Speaker Profile
PhD, Professor, Jackson Laboratory
Biography
I am Professor at The Jackson Laboratory for Genomic Medicine, leading a lab that studies problems at the intersections of computational biology, cancer, and spatial data science, especially: development of interpretable methods for deep learning-based tissue image analysis; mechanistic and evolutionary analysis of multiplex image data including spatial transcriptomics and spatial proteomics; and the application of such methods to clinical and patient-derived cancer model tissues to develop new clinical trials. My current projects include leadership of the Data Commons and Coordination Center of the National Cancer Institute (NCI) Patient-Derived Xenograft Network Consortium; major roles in the NCI PIVOT Pediatric Cancer Testing Consortium and the NIH Senescence Network Consortium; and diverse studies to quantify intratumoral heterogeneity and improve outcomes for patients with cancer types including breast cancer, colorectal cancer, and melanoma. I am also Deputy Director of The Jackson Laboratory Cancer Center and leader of the JAX Cancer Advanced Technology program.
Talk
AI Approaches for Interpreting Cancer Topography
Recent multiplex imaging technologies have dramatically improved our ability to quantify the spatial topography of biological tissues, including both the cells and their surrounding environments. I will discuss how new spatial data science approaches combining deep neural networks and mechanistically interpretable methods can improve cancer prediction and treatment.
AI and Data Sciences Showcase:
Jackson Laboratory
The Jackson Laboratory is an independent, nonprofit biomedical research institution with a mission is to discover precise genomic solutions for disease and empower the global biomedical community in the shared quest to improve human health. For more information visit www.jax.org.
Session Abstract – PMWC 2024 Silicon Valley
The PMWC 2024 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
