Education

• Ph.D. in Biological and Biomedical Sciences, Harvard University, 2017 – 2022
  • Advisors: Aviv Regev, Debora Marks, Chris Sander
• B.S. cum laude in Life Sciences (Zhiyuan College), Shanghai Jiao Tong University, 2013 – 2017
  • Advisors: Tian Xu, Xiaojing Ma

Work experience

• Mar 2026 – Present: Principal Investigator / AI Lead, Lingang Laboratory
  • Leading AI-driven scientific research initiatives in the Shanghai Lingang Special Zone.
  • Focusing on next-generation drug discovery and synthetic biology applications.
  • Building interdisciplinary teams to bridge AI and experimental biology.
• Aug 2025 – Feb 2026: Associate Director, AI Team Lead, Merck & Co.
  • Led AI team for scientific R&D, driving AI-driven acceleration and optimization of drug R&D for small molecules and biologics.
  • Managed strategic partnerships and cross-functional collaboration with data sciences teams.
  • Manager: Xiang Yu, Senior Director, Data Sciences.
• May 2025 – Aug 2025: Senior AI Scientist II, Project Lead, Merck Research Labs
  • Led projects on streamlined PK/PD learning with foundation models.
  • Optimized real-world strategic ROP decisions using advanced representation learning.
• Jun 2023 – May 2025: Senior AI Scientist, Merck Research Labs
  • Drove automation and efficiency improvement utilizing decades of accumulated data.
  • Implemented representation learning frameworks for novel drug discovery pipelines.
• Oct 2022 – Feb 2023: Research Intern (Sciences), DeepMind Sciences (AlphaFold team)
  • Bridged interpretable ML to cell biology with the AlphaFold team.
  • Designed core ML frameworks in JAX and conducted large-scale model training on Google Cloud.
  • Advisor: Shakir Mohamed, Research Director.
• May 2022 – Aug 2022: Research Intern (Genomics), Google Brain / Google Health
  • Developed denoising methods for ultra-large genomic challenges.
  • First-authored paper accepted at NeurIPS LMRL workshop.
  • Advisor: Cory McLean, Staff SWE Lead.
• Feb 2022 – May 2022: Research Intern, Tencent AI Labs
  • Executed de novo antibody design with biology-inspired neural networks.
  • Performed integrative modeling for multi-modality biological data.
  • Advisor: Jianhua Yao, Director, Center for AI Medicine.
• Apr 2019 – Apr 2022: Research Fellow, Dana-Farber Cancer Institute and MIT CSAIL
  • Predicted early cancer risk with real-world EHR data using interpretable deep learning (Transformer models).
  • Led international collaboration with Danish National Patient Registry.
  • Advisor: Chris Sander, Director, cBio Center.
• Oct 2017 – Dec 2019: Research Assistant, Harvard University and Broad Institute
  • Conducted AI4Science and virtual cell modeling using recurrent graph neural networks.
  • Developed interpretable machine learning methods for genetic network inference.
  • Advisors: Aviv Regev, Chris Sander.
• Apr 2016 – Apr 2017: Research Assistant, HHMI and Yale School of Medicine
  • Applied unsupervised and supervised learning in clinical cancer transcriptomics.
  • Established a new deep learning branch in the lab.
  • Advisor: Tian Xu, HHMI Investigator.

Publications

QComp: A QSAR-Based Imputation Framework for Drug Discovery

Yang, B., Chung, Y., Yang, A. Y., Yuan, B., Chen, T., & Yu, X. (2025). "QComp: A QSAR-Based Imputation Framework for Drug Discovery." Journal of Chemical Information and Modeling, 65(15), 7862-7873.

scPerturb: harmonized single-cell perturbation data

Peidli, S., Green, T. D., Shen, C., et al. (2024). "scPerturb: harmonized single-cell perturbation data." Nature Methods, 21(3), 531-540.

De novo generation of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language model

Yang, J., He, B., Zhao, Y., Jiang, F., Wang, Z., Guo, Y., Xu, Z., Yuan, B., Song, J., Zhang, Q., & Yao, J. (2023). "De novo generation of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language model." bioRxiv, 2023.10.18.562845.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Placido, D., Yuan, B., Hjaltelin, J. X., et al. (2023). "A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories." Nature Medicine, 29, 1113-1122.

An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic Discovery

Yuan, B., Hormozdiari, F., McLean, C. Y., & Cosentino, J. (2022). "An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic Discovery." 36th NeurIPS (LMRL workshop).

Inference of cell dynamics on perturbation data using adjoint sensitivity

Ji, W., Yuan, B., Shen, C., Regev, A., Sander, C., & Deng, S. (2021). "Inference of cell dynamics on perturbation data using adjoint sensitivity." ICLR (SimDL workshop).

CellBox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy

Yuan, B., Shen, C., Luna, A., et al. (2021). "CellBox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy." Cell systems, 12(2), 128-140.

Constructing Data-Driven Network Models of Cell Dynamics with Perturbation Experiments

Yuan, B., Shen, J., Luna, A., Marks, D., Ingraham, J., & Sander, C. (2020). "Constructing Data-Driven Network Models of Cell Dynamics with Perturbation Experiments." 34th NeurIPS (LMRL workshop).

Unsupervised and supervised learning with neural network for human transcriptome analysis and cancer diagnosis

Yuan, B., Yang, D., Rothberg, B. E. G., Chang, H., & Xu, T. (2020). "Unsupervised and supervised learning with neural network for human transcriptome analysis and cancer diagnosis." Scientific Reports, 10(1), 19106.

Teaching

• Teaching Assistant, STAT 215: Introduction to Bioinformatics and Computational Biology, Harvard University.
• Teaching Fellow, BMI 715: Computational Statistics for Biomedical Sciences, Harvard Medical School.
• Teaching Fellow, BMI 713: Computing Skills for Biomedical Sciences, Harvard Medical School.

Service and leadership

• Program Committee Member, ICML Workshop on Computational Biology (WCB), 2020 – 2023.
• Program Committee Member, International Symposium on Mathematical and Computational Oncology (ISMCO), 2021.
• Vice President, Harvard-China Forum of Science and Technology, 2018 – 2019.
• President, Zhiyuan College Student Union, Shanghai Jiao Tong University, 2014 – 2015.

Selected Honors and Awards

• Honor Graduate, Shanghai Jiao Tong University (Top 0.1%), 2017.
• Shanghai Outstanding Student Research (First Place), 2015.
• Xingcai Scholarship (Top 5/400) & KoGuan Scholarship (Top 30/400), 2015.
• Honorable Mention, Mathematical/Interdisciplinary Contest in Modeling (MCM/ICM), 2016.