Feng Bao is a Research Fellow and doctoral supervisor at the College of Future Information Technology, Fudan University. He received his B.Eng. in Electronic and Information Engineering from Xidian University in 2014 and his Ph.D. in Control Science and Engineering from Tsinghua University in 2019. He was a postdoctoral scholar at the University of California, San Francisco from 2019 to 2024, and joined Fudan University in November 2024.
His research lies at the intersection of artificial intelligence and the life sciences, with a focus on computational methods for spatial and single-cell omics, multimodal representation learning and deep generative models, and the integrative analysis of high-content screening data. He has developed computational methods including MUSE, CLIPn and scScope, and has published as first or corresponding author (including co-first and co-corresponding) in Cell, Nature Biotechnology, Nature Methods and Nature Communications.
He is the principal investigator of a General Program grant from the National Natural Science Foundation of China on cross-scale 3D reconstruction methods for spatial omics, and of a Key Technology R&D project from the Science and Technology Commission of Shanghai Municipality on contrastive integration algorithms for multimodal spatial omics and their application to the spatiotemporal evolution of tumors. He was selected for the Shanghai Science and Technology Youth 35 Under 35 Leading Program (2025) and as a Xiaomi Young Scholar (2025), and is a recipient of the IEEE CIS TFS Outstanding Paper Award, the Youth Outstanding Paper Award of the World Artificial Intelligence Conference, and the Cell Press Most Picked Paper Award.
Selected Publications
1. Clevenger MH, Cipurko D, Patil A, Li B, Takahama M, Mei L, Plaster M, Richey G, Kawamoto T, Bao F, Chevrier N. Whole-body molecular and cellular mapping of the laboratory mouse. Cell, 2026, 189(11): 3481-3498.e24 (co-corresponding author)
2. Bao F, Li L, Hammerlindl H, Shen SQ, Hammerlindl S, Altschuler SJ, Wu LF. Transitive prediction of small-molecule function through alignment of high-content screening resources. Nature Biotechnology, 2026, 44(5): 862-872 (first author)
3. Bao F, Deng Y, Wan S, Shen SQ, Wang B, Dai Q, Altschuler SJ, Wu LF. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature Biotechnology, 2022, 40(8): 1200-1209 (co-first author)
4. Li B, Bao F, Hou Y, Li F, Li H, Deng Y, Dai Q. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model. Nature Communications, 2024, 15(1) (co-first author)
5. Deng Y, Bao F, Dai Q, Wu LF, Altschuler SJ. Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nature Methods, 2019, 16(4): 311-314 (co-first author)