Aaron Newman, Professor of Biomedical Data Science
Title: Decoding cellular plasticity and spatial biology with data science
Abstract: Cellular plasticity and stem cell regulatory programs play key roles in cancer pathogenesis. However, the microenvironments and gene expression programs that underpin such phenotypes remain poorly understood. We have developed novel computational approaches that leverage and integrate single-cell, bulk, and spatial gene expression profiling to infer developmental phenotypes and perform large-scale and spatially informed analysis of cellular ecosystems from complex tumor specimens.
Nima Aghaeepour, Professor of Anesthesiology, Perioperative and Pain Medicine, of Pediatrics and, by courtesy, of Biomedical Data Science
Title: A Data Driven Taxonomy for Prematurity
Abstract: Preterm birth is the single largest cause of death in children under 5 years of age. Recent technological advances in machine learning, biology, and and wearable devices provide exciting opportunities to unravel the complex biology of pregnancy and early life. I will discuss a series of studies that use immunology, electronic health records, and wearable devices to enable a precision-medicine approach to maternal and neonatal health.
Serena Young, Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Title: Using AI to Improve Surgeon Skill and Outcomes
Abstract: AI offers great potential to provide automated assessment, feedback, and assistance to surgeons through computer vision-based interpretation of surgical video. In this talk I will discuss our group’s ongoing work on developing computer vision methods that can perform fine-grained understanding of surgical video, and present applications towards improving surgeon training and identifying aspects of surgeon technique associated with outcomes.
Manuel Rivas, Professor of Biomedical Data Science
Title: Inference and prediction from population biobanks
Abstract: Recent advances in statistical methods and computational power have enabled the analysis of large-scale datasets from population biobanks, leading to the discovery of novel therapeutic targets and pathways. I will showcase the path from statistical methodological development to target identification for glaucoma to therapeutic development using monoclonal antibodies to mimic effects of protective mutations in humans. Finally, I will present on approaches for disease risk prediction using genetics, metabolomics, and proteomics data. Together, the methods and applications presented in this talk demonstrate the value of population-scale cohorts to advance our understanding of disease and development of new treatments.
Tina Hernandez-Boussard, Associate Dean of Research; Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Title: Fostering Inclusive AI in Healthcare: A Path to Equitable Care Delivery and Health
Abstract: In the pursuit of equitable AI in healthcare, we focus on cultivating AI solutions that provide benefit to all populations. By addressing bias, accessibility, and diverse representation, we strive to build a healthcare AI framework that ensures fairness and benefits for all.
Akshay Chaudhari, Professor of Radiology and, by courtesy, of Biomedical Data Science
Title: “Vision-Language Models in Radiology”
Abstract: In this talk, I will describe recent work in multi-modal vision-language pretraining for applications in radiology. We demonstrate the ability of multi-modal vision-language models for synthetic text-driven medical image generation as well as enabling few-shot understanding of medical images. We will discuss potential applications and challenges of such generative and discriminative models in radiology.
Nigam Shah, Chief Data Scientist for Stanford Health Care; Professor of Medicine and of Biomedical Data Science
Title: Data Science at Stanford Healthcare
Abstract: There is immense excitement around the use of artificial intelligence to advance the science and practice of medicine as well as the delivery of healthcare. We will discuss the experience at Stanford Healthcare in shaping the responsible use of this technology in our health system.