Bo Li 李波

Bo Li 李波

Ph.D. student · University of Macau
Visiting student · National University of Singapore

Bo Li 李波 — AI for Biology: from multimodal virtual cells to experimental decisions

AI FOR BIOLOGY
From Multimodal Virtual Cells to Experimental Decisions

I develop decision-centric AI methods that connect multimodal biological measurements, virtual-cell modeling, and intervention design. My work asks what biological distinctions experiments can reliably resolve, what information generalizes across cellular contexts, and which interventions are worth testing under limited experimental budgets.

Long-term visionAI-driven Experimental Intelligence for Biology
University of Macau · advised by Prof. Bob Zhang · co-advised by Prof. Qianqian Song at Purdue University
National University of Singapore · visiting student · hosted by Prof. Yang Zhang
Seeking Fall 2027 postdoctoral opportunities in AI for Biology, virtual cells, measurement-aware evaluation, and intervention design.

🔥 News

2026.09 🎯 Released VCDesign, a framework for finite-budget intervention design in virtual cells. Project · Code
2026.09 🧬 Released PertResolve for measurement-aware perturbation evaluation. Project · Code
2026.09 📘 Collaborative paper published in IJCV on incomplete multi-view multi-label learning.
2026.08 📘 Collaborative paper published in IEEE TPAMI on incomplete multi-view multi-label learning.
2026.08 🧬 Collaborative paper PSSD published in Bioinformatics for histology-to-gene-expression prediction.
2026.06 🇸🇬 Started a one-year visit to the 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.
2025.12 🧬 Collaborative spatial-omics paper HAST published in Communications Biology.
2025.08 🧬 SpaIM published in Nature Communications.
2024.10 📄 HGGEP published in Briefings in Bioinformatics.
2024.08 🧬 Collaborative paper AntiFormer published in Briefings in Bioinformatics for antibody binding-affinity prediction.
2024.08 🎓 Started my Ph.D. at the University of Macau.
2024.07 🏅 Graduated from Beijing University of Technology as a Top 100 Graduate and Beijing Outstanding Graduate.
2024.03–06 🔬 Published MHFAN, Lite-UNet, and EDTM across Pattern Recognition and EAAI.

🔬 Research

01 · MEASURE

What biological distinctions can experiments reliably resolve?

Determine whether experimental measurements contain reproducible information at the biological resolution required by downstream prediction and evaluation. Separate measurement limitations from model limitations before interpreting benchmark performance.

PertResolve MorphoSuff
02 · MODEL

What biological information transfers across modalities and contexts?

Learn and evaluate cellular representations across transcriptomic, morphological, spatial, and perturbational measurements, with an emphasis on what information remains useful under biological and experimental shifts.

MVCBench PhenoProfiler SpaIM
03 · DESIGN
Current focus

Which experiments are worth performing next?

Formulate intervention design as a finite-budget decision problem: rank feasible interventions toward a biological objective and evaluate the selected experiments using independently measured outcomes.

Toward adaptive intervention selection and prospective experimental feedback.
VCDesign

🔭 Research Vision

AI systems should not only model cellular responses, but help decide what biological experiments are worth performing next.
My long-term goal is to connect reliable biological models, intervention-design algorithms, scientific workflow agents, and experimental feedback into progressively more capable experimental decision systems.
Reliable measurements Decision-centric virtual cells Intervention design Prospective experimental feedback
Virtual cell models biology · Scientific agent plans and orchestrates · Experiment provides external feedback and validation

📝 Selected Publications

DESIGN · Intervention Design
VCDesign: finite-budget intervention design for virtual cells

VCDesign: Finite-Budget Intervention Design for Virtual Cells

Bo Li, Lin Wang, Bob Zhang, Mengran Li, Zhenchao Tang, Chengyang Zhang, Minghao Sun, Chengliang Liu, Zhiyuan Liu, Yang Zhang

Manuscript 2026  ·  Project  ·  Manuscript  ·  Code  ·  Data

TL;DR: Formulates cellular intervention design as finite-budget ranking over feasible candidates and evaluates the selected experiments using independently measured held-out outcomes. VCDesign separates the experimental decision from the computational solver, while VCDesign-CED supports response-unseen candidates by transferring historical perturbation evidence through biological knowledge.

MEASURE · Measurement-aware Evaluation
PertResolve: measurement resolution for fine-grained perturbation prediction

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 measurement limits from modeling limits by asking whether an experiment can reproducibly resolve the biological distinctions that downstream prediction models are expected to recover.

MODEL · Virtual Cell Generalization
MVCBench: benchmarking drug-molecular and gene representations for drug-induced virtual cell phenotypes

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: Systematically evaluates which molecular and cellular representations support multimodal virtual-cell prediction and which gains remain useful across perturbations, cellular contexts, and experimental shifts.

MODEL · Cellular Representation
PhenoProfiler: end-to-end phenotypic profiling of high-content cell images

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 to support image-based phenotypic profiling and drug discovery.

MODEL · Multimodal Biology
SpaIM: style-transfer imputation for single-cell spatial transcriptomics

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 cross-modal modeling of gene-expression information across biological measurement spaces.

Full publication record on Google Scholar ↗

🛠 Software & Resources

Selected research software and community resources.

Project What it is Stars
VCDesign Finite-budget intervention ranking and decision-aligned evaluation for virtual cells GitHub stars
PertResolve Measurement-aware evaluation framework for interpreting perturbation-prediction performance GitHub stars
MVCBench Multimodal benchmark for virtual-cell representation and generalization GitHub stars
PhenoProfiler End-to-end phenotypic representation learning for high-content cell imaging GitHub stars
Awesome-Virtual-Cell Curated literature, datasets, benchmarks, and resources for virtual-cell research GitHub stars
Nature-Paper-Skills Agent skills for scientific manuscript drafting, revision, auditing, and resubmission GitHub stars
SpaIM Cross-modal modeling for spatial transcriptomics imputation GitHub stars

📖 Education

  • 2026.06 – 2027.06: National University of Singapore

    - Visiting Student. Host: Prof. Yang Zhang

  • 2024.08 – Present: University of Macau

    - Ph.D. in Computer Science, Department of Artificial Intelligence, Full Scholarship. Advisor: Prof. Bob Zhang; Co-advisor: Prof. Qianqian Song, Purdue University

  • 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, Pattern Recognition, 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 · measurement-aware evaluation · multimodal biological modeling · cellular perturbations · intervention design · experimental decision making · scientific agents
Technical: Python · PyTorch · CUDA · Linux

Beijing Information Science & Technology University Beijing University of Technology University of Macau West China Hospital, Sichuan University National University of Singapore