Biography: Guillaume is a postdoctoral researcher at Harvard Medical School (Mahmood Lab) and Brigham & Women’s Boston Hospital in the group of Prof. Faisal Mahmood. He obtained his Ph.D. in Electrical and Electronic Engineering from EPFL in collaboration with IBM Research and ETH Zurich in 2022. Guillaume’s research focuses on computational pathology with the aim to integrate artificial intelligence (AI) tools into both the clinical and research facets of pathology. His research involves two main objectives: first, enhancing the representation learning of tissue by developing general-purpose foundation models for histology; and second, integrating AI tools in drug development to improve drug safety assessment, detect toxicity, and discover safety biomarkers.
Abstract: Pathology practice and biological research generate large amounts of multimodal data, ranging from tissue sections marked by various stains to DNA assays and recently spatial transcriptomics. Yet, H&E-stained tissue remains the gold standard for studying many diseases, including cancer. This underscores the importance of developing general-purpose AI models for histology to advance precision medicine, patient stratification and prognostication, among others. In this talk, I will explore two key questions: How can we develop universal and transferable models for histology?, and How can we effectively leverage these models for diagnosis and prognosis, and biomarker discovery? I will be discussing these points from the perspective of recent cancer studies, with projects such as UNI (Nature Medicine), CONCH (Nature Medicine), and Tangle (CVPR), as well as from the point of view of drug-induced toxicity, with studies such as TRACE and GEESE (under review).
