🟧 MACHINE LEARNING FOR MEANINGFUL LIVES
We build AI that understands life.
M2Lab develops machine learning methods for medicine, biology, and human health—turning complex biomedical data into models that matter.
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WHAT WE DO
WHAT WE DO
At the intersection of machine learning, medicine, and computational biology, we create scientifically interpretable and clinically useful models. This boils down to developing computational methods that bridge foundational machine learning with meaningful biomedical questions.
Medical Digital Twins with Explainable Clinical AI
Medical Digital Twins with Explainable Clinical AI
Predict patient deterioration from longitudinal physiological data. Build fine-grained, faithful explanations for clinical time-series models.
Digital twins are intelligent, adaptive computational replicas of individual patients that continuously evolve by integrating real-time, multimodal data streams—such as vital signs, laboratory results, medical imaging, genomics, and clinical narratives. These dynamic models emulate physiological processes, anticipate disease progression, and evaluate potential interventions before they are implemented in practice. By mirroring a patient’s unique and evolving health profile, digital twins empower clinicians to deliver personalized, data-driven care with greater precision and foresight. They serve as a foundation for next-generation precision medicine, enabling predictive analytics, continuous learning, and closed-loop feedback systems that connect real-world clinical data with virtual simulation environments for continual model refinement.
Virtual Cells & Spatial Biology with Generative AI
Virtual Cells & Spatial Biology with Generative AI
Connect tissue morphology with spatial gene expression. Develop generative modeling for molecular, biological, and clinical applications.
Virtual cells are advanced computational representations of individual cells that integrate high-resolution single-cell and spatial multi-omics data to model cellular identity and behavior. By capturing gene expression, chromatin accessibility, protein profiles, and spatial context, digital cells reconstruct molecular mechanisms underlying development, disease progression, and therapeutic response. These models reveal dynamic lineage trajectories, cell–cell communication networks, and microenvironmental influences that shape cellular states. As digital counterparts to living cells, they enable in silico experimentation, virtual perturbation analysis, and predictive modeling — laying the foundation for precision diagnostics, targeted drug discovery, and the next generation of data-driven biomedical research.
WHO WE ARE
WHO WE ARE
Different disciplines. One shared purpose.
Different disciplines. One shared purpose.
We are a multidisciplinary team at National Taiwan University working across machine learning, biomedical engineering, computational biology, and medicine.
Let’s build something meaningful.
Let’s build something meaningful.
JOIN M2LAB
Interested in machine learning for medicine and biology? We welcome curious students, researchers, and collaborators.
© 2026 M2Lab
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