{"id":166,"date":"2024-08-22T13:56:46","date_gmt":"2024-08-22T05:56:46","guid":{"rendered":"http:\/\/smile.ee.ncku.edu.tw\/cibcb2025\/?page_id=166"},"modified":"2025-08-13T03:19:42","modified_gmt":"2025-08-12T19:19:42","slug":"keynote-speech","status":"publish","type":"page","link":"http:\/\/smile.ee.ncku.edu.tw\/cibcb2025\/?page_id=166","title":{"rendered":"KEYNOTE SPEECH"},"content":{"rendered":"\n

KEYNOTE 1<\/strong><\/p>\n\n\n\n

Advancing Biomedical Discovery in a Data-Driven AI Era<\/strong><\/p>\n\n\n\n

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Yang C. Fann<\/strong><\/p>\n\n\n\n

Director of Clinical Informatics, National Institutes of Health, USA<\/p>\n\n\n\n

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  • Abstract:<\/strong>
    The convergence of computational intelligence (CI), artificial intelligence (AI), and vast, ever-expanding biomedical datasets is reshaping the future of scientific discovery and healthcare innovation. The recent data-driven AI techniques and biologically inspired CI methods are driving breakthroughs in genomics, medical imaging, diagnostics, and personalized medicine. The evolution from early rule-based systems to today\u2019s hybrid, adaptive, and explainable frameworks highlights a dramatic shift in technological capability.

    Real-world case studies ranging from deep learning for disease detection to evolutionary algorithms for biomarker discovery and hybrid systems for clinical decision support demonstrate the transformative impact of these technologies on biomedical research. With recent advances in AI and the availability of rich, high-quality data, discoveries once thought impossible are now within reach. However, challenges and issues such as data quality, heterogeneity, interpretability, trust, and ethical concerns will need to be addressed to ensure responsible use and effective implementation. \u00a0At the same time, exciting new opportunities are emerging in multi-modal data integration, generative AI, real-time predictive analytics, and the democratization of advanced analytical tools for advancing the biomedical discovery.

    To help navigate this rapidly evolving landscape, this presentation will share key lessons learned and offer practical advice for building common informatics infrastructure to support interdisciplinary teams and fostering collaboration. By embracing a data-driven approach and uniting experts across domains, we can unleash the full potential of AI in advancing biomedical discovery to improve health care and shaping the future of global health.
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  • Biography<\/strong>
    Prof. Yang C. Fann earned his Ph.D. in Computational Chemistry from Temple University. Currently, he holds the position of Director for the Intramural IT and Bioinformatics Program at the National Institute of Neurological Disorders and Stroke (NINDS), which is part of the National Institutes of Health (NIH). Additionally, he serves as Clinical Informatics for the Office of Intramural Research. In these roles, he plays a pivotal role in addressing specific clinical informatics challenges and issues within the NIH Intramural Research Program (IRP). Prof. Fann has also overseen the development of numerous IT systems and extensive biomedical databases to facilitate data sharing, foster collaboration, and support innovative research endeavors.

    Between2010 and 2015, Prof. Yang C. Fann received several prestigious awards recognizing their outstanding contributions to biomedical research and administrative innovation. In 2014, he earned two notable accolades: the HHS Green Champion Leadership Award and the NIH Director Award for the operation of the Porter Neuroscience Building. In 2015, he was recognized with the NIH CIT Director Award for his innovations in biomedical research systems.<\/li>\n<\/ul>\n\n\n\n

    KEYNOTE 2<\/strong><\/p>\n\n\n\n

    Building predictive models with AI in radiology and digital pathology<\/strong><\/p>\n\n\n\n

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    Anne Martel<\/strong><\/p>\n\n\n\n

    University of Toronto, Canada<\/p>\n\n\n\n

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    • Biography<\/strong>
      Dr Anne Martel is a Professor in Medical Biophysics at the University of Toronto and a Senior Scientist at Sunnybrook Research Institute where she holds the Tory Family Chair in Oncology. She is also a Faculty Affiliate of the Vector Institute, Toronto and the CSO of  Pathcore, a digital pathology software company, that she co-founded in 2006.

      Dr Martel is a fellow of the MICCAI Society, a senior member of SPIE and currently serves as a senior editor for the journal Medical Image Analysis.

      Her research program is focused on medical image and digital pathology analysis, particularly on applications of machine learning and artificial intelligence for detection, diagnosis, and prediction\/prognosis.<\/li>\n<\/ul>\n\n\n\n

      KEYNOTE 3<\/strong><\/p>\n\n\n\n

      Computational Medicine for Mental and Physical Health<\/strong><\/p>\n\n\n\n

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      Rose T. Faghih<\/strong><\/p>\n\n\n\n

      New York University, United States<\/p>\n\n\n\n

      Abstract:<\/strong><\/p>\n\n\n\n

      As new physiological sensing technologies become available for continuous monitoring of physiological signals, the dynamic response to external influences such as environmental inputs, medication, and surgery can be quantified. This research focuses on developing mathematical algorithms for dynamically tracking health states related to mental and physical health in the presence of different interventions. (1) Mental Health Focus: We design algorithms for a closed-loop neural wearable architecture called MINDWATCH for mental and cognitive well-being. We first infer arousal-related autonomic nervous system (ANS) activations. Then, we model and decode cognitive arousal and performance brain states where the inferred ANS activations and behavioral data are used as cognitive arousal and performance observations, respectively. We use neurofeedback to close the loop and modulate cognitive arousal and performance. (2) Physical Health Focus: We investigate clinical data from patients to study inflammation, fatigue, and metabolism using cytokines, stress hormones, and metabolic hormones, respectively. We deconvolve biochemical signals (e.g., hormones) to obtain the secretory events underlying their pulsatile production. Then, utilizing the recovered secretory events, we decode hidden health states (e.g., energy) dynamically. The ultimate goal is to design toolsets that can provide clinically relevant information using biosensors to prevent, diagnose, and manage health conditions.<\/p>\n\n\n\n

      Biography:<\/strong><\/p>\n\n\n\n

      Dr. Rose T. Faghih is an associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory. She received a bachelor\u2019s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT). She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Dr. Faghih is the recipient of various awards including a 2024 IEEE Engineering Medicine and Biology Society (EMBS) Early Career Achievement Award, a 2023 National Institutes of Health (NIH) Maximizing Investigators\u2019 Research Award for Early Stage Investigators, a 2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a 2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a \u201cWoman to Watch\u201d. She is on the editorial board of PNAS Nexus by the National Academy of Sciences, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Moreover, she is a senior member of IEEE and currently an IEEE Engineering in Medicine and Biology Society Administrative Committee Technical Representative. Dr. Faghih is the senior author of a Biomedical Engineering book titled Bayesian Filter Design for Computational Medicine published by Springer. Her research interests include wearable technologies, and medical cyber-physical systems, as well as neural and biomedical signal processing.<\/p>\n","protected":false},"excerpt":{"rendered":"

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