Adarsh Subbaswamy is a Staff Fellow (Regulatory Scientist) at the U.S. Food and Drug Administration (FDA) in the Office of Science and Engineering Laboratories (OSEL) at the Center for Devices and Radiological Health (CDRH). His research at FDA focuses on developing tools to quantify algorithmic bias, reduce performance differences among subpopulations, and evaluate the quality of medical data. More broadly he has worked on improving the reliability of machine learning models in healthcare through research on dataset shifts, causal machine learning, and distributional robustness. Before joining FDA, he completed his PhD in Computer Science at Johns Hopkins University where he was advised by Professor Suchi Saria.
Dr. Shannon McWeeney, Professor and Vice-Chair at Oregon Health and Science University (OHSU), is a methodologist working at the intersection of computer science, biostatistics and genetics to develop approaches to solve research bottlenecks and novel ways to visualize and interpret information. Her work on novel computational methods and frameworks for prioritization is one of her most significant contributions to science. While these methods were initially applied to precision medicine (cancer), they have wide applicability for target identification and therapeutic prioritization for many complex traits. In 2010, Dr. McWeeney was selected as a Kavli Frontiers Fellow by the US National Academy of Sciences for these contributions.
Dr. McWeeney is the inaugural Chief Data Officer and Associate Director of Data Science for the OHSU Knight Cancer Institute and has a career-long commitment to data sharing and patient engagement. She served as a member of the Enhanced Data Sharing working group for the NCI Blue Ribbon Panel for White House Moonshot Initiative and as a member of the Biden Cancer Initiative Data Sharing and Patient Empowerment workstream. In 2020, she was named the Director of Medical Bioinformatics for the OHSU Knight Precision Oncology Program, in which she is responsible for the hardening and validation of assays, algorithms and models in the transition from research analytics to CLIA. She is a M-PI for OHSU’s NCI Acquired Resistance to Therapy Network (ARTNet), which arose from her collaborative work for over a decade as part of the OHSU BeatAML program. She is also an MPI for the NIH Bridge2AI AI-READi Salutogenesis Data Generation Project, collaboratively working to develop automated tools to accelerate the creation of FAIR (Findable, Accessible, Interoperable, and Reusable), CARE (Principles for Indigenous Data Governance) and ethically sourced data sets for machine learning and artificial intelligence.
Dr Yukun Zhou is a postdoctoral researcher at UCL Institute of Ophthalmology, Centre for Medical Image Computing, and Moorfields Eye Hospital. He leads the team’s research in AI for Medicine. He published AutoMorph, a pioneering deep learning-based retinal image analysis tool, and RETFound, the first ophthalmic foundation model trained and validated on large-scale clinical data, in Nature 2023. His research has been featured in social media like TED, ScienMag, and MedicalXpress. His contribution to the community has been recognised by Ruskell Medal, CNT Early Career Award in Neuroimaging Technique, and Robert Speller Prize. He also serves as a reviewer for journals including Nature Medicine, Nature Biomedical Engineering, and Lancet Digital Health.
Jason Alan Fries, PhD is a computer scientist at Stanford University’s Center for Biomedical Informatics Research. His work focuses on methods that enable domain experts to rapidly build and modify machine learning models in complex domains such as healthcare, where obtaining large-scale, expert-labeled training data is a significant challenge. His research spans foundation models in healthcare, including multimodal foundation models, large language models (LLMs), data-centric AI, and human-in-the-loop systems. His work has appeared in NeurIPS, ICLR, AAAI, and Nature Communications.
Professor Su-In Lee, the Paul G. Allen Endowed Professor of Computer Science at the University of Washington (UW), earned her PhD from Stanford University in 2009 under the guidance of Professor Daphne Koller. She joined UW in 2010 after serving as a visiting Assistant Professor at Carnegie Mellon University. Renowned for her groundbreaking work at the intersection of AI, biology, and medicine, Professor Lee has received several prestigious awards, including the Samsung Ho-Am Prize—often referred to as the “Korean Nobel Prize”— as the first woman to receive the Engineering award in its 34-year history, the International Society for Computational Biology (ISCB) Innovator Award, and the National Science Foundation (NSF) CAREER Award. She has also been honored as an ACS Research Scholar and American Institute for Medical and Biological Engineering (AIMBE) Fellow. Professor Lee is recognized as a pioneer in explainable AI (XAI) for her seminal contributions, particularly her Shapley Additive Explanations (SHAP) framework, significantly advancing the interpretability of machine learning models.
Her recent research focuses on fundamental XAI principles and techniques, as well as innovative biomedical research, spanning from basic biology to clinical medicine, which has been enabled by advancements in XAI. By fundamentally shifting how AI is integrated into biomedical research, her work addresses cutting-edge scientific questions and enables novel discoveries from high-throughput molecular data and electronic health records. This transformative integration is advancing healthcare in meaningful ways. This innovative research has resulted in highly cited publications in foundational AI, computational molecular biology, and clinical medicine.
