As machine learning becomes increasingly central to biomedical discovery and clinical decision-making, ensuring the reliability, fairness, and interpretability of these models is critical. In our lab, we are committed to developing and applying machine learning methods that are not only accurate but also trustworthy.
Single-cell multi-omics data present unique challenges. Our lab combines advanced computational methods with high-dimensional biological data to uncover mechanisms of disease, with a particular emphasis on cancer and metabolic disorders.
High-dimensional imaging datasets pose significant challenges in quantification and interpretation. Our lab applies quantitative imaging, from multiplex confocal microscopy to imaging flow cytometry, to convert biological images into precise, spatially resolved measurements that retain each cell’s location, phenotype, and interactions.
Understanding the complexity of cancer requires making sense of equally complex biological data. In our lab, we develop probabilistic models for the integration of multi-omics datasets to uncover hidden structure in the data by capturing both shared and modality-specific sources of variation.