Teams

Research groups

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  Gottardo Group

  The Gottardo Group develops and applies innovative statistical and computational methods to tackle fundamental biological and clinical questions. Our work sits at the interface of new experimental technologies, large-scale data generation, and advanced data analysis, with a strong emphasis on probabilistic and Bayesian modeling, high-dimensional inference, and multimodal data integration. By working closely with experimentalists and clinicians, we generate and analyze complex datasets derived from state-of-the-art single-cell and spatial technologies and well-annotated clinical samples, with the goal of extracting robust, actionable biological insight from real-world data.

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  Raisaro Group

  The Raisaro Group advances trustworthy and clinically grounded AI to improve patient care and enable multicenter research. We develop methods and models ranging from clinical Large Language Models and agentic systems to multimodal foundation models that integrate electronic health record data, including clinical text, structured variables, and time-series signals. Our vision is to translate rigorous, reproducible research into scalable, regulatory-ready clinical AI with measurable impact on patient outcomes. To support safe deployment in real-world settings, we also build foundational infrastructure such as CHORUS, a cloud-native secure processing environment that enables compliant data access, sharing, and analysis across clinical and research partners.

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  Rapsomaniki Group

  The Rapsomaniki Group develops AI and machine learning methods for biomedicine, with the vision to transform complex biomedical data into mechanistic models of disease and to guide precision intervention across scales- from molecules and cells to tissues, patients, and therapeutic outcomes. Our research is organized around three interconnected themes: Represent: integrating diverse biomedical data types- from single-cell and spatial omics to histopathology- into unified multimodal representations that capture cellular states, tissue morphology, and spatial architecture; Interpret: building explainable AI methods that reveal the drivers behind model predictions uncovering biological mechanisms and spatial biomarkers; and Perturb: creating predictive models that simulate heterogeneous, system-level responses to drug, genetic, or immunotherapy perturbations to support virtual experimentation. Together, these efforts aim to elucidate disease mechanisms from the intra-cellular to the patient level, bridging data, AI, and precision medicine.

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  Fellay Group

  The Fellay group conducts translational genomics research to demonstrate the clinical value of human genome analysis in medicine, focusing on polygenic risk scores, pharmacogenomics and genomic data integration in healthcare.

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