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SUMMARY:Bridge2AI Discussion Forum on Emerging ELSI Issues: “Conjoint analysis of perspectives on ethical tradeoffs in data generation project"
DESCRIPTION:Please join us on Tuesday\, March 17th 2026 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Conjoint analysis of perspectives on ethical tradeoffs in data generation project”\,  by Dr. Dr. Nicholas G. Evans. \nRegistration not required!\, view past recordings here. \n Additional details in the attached documents and message below. \nBio: \nDr. Nicholas G. Evans\, Ph.D. is Associate Professor of Political Science at the University of Massachusetts Lowell\, where he co-directs the Modelling Individual and Networked Decisions (MIND) Lab. His work focuses on the intersection of ethics\, infectious disease\, emerging technologies\, and national security. His current major projects focus on ethics of artificial intelligence\, funded by the National Institutes of Health\, US Army Research Laboratory DEVCOM\, and Greenwall Foundation. \nDr. Evans is best known for his work on “dual-use research of concern\,” beneficial scientific research that has a risk of misuse in the development of weapons of mass destruction. In 2012 he completed one of the first Ph.D. dissertations on the ethics of dual-use research of concern\, where he wrote on the scope and strength of scientific freedom in the face of national security concerns. He has since published more than a dozen articles\, books\, and book chapters on dual-use research. In Spring 2025 he published Gain of Function with The MIT Press Essential Knowledge Series\, providing a thoroughgoing guide to the topic\, its controversies\, and future. \nDr. Evans is also a recognized expert in public health ethics\, writing on the ethics of social distancing\, research ethics during health emergencies\, and the use of force in pandemic response. His 2016 collection\, Ebola’s Message: Public Health and Medicine in the 21st Century received favorable reviews in Nature from Dr. Peter Piot\, who first identified the virus in 1976. His new book on the ethics of pandemic preparedness and response\, War on All Fronts: A Theory of Health Security Justice\, was published in May 2023. Both are available open access at The MIT Press Website. \nPrior to his appointment at the University of Massachusetts Lowell\, Dr. Evans completed postdoctoral research at the University of Pennsylvania. In 2015\, he held an Emerging Leaders in Biosecurity Initiative Fellowship at the UPMC Center for Health Security\, Baltimore; has held visiting appointments at the Universities of Exeter\, Bradford\, and Cambridge; and is a three-time Fondation Brocher resident scholar in bioethics in Hermance\, Switzerland. He is also a former policy officer with the Australian Department of Health where he worked on therapeutics regulation\, and assisted reproduction policy.
URL:https://bridge2ai.org/event/bridge2ai-discussion-forum-on-emerging-elsi-issues-conjoint-analysis-of-perspectives-on-ethical-tradeoffs-in-data-generation-project/
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DTSTART;TZID=America/New_York:20260319T150000
DTEND;TZID=America/New_York:20260319T160000
DTSTAMP:20260410T045245
CREATED:20260223T180613Z
LAST-MODIFIED:20260223T180613Z
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SUMMARY:TRM Novel AI Technology Module Lecture: “Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification” by Dr. William Speier
DESCRIPTION:  \nJoin us on Thursday\, March 19\, 2026\, at 12:00 PM PT/3:00 PM ET for the lecture: “Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification” by Dr. William Speier. This lecture is part of the Bridge2AI Training\, Recruitment\, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module. \nDr. William Speier is an Associate Professor in the Departments of Radiology\, Bioengineering\, and Bioinformatics at UCLA and a member of the Medical Informatics home area. He is the Associate Director of the UCLA Biomedical Artificial Intelligence Research (BAIR) Laboratory\, where his research focuses on developing machine learning and AI methods to improve clinical support applications\, including brain-computer interface assistive devices and automated pipelines for limbal stem cell deficiency diagnosis. His work integrates large language models\, statistical modeling\, system optimization\, and evaluation metrics\, with a strong emphasis on translating computational methods into real-world clinical implementation. \nDr. Speier serves as Co-chair of the Medical Informatics Curriculum Committee and on the Steering Committee for the Brain Info Conference. He is an Academic Editor for Frontiers in Human Neuroscience (since 2023) and PLOS ONE (since 2018)\, and has contributed as a mentor in the UCLA CARE Science\, Engineering\, and Math (SEM) Summer Program (2019) and the Bruins-In-Genomics (BIG) Mentorship Program (2019-20). His research highlights a commitment to translational science\, combining theoretical development with online implementation and patient testing. \n\nLecture Learning Objectives: \n\nExplain the importance of type 1 error in thyroid cancer diagnosis.\nCompare different approaches to handle multi-modal data.\nEvaluate the effectiveness and clinical utility of biomedical AI applications.\n\nIntroduction
URL:https://bridge2ai.org/event/trm-novel-ai-technology-module-lecture-multimodal-deep-learning-models-for-thyroid-cancer-risk-stratification-by-dr-william-speier/
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DTSTART;TZID=America/New_York:20260326T150000
DTEND;TZID=America/New_York:20260326T160000
DTSTAMP:20260410T045245
CREATED:20260323T172703Z
LAST-MODIFIED:20260323T172918Z
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SUMMARY:TRM Novel AI Technology Module Lecture: “Identifying Heterogeneous Treatment Effects using Machine Learning for Future Precision Medicine and Public Health” by Dr. Kosuke Inoue
DESCRIPTION:Join us on Thursday\, March 26\, 2026\, at 12:00 PM PT/3:00 PM ET for the lecture: “Identifying Heterogeneous Treatment Effects using Machine Learning for Future Precision Medicine and Public Health” by Dr. Kosuke Inoue\, MD\, PhD. This lecture is part of the Bridge2AI Training\, Recruitment\, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module. \nDr. Kosuke Inoue is a physician-epidemiologist at Kyoto University whose research focuses on clinical and cardiovascular epidemiology\, with an emphasis on statistical modeling and causal inference. Formally trained as an endocrinologist\, he has advanced training in causal inference methodologies. His research integrates causal inference frameworks with machine learning algorithms to analyze high-dimensional cohort and clinical trial data. He also applies machine learning–based heterogeneous treatment effect methods to both randomized controlled trial (RCT) and observational data to better understand variation in treatment responses and disease risk across populations. \nDr. Inoue has established a strong research record\, with 160 peer-reviewed publications\, including 95 first- or last-author papers\, over the past decade. His work has been recognized with many prestigious honors\, including the Young Scientist Award from the Ministry of Education\, Culture\, Sports\, Science and Technology of Japan (2025)\, MIT Technology Review Innovators Under 35 Japan (2023)\, the Medical Research Encouragement Prize from the Japan Medical Association (2023)\, the Young Investigator Award from the Japan Endocrine Society (2023)\, and the Encouragement Award from the Japan Epidemiological Association (2024). His work advances the integration of epidemiology\, causal inference\, and data science to support more evidence-based clinical and public health decision-making. \nLecture Learning Objectives: \n\nAssessing treatment effect heterogeneity is essential for understanding mechanisms behind average effects and identifying sub groups with higher or lower benefit.\nRecently developed machine learning methods\, including casual forests and meta-learners\, can be used for evaluating such heterogeneity.\nThe High-Benefit Approach\, an approach targeting individuals with high benefit\, has the potential to serve as a future personalized medicine strategy by efficiently allocating healthcare resources and reducing health disparities.\n\nIntroduction
URL:https://bridge2ai.org/event/trm-novel-ai-technology-module-lecture-identifying-heterogeneous-treatment-effects-using-machine-learning-for-future-precision-medicine-and-public-health-by-dr-kosuke-inoue/
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