BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//BRIDGE2AI - ECPv6.15.13//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:BRIDGE2AI
X-ORIGINAL-URL:https://bridge2ai.org
X-WR-CALDESC:Events for BRIDGE2AI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261008T150000
DTEND;TZID=America/New_York:20261008T160000
DTSTAMP:20261023T075400
CREATED:20261002T083847Z
LAST-MODIFIED:20261002T173749Z
UID:7795-1791471600-1791475200@bridge2ai.org
SUMMARY:NIH Bridge2AI Training WG Learning Module | Data Privacy and Federated Learning: Private Insights into GenAI Use by Dr. Peter Kairouz
DESCRIPTION:NIH Bridge2AI Training WG Learning Module | Data Privacy and Federated Learning: Private Insights into GenAI Use by Dr. Peter Kairouz\nScheduled: Oct 8\, 2026 at 12:00 PM to 1:00 PM\, Local Phoenix Time |  https://arizona.zoom.us/j/86095514301\n\n\n\n\nDr. Peter Kairouz is a Research Scientist at Google\, where he leads groundbreaking research initiatives focused on distributed\, robust\, and privacy-preserving machine learning. After earning his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign and serving as a Postdoctoral Research Fellow at Stanford University’s Information Systems Laboratory\, he has established himself as a significant voice on data privacy\, differential privacy\, and federated learning architecture. He is widely recognized for his foundational theoretical contributions\, including defining optimal bounds for privacy loss under sequential execution via the composition theorem for differential privacy\, and for co-authoring seminal field guides and reference frameworks like Advances and Open Problems in Federated Learning.\n\nContinuing to push the boundaries of AI safety\, Dr. Kairouz’s recent work investigates complex vulnerabilities in modern generative systems\, such as context hijacking threats in conversational agents (highlighted by his pioneering work on AirGapAgent) and the systematic flaws of ad-hoc heuristics in GenAI analytics. By championing mathematically rigorous alternatives like Provably Private Insights\, he bridges the critical gap between high-performance machine learning and uncompromised user data security at scale. An active leader in the research community\, he has served as general chair for premier workshops on federated learning and analytics\, and brings an extraordinary depth of vision to the future of trustworthy\, decentralized AI ecosystems.\n\nLEARNING OBJECTIVES\n\n\nEvaluate Heuristic Vulnerabilities: Analyze how ad-hoc\, heuristic-based privacy protections (such as PII redaction and k-anonymity) fail against sophisticated data extraction methods like the CLIOPATRA attack.\nUnderstand Provably Private Frameworks: Examine the architecture and principles of Provably Private Insights (PPI) as a mathematically guaranteed alternative to heuristics for secure AI analytics.\nExplore Secure Deployment Technologies: Learn how integrating Trusted Execution Environments (TEEs)\, secure LLMs\, and Differential Privacy enables scalable\, privacy-preserving data extraction in real-world applications.
URL:https://bridge2ai.org/event/7795/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261008T150000
DTEND;TZID=America/New_York:20261008T160000
DTSTAMP:20261023T075400
CREATED:20261004T234705Z
LAST-MODIFIED:20261004T234705Z
UID:7810-1791471600-1791475200@bridge2ai.org
SUMMARY:Bridge2AI Discussion Forum on Emerging ELSI Issues: “AI\, Augmented Intelligence\, in Healthcare"
DESCRIPTION:Please join us on Thursday\, October 8 2026 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “AI\, Augmented Intelligence\, in Healthcare”\, by Dr. Vincent Liu\, MD\, MS. \n\n\n\n\nArtificial intelligence is rapidly progressing from tools that generate predictions to systems that perform work and increasingly participate in care delivery. This talk explores how trust\, workflow integration\, and rigorous real-world evaluation\, rather than model capability alone\, are becoming the key determinants of healthcare AI impact using examples from traditional AI\, generative AI\, and into agentic AI. \n\n\n\n\n\nVincent Liu\, MD\, MS is Chief Data Officer for The Permanente Medical Group and a Research Scientist with the Kaiser Permanente Division of Research. A pulmonary-critical care physician\, health services researcher\, and AI leader\, Dr. Liu’s work focuses on the development\, implementation\, and rigorous evaluation of artificial intelligence in real-world healthcare settings. His research has spanned predictive analytics\, sepsis and deterioration detection\, ambient AI documentation\, and large-scale evaluation of AI-enabled care delivery\, including leadership of the Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) and participation in ARPA-H programs evaluating agentic AI in clinical care. He is nationally recognized for advancing evidence-based approaches to healthcare AI that improve patient outcomes while emphasizing trust\, workflow integration\, safety\, and equity. \n\n\n\n\n\nMeeting ID: 941 6437 8982
URL:https://bridge2ai.org/event/bridge2ai-discussion-forum-on-emerging-elsi-issues-ai-augmented-intelligence-in-healthcare/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261020
DTEND;VALUE=DATE:20261022
DTSTAMP:20261023T075400
CREATED:20260901T190552Z
LAST-MODIFIED:20261019T145559Z
UID:7730-1792454400-1792627199@bridge2ai.org
SUMMARY:Bridge2AI Fall 2026 All-Hands Meeting & Open House
DESCRIPTION:The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program tackled a key gap in the biomedical research community by generating new “flagship” data sets and best practices for machine learning (ML) analysis. These datasets were collected and processed with AI modeling in mind. To complement these datasets\, the program also developed: software\, standards\, tools\, best practices\, and training materials for workforce development. \nThe Fall 2026 All-Hands Meeting and Open House will highlight significant advances made in the program and highlight some of the breakthroughs by the program teams. Join us to connect with consortium members\, discover new resources\, and gain hands-on experience with the datasets\, tools\, and processes driving the future of biomedical AI. \nClick here to view the full digital program of the meeting’s events.
URL:https://bridge2ai.org/event/bridge2ai-fall-2026all-hands-meeting-open-house/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261022T150000
DTEND;TZID=America/New_York:20261022T160000
DTSTAMP:20261023T075400
CREATED:20261016T155046Z
LAST-MODIFIED:20261016T155309Z
UID:7873-1792681200-1792684800@bridge2ai.org
SUMMARY:Bridge2AI × VUMC ADVANCE Forum on ELSI Issues in AI: “From Prediction to Practice: Generating Evidence for Healthcare AI"
DESCRIPTION:Artificial intelligence has rapidly affected the healthcare landscape with applications ranging from prognostic models to language-based systems and emerging generative and agentic AI applications. But performant systems might not be clinically impactful. Drawing on pragmatic clinical trials of AI-driven interventions in urban and rural clinical settings\, this talk will explore how silent validation\, workflow integration\, governance navigation\, and continuous monitoring can bridge the gap between AI/ML development and improved patient care. The talk will also discuss how advances in language models and phenotyping are expanding what we can measure in healthcare\, while reinforcing the need for rigorous evidence generation in real-world clinical settings.\n\nBio: Colin Walsh\, MD\, MA\, FACMI\, FAMIA\, FIAHSI\, is an Associate Professor of Biomedical Informatics\, Medicine\, and Psychiatry at Vanderbilt University Medical Center. His research focuses on developing\, evaluating\, and implementing artificial intelligence to improve healthcare delivery\, with an emphasis on pragmatic clinical trials\, clinical decision support\, natural language processing\, and mental health. Dr. Walsh leads multisite\, multidisciplinary efforts spanning clinical prediction\, multimodal phenotyping\, and public health informatics\, with the goal of generating rigorous evidence that AI can improve clinical decision-making\, healthcare quality\, and patient outcomes in real-world practice.\n\nhttps://vanderbilt.zoom.us/j/93710289037\nMeeting ID: 937 1028 9037\n\n\n\n\n\n\n\n\n\n\n\n 
URL:https://bridge2ai.org/event/bridge2ai-x-vumc-advance-forum-on-elsi-issues-in-ai-from-prediction-to-practice-generating-evidence-for-healthcare-ai/
END:VEVENT
END:VCALENDAR