Seminars

Bio:  Senior Vice President and Global Head of AI/ML

Dr Kim Branson, is SVP and Global Head, AI/ML at GSK, based in San Francisco. Kim oversees a global team of more than 150 engineers and machine learning researchers. The GSK AI/ML team is focused on the application and development of AI methodology at the intersection of functional genomics and human genetics for target discovery, causal machine learning and clinical applications. Kim joined GSK in 2019 from Genentech where he was Head of AI, Early Clinical Development. Kim received his PhD in Computational Drug Design from University of Melbourne.

Abstract: The dream of precision health is to develop a data-driven, continuous learning system where new health information is instantly incorporated to optimize care delivery and accelerate biomedical discovery. The confluence of technological advances and social policies has led to rapid digitization of multimodal, longitudinal patient journeys, such as electronic health records (EHRs), imaging, and multiomics. Our overarching research agenda lies in advancing multimodal generative AI for precision health, where we harness real-world data to pretrain a virtual patient model as digital twins for patients in forecasting disease progression and treatment response. This enables us to synthesize multimodal, longitudinal information for millions of cancer patients, and apply the population-scale real-world evidence to advancing precision oncology, in deep partnerships with real-world stakeholders such as large health systems and life sciences companies.

Bio: Hoifung Poon is the General Manager of Real-World Evidence at Microsoft Research and an affiliated faculty at the University of Washington Medical School. He leads biomedical AI research and incubation, with the overarching goal of structuring medical data to optimize delivery and accelerate discovery for precision health. His team and collaborators are among the first to explore large language models (LLMs) and multimodal generative AI in health applications, producing popular open-source foundation models such as PubMedBERT, BioGPT, BiomedCLIP, LLaVA-Med, BiomedParse, with tens of millions of downloads. His latest publications in Nature and Cell features groundbreaking digital pathology and spatial proteomics foundation models such as GigaPath and GigaTIME. He has led successful research partnerships with large health providers and life science companies, creating AI systems in daily use for applications such as molecular tumor board and clinical trial matching. His prior work has been recognized with Best Paper Awards from premier AI venues such as NAACL, EMNLP, and UAI, and he was named the "Technology Champion" by the Puget Sound Business Journal in the 2024 Health Care Leadership Awards. He received his PhD in Computer Science and Engineering from the University of Washington, specializing in machine learning and NLP.

Abstract: Female reproductive endocrinology is defined by two major physiological transitions: the rhythmic hormonal oscillations of the menstrual cycle and the permanent cessation of ovarian function at menopause. While reproductive transitions are intrinsic to female physiology, their systemic molecular imprint, and potential contribution to long-term cardiometabolic risk, remains largely unexplored. My research leverages large-scale plasma proteomics and genetic causal inference to define how these transitions shape the systemic molecular landscape and influence long-term health. We first map coordinated proteomic dynamics across the menstrual cycle, identifying phase-specific molecular signatures spanning endocrine, immune, and extracellular matrix pathways. These signatures are enriched in endometrial tissues and cell compartments, associate with common reproductive disorders, and can be distilled into a multi-protein score that captures cycle timing from a single blood sample. We then characterize the proteomic architecture of menopause, demonstrating that ovarian failure induces widespread molecular reprogramming beyond chronological aging. We demonstrate that hormone replacement therapy partially reverses these changes, and that menopause reshapes age-related proteomic trajectories during midlife. Finally, using Mendelian randomization, we identify proteins that are causally altered by ovarian aging and may mediate downstream cardiometabolic disease risk. Together, this work establishes a systems-level framework for understanding female reproductive transitions as molecular events with lifelong health implications.

Bio: Dr. Jonas Ghouse is a physician-scientist specializing in translational genomics, clinical biochemistry, and precision medicine. He currently holds a residency at the Department of Clinical Biochemistry at Aarhus University Hospital, Denmark, and serves as Associate Professor at the Department of Clinical Medicine at Aarhus University. He previously worked in industry in Translational Genomics and Precision Medicine at Novo Nordisk, based in Oxford and Copenhagen, where he focused on integrating human genetics and omics data into drug discovery and development. His research centers on large-scale human genomics and proteomics, leveraging biobank-scale datasets to uncover disease mechanisms, identify causal pathways, and improve risk prediction in cardiovascular, metabolic, inflammatory, and reproductive diseases. He collaborates with international consortia including Copenhagen Hospital Biobank, deCODE Genetics, and the Million Veteran Program, and integrates genetic epidemiology, bioinformatics, and imaging-based artificial intelligence to translate biological insights into clinical impact. Dr. Ghouse received his MD from the University of Copenhagen and completed his PhD at Rigshospitalet, Copenhagen.

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Abstract: The biomedical community has increasingly viewed the medical digital twin (MDT) as a “holy grail”- a bold vision for modeling an individual’s health, predicting disease trajectories, and enabling real-time, personalized interventions. Developing such an MDT is Dr. Wen’s long-term research goal. In this talk, Dr. Wen will highlight prior and ongoing work that applies AI/ML to multi-organ and multi-omics biomedical data to study human aging and disease in a holistic, systems-level manner. The presentation is organized around three connected and progressively advancing themes: i) evaluating the reproducibility of AI/ML methods in neuroimaging research, centered on the brain; ii) characterizing the neuroanatomical heterogeneity of brain disorders using brain imaging and genetics; and iii) developing a multi-organ, multi-omics biological aging clock framework, emphasizing brain-body connections. Together, these efforts lay important preliminary work toward a future multi-scale MDT.

Bio: Dr. Junhao (Hao) Wen is the Principal Investigator of LABS (https://labs-laboratory.com/). He is an Assistant Professor at Columbia’s Department of Radiology, Computer Science, Biomedical Engineering, and the New York Genome Center. He is also a visiting faculty at Penn’s AI2D center.

Abstract: Periodontitis is a prevalent chronic inflammatory disease and a leading cause of tooth loss worldwide, with substantial implications for systemic health and health care utilization. Despite its burden, early identification of individuals at high risk for disease onset and progression remains challenging in routine clinical practice. This seminar will describe a data-driven framework for developing, testing, and validating prediction models for periodontitis using real-world electronic dental record (EDR) and linked health data. The talk will cover key methodological considerations, including cohort construction from longitudinal clinical data, outcome definition using contemporary periodontal classifications, feature engineering from structured dental and medical records, and model development using machine-learning approaches. Emphasis will be placed on internal and external validation strategies, transportability across heterogeneous populations and care settings, and evaluation of model performance, calibration, and clinical utility. The seminar will also discuss challenges related to data quality, missingness, bias, and fairness, as well as strategies such as federated learning to enable multi-institutional validation without sharing patient-level data. Together, this work highlights how predictive modeling can support earlier risk stratification, inform personalized preventive strategies, and advance the integration of clinical decision support tools into dental practice, while illustrating broader lessons for applying biomedical data science methods to oral health research.

Bio: Jay Patel (BDS, MS, PhD in Informatics) is an Assistant Professor and Director of the Center for Artificial Intelligence (AI), Data Science, and Informatics at Temple University Kornberg School of Dentistry. He is among the few in the US with formal training in both clinical dentistry and a doctorate in informatics, an interdisciplinary background that uniquely positions him to lead transformative AI-driven dental research. Dr. Patel’s work focuses on developing AI models and software applications using large-scale electronic health record data to predict disease onset and progression, with the ultimate goal of improving prevention strategies. His research also emphasizes the integration of oral and systemic health through the linkage of medical and dental records to enable real-time health information exchange and support comprehensive data-driven studies. He has developed over 40 natural language processing pipelines and electronic dental record quality metrics to extract valuable insights from unstructured clinical notes. Dr. Patel has been an invited speaker and panelist at leading institutions, including the National Institute of Dental and Craniofacial Research and the National Institutes of Health, where he has presented his pioneering work in dental AI. He has received several NIH-NIDCR-funded awards as both Principal Investigator and Co-Investigator, along with prestigious foundation awards including the William Buttler Award, Robert Wood Johnson Foundation, New Jersey Health Foundation, and the CareQuest Institute. Dr. Patel has published over 50 peer-reviewed papers and is the recipient of multiple national honors, including a U.S. patent for innovation in dental diagnostics.

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