Bo Li 李波 — AI for Biology: measuring, modeling, and designing cellular perturbations
I develop computational methods for understanding and controlling cellular responses. My research asks what biological distinctions experiments can reliably resolve, how multimodal virtual-cell models can predict perturbation responses, and how desired cellular states can be translated into effective interventions.
I am a Ph.D. student in the Department of Artificial Intelligence, University of Macau, advised by Prof. Bob Zhang and co-advised by Prof. Qianqian Song. Since June 2026, I have been a visiting student at the School of Computing, National University of Singapore, hosted by Prof. Yang Zhang.
📫 Contact: Boom985426@gmail.com · WeChat: BoomLi5426
🔬 Research
What can an experiment actually resolve?
Quantify perturbation detectability, identifiability, reproducibility, and the measurement resolution available to downstream predictive models.
How do cells respond across perturbations and modalities?
Learn and evaluate cellular representations across transcriptomics, morphology, molecular structure, and biological context.
Which intervention can move a cell toward a desired state?
Move beyond forward prediction toward inverse intervention design: given an initial and desired cellular state, identify effective perturbations.
📝 Selected Publications
Measurement resolution constrains fine-grained perturbation prediction
Bo Li, Chengyang Zhang, Mengran Li, Bob Zhang, Lin Wang, Zhenchao Tang, Jun Liu, Chengliang Liu, Chen Wei, Yuhao Yi, Jiancheng Lv, Yang Zhang
Manuscript 2026 · Project · Manuscript · Code · Data
TL;DR: Separates what biological distinctions experiments can reproducibly resolve from what perturbation-prediction models can learn.
MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes
Bo Li, Qing Wang, Shihang Wang, Bob Zhang, Yuzhong Peng, Pinxian Zeng, Chengliang Liu, Mengran Li, Ziyang Tang, Xiaojun Yao, Chuxia Deng, Qianqian Song
bioRxiv 2026 · Project · Preprint · Code · Data
TL;DR: Benchmarks molecular and gene representations for multimodal prediction of drug-induced cellular phenotypes at scale.
PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery
Bo Li, Bob Zhang, Chengyang Zhang, Minghao Zhou, Weiliang Huang, Shihang Wang, Qing Wang, Mengran Li, Yong Zhang, Qianqian Song
Nature Communications 17, 793 (2026) · Paper · Webserver · Code · arXiv
TL;DR: Learns cellular representations directly from high-content microscopy for image-based phenotypic drug discovery.
SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer
Bo Li, Ziyang Tang, Aishwarya Budhkar, Xiang Liu, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song
Nature Communications 16, 7861 (2025) · Paper · Code · Data
TL;DR: Connects single-cell and spatial transcriptomics through a style-transfer formulation for cross-modal gene-expression inference.
🔥 News
- 2026.09: 🧬 Released PertResolve, a measurement-resolution framework for fine-grained perturbation prediction. Project · Code
- 2026.06: 🇸🇬 Started a one-year visit to the School of Computing, National University of Singapore, hosted by Prof. Yang Zhang.
- 2026.05: 📄 CellScientist preprint released on arXiv (co-author).
- 2026.04: 🧬 MVCBench preprint released on bioRxiv.
- 2026.01: 🎉 One paper accepted at ICLR 2026 (co-author).
- 2025.12: 🎉 PhenoProfiler published in Nature Communications.
🛠 Software & Resources
Selected research software and community resources.
| Project | What it is | Stars |
|---|---|---|
| PertResolve | Measurement-resolution framework and benchmark for perturbation prediction |  |
| MVCBench | Multimodal benchmark for drug-induced virtual cell phenotypes |  |
| Nature-Paper-Skills | Agent skills for drafting, revising, auditing, and resubmitting scientific manuscripts |  |
| Awesome-Virtual-Cell | Curated papers, datasets, benchmarks, and resources for AI virtual cells |  |
| PhenoProfiler | End-to-end phenotypic profiling for image-based drug discovery |  |
| SpaIM | Cross-modal imputation for spatial transcriptomics |  |
📖 Education
-
2026.06 – 2027.06: National University of Singapore
- Visiting Student, School of Computing. Host: Prof. Yang Zhang
-
2024.08 – Present: University of Macau
- Ph.D. in Computer Science, Department of Artificial Intelligence, Full Scholarship. Advisors: Prof. Bob Zhang, Prof. Qianqian Song
-
2021.09 – 2024.07: Beijing University of Technology
- M.Eng. in Electronic Information. Advisors: Prof. Yong Zhang, Prof. Baocai Yin
-
2017.09 – 2021.07: Beijing Information Science & Technology University
- B.Eng. in Robotics Engineering. Advisor: Prof. Hongbo Huang
🎖 Selected Honors & Awards
- 2024: Ph.D. Scholarship, University of Macau
- 2024.07: Top 100 Graduates of BJUT (Top 100 / 6331)
- 2024.07 & 2021.07: Beijing Outstanding Graduate
- 2023.10: Xiaomi Scholarship
- 2022.10: National Scholarship
- 2021: First Prize, Science & Technology Innovation Scholarship, BISTU
📜 Patents
Co-inventor of three Chinese invention patents on cell image density map generation and cell localization: CN115457546A, CN115810046A, CN115457547A.
💼 Academic Service
Journal reviewer: Science Advances, IEEE TPAMI, IEEE TIP, IEEE TNNLS, Medical Image Analysis, Bioinformatics, Briefings in Bioinformatics, BMC Biology, Engineering Applications of Artificial Intelligence, and Expert Systems with Applications.
Collaborations: Purdue University, Cornell University, University of Florida, National University of Singapore, Sun Yat-sen University, Sichuan University, Beijing University of Technology, and Macao Polytechnic University.
🧭 Research Interests & Technical Stack
Research: AI for Biology · virtual cells · single-cell perturbation modeling · multimodal learning · phenotypic drug discovery · spatial omics · intervention design · scientific agents
Technical: Python · PyTorch · CUDA · Linux
