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SUMMARY:Bridge2AI Discussion Forum on Emerging ELSI Issues: “Pragmatic and Nimble AI Governance in Healthcare"
DESCRIPTION:Please join us on Tuesday\, February 17th 2026 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Pragmatic and Nimble AI Governance in Healthcare“\,  by Dr. Susannah Rose. \n\n\n\n\nDr. Susannah Rose will discuss the ethical issues related deploying system-wide artificial intelligence in healthcare delivery organizations. This presentation will focus on the practical governance policies\, process and challenges. Attendees will have the opportunity to ask questions as part of an engaging and dynamic presentation. \n\nBio: \n\n\n\n\nDr. Rose is an ethicist and mixed-methods researcher specializing in patient experience and the ethics of artificial intelligence (AI) in healthcare. She is an Associate Professor at Vanderbilt University Medical Center and serves as Vice-Chair of VUMC’s AI Technology Committee and Executive Director of the ADVANCE Center. Dr. Rose previously founded the AI + Ethics Program at Cleveland Clinic\, teaches public health ethics at Harvard T.H. Chan\, and her research is funded by organizations including ARPA-H\, NIH\, and The Greenwall Foundation.
URL:https://bridge2ai.org/event/bridge2ai-discussion-forum-on-emerging-elsi-issues-pragmatic-and-nimble-ai-governance-in-healthcare/
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DTSTART;TZID=America/New_York:20260219T150000
DTEND;TZID=America/New_York:20260219T160000
DTSTAMP:20260522T222417
CREATED:20260120T202102Z
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SUMMARY:TRM Novel AI Technology Module Lecture: "Encrypted Machine Learning" by Dr. Vishnu Boddeti
DESCRIPTION:Join us on Thursday\, February 19\, 2026\, at 12:00 PM PT/3:00 PM ET for the lecture: “Encrypted Machine Learning” by Dr. Vishnu Boddeti. This lecture is part of the Bridge2AI Training\, Recruitment\, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module. \nLecture Learning Objectives: \n\nUnderstand the advantages and disadvantages of neural (in particular LLM) architectures vs. neuro-symbolic approaches in the biomedical space.\nLearn how to build symbolic and neuro-symbolic natural language processing systems that are faithful\, pliable\, and fast.\n\nBiography: \nDr. Vishnu Boddeti is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University and the Director of the Human Analysis Lab. His research focuses on developing AI systems with provable guarantees of fairness\, privacy\, and accuracy\, with applications spanning high-stakes scientific and societal domains. His work includes auditing and mitigating bias in foundation models through fairness–utility trade-offs and adversarial red-teaming\, designing cryptographically secure AI systems using homomorphic encryption and FHE-native architectures\, and advancing physics-informed AI for scientific discovery by integrating physical laws to improve generalization and reduce data requirements. \nHe has received multiple distinguished research honors\, including the 2024 IEEE-CCF Cloud Computing Best Paper Award\, Best Paper Awards at IROS 2023 and IEEE TBIOM (2022–2023)\, and recognition as an Editor Highlight in Nature Communications. He is also a recipient of a Facebook Research Grant on Multi-objective Co-evolutionary Learning (2021). Dr. Boddeti is an active contributor to the research community\, serving as Senior Area Editor for IEEE Transactions on Information Forensics and Security (2025) and Area Chair for NeurIPS (2025) and AutoML (2023). His research demonstrates that well-designed cryptographic\, statistical\, and physical constraints can enable AI capabilities\, providing both theoretical foundations and practical systems for trustworthy AI deployment in scientific and biomedical contexts.” \nIntroduction
URL:https://bridge2ai.org/event/trm-novel-ai-technology-module-lecture-encrypted-machine-learning-by-dr-vishnu-boddeti/
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DTSTART;TZID=America/New_York:20260226T150000
DTEND;TZID=America/New_York:20260226T160000
DTSTAMP:20260522T222417
CREATED:20260223T160917Z
LAST-MODIFIED:20260223T161007Z
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SUMMARY:TRM Novel AI Technology Module Lecture: "From Volume to Value: Rethinking Data for AI in Healthcare” by Dr. Teresa Wu
DESCRIPTION:  \nJoin us on Thursday\, February 27\, 2026\, at 12:00 PM PT/3:00 PM ET for the lecture: “From Volume to Value: Rethinking Data for AI in Healthcare” by Dr. Teresa Wu. This lecture is part of the Bridge2AI Training\, Recruitment\, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module. \nDr. Teresa Wu is the Fulton Professor of Industrial Engineering\, the Vice Dean for Academic and Student Affairs at Ira Fulton Schools of Engineering (FSE)\, Arizona State University. She is also the founding Director of the ASU–Mayo Center for Innovative Imaging (AMCII)\, a multi-institutional center uniting ASU engineers and data scientists with clinicians at Mayo Clinic\, Arizona. Her research focuses on machine learning and deep learning for heterogeneous\, multi-modal medical data\, with applications in disease diagnosis and prognosis. \nDr. Wu is a President’s Professor at ASU (2024) and an IISE Fellow (2020)\, and has received honors including the IBM Faculty Research Award in Health Systems (2017)\, the Harold G. Wolff Lecture Award at Mayo Clinic (2015)\, and the Fulton Schools Exemplar Award at ASU (2016). She was also an ASU PLuS Global Health Alliance Fellow (2016–2020) and an NSF CAREER Award recipient (2003). She serves as the Emeritus Editor-in-Chief of IISE Transactions on Healthcare Systems Engineering; Associate Editor forJournal of Alzheimer’s Disease\,Neuroscience and Biomedical Engineering\, and IIE Transactions on Healthcare Engineering. She is an active contributor to the research community through editorial leadership\, program committees for NIPS\, SDM\, and KDD\, long-standing NSF grant review service\, and a member of the Institute of Industrial and Systems Engineers. \nLecture Learning Objectives: \n\nReview the evolution of AI methodologies\, with a focus on modeling approaches in quantitative medical imaging.\nReview the impact of data quality versus data quantity on the performance and reliability of AI models in medical applications.\nLearn how post-hoc calibration improves prediction with confidence.\n\nIntroduction
URL:https://bridge2ai.org/event/trm-novel-ai-technology-module-lecture-from-volume-to-value-rethinking-data-for-ai-in-healthcare-by-dr-teresa-wu/
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