 311–314.