Bridge2AI Discussion Forum on Emerging ELSI Issues: “Trust Starts Upstream: Recommendations for Creating Ethically Sourced Health Data Repositories for AI/ML"
2026-01-20 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, January 20th, 2026 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Trust Starts Upstream: Recommendations for creating ethically sourced health data repositories for AI/ML,” by Dr. Camille Nebeker.

Creating ethically sourced and trustworthy health data repositories is critical for building trustworthy biomedical and behavioral research infrastructure, especially when such repositories underpin machine learning and AI systems. In Ethical sourcing in the context of health data supply chain management: a value sensitive design approach (Nebeker et al., 2025), we integrate value sensitive design (VSD) with concepts from supply chain management to operationalize ethical values across the stages of developing health data repositories. The resulting framework identifies key actors, values (e.g., traceability, security, equity), and tensions that arise during repository creation and highlights practices such as documenting data provenance, articulating expectations for data stewards, and implementing comprehensive privacy and bias mitigation strategies.
During this session, Dr. Nebeker will provide an overview of this framework with a focus on developing a companion checklist that is grounded in VSD and supply chain scaffolding and can be used as actionable guidance in dataset creation.

Bio:
Dr. Camille Nebeker is a professor of public health with appointments in the UC San Diego Design Lab and the Herbert Wertheim School of Public Health and Human Longevity Science. In 2018, Dr. Nebeker co-founded the ReCODE Health center, which is dedicated to conducting cutting edge research to inform ethical practices in digital/AI health research – including machine learning and the use of large language models. The ReCODE Health center supports education and consultation services to guide ethical practices in technology-supported health research across diverse research sectors including traditional academic research and, increasingly, the health technology sector. She’s a principal investigator with the Bridge2AI Bridge Center’s Ethical, Legal and Social Implications Core.

TRM Novel AI Technology Module Lecture: "Beyond LLMs: Neuro-Symbolic Approaches for Language Understanding in the Biomedical Domain" by Dr. Mihai Surdeanu
2026-01-29 15:00 - 16:00 (America/New_York)
“Join us on Thursday, January 29, 2026, at 12:00 PM PT/3:00 PM ET for the lecture: “Beyond LLMs: Neuro-Symbolic Approaches for Language Understanding in the Biomedical Domain” by Dr. Mihai Surdeanu. This lecture is part of the Bridge2AI Training, Recruitment, and Mentoring (TRM) 2025-26 Lecture Series Novel AI Technology Module.
Lecture Learning Objectives:

Understand the advantages and disadvantages of neural (in particular LLM) architectures vs. neuro-symbolic approaches in the biomedical space.
Learn how to build symbolic and neuro-symbolic natural language processing systems that are faithful, pliable, and fast.

Biography:
Dr. Mihai Surdeanu is a Professor of Computer Science in the College of Science at the University of Arizona, with joint appointments in Linguistics and Cognitive Science. His research focuses on natural language processing, including question answering, information extraction, and building systems that derive meaning from unstructured text, with a strong emphasis on interpretable models that can explain their decisions.
Dr. Surdeanu has received the ACL Outstanding Area Chair Award (2023) and the Distinguished Scholar Award from the University of Arizona’s Department of Computer Science (2023). He is an active contributor to the NLP community, serving as an Action Editor for Transactions of the Association for Computational Linguistics (TACL) since 2019 and as Senior Area Chair for ACL (2023, 2025) and EMNLP (2022). He has held Area Chair roles for EMNLP, NAACL, and COLING–LREC across multiple years. Dr. Surdeanu has also served on program committees for ACL and EMNLP for a long time, in addition to contributing to COLM, COLING, as well as Graph-Based NLP and Pan-DL workshops.”
Introduction Slides

Bridge2AI Discussion Forum on Emerging ELSI Issues: “Pragmatic and Nimble AI Governance in Healthcare"
2026-02-17 15:00 - 16:00 (America/New_York)
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.

Dr. 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.

Bio:

Dr. 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.

TRM Novel AI Technology Module Lecture: "Encrypted Machine Learning" by Dr. Vishnu Boddeti
2026-02-19 15:00 - 16:00 (America/New_York)
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.
Lecture Learning Objectives:

Understand the advantages and disadvantages of neural (in particular LLM) architectures vs. neuro-symbolic approaches in the biomedical space.
Learn how to build symbolic and neuro-symbolic natural language processing systems that are faithful, pliable, and fast.

Biography:
Dr. 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.
He 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.”
Introduction

TRM Novel AI Technology Module Lecture: "From Volume to Value: Rethinking Data for AI in Healthcare” by Dr. Teresa Wu
2026-02-26 15:00 - 16:00 (America/New_York)
Join 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.
Dr. 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.
Dr. 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.
Lecture Learning Objectives:

Review the evolution of AI methodologies, with a focus on modeling approaches in quantitative medical imaging.
Review the impact of data quality versus data quantity on the performance and reliability of AI models in medical applications.
Learn how post-hoc calibration improves prediction with confidence.

Introduction

Bridge2AI Discussion Forum on Emerging ELSI Issues: “Conjoint analysis of perspectives on ethical tradeoffs in data generation project"
2026-03-17 15:00 - 16:00 (America/New_York)
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.
Registration not required!, view past recordings here.
Additional details in the attached documents and message below.
Bio:
Dr. 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.
Dr. 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.
Dr. 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.
Prior 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.

TRM Novel AI Technology Module Lecture: “Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification” by Dr. William Speier
2026-03-19 15:00 - 16:00 (America/New_York)
Join 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.
Dr. 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.
Dr. 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.

Lecture Learning Objectives:

Explain the importance of type 1 error in thyroid cancer diagnosis.
Compare different approaches to handle multi-modal data.
Evaluate the effectiveness and clinical utility of biomedical AI applications.

Introduction

TRM Novel AI Technology Module Lecture: “Identifying Heterogeneous Treatment Effects using Machine Learning for Future Precision Medicine and Public Health” by Dr. Kosuke Inoue
2026-03-26 15:00 - 16:00 (America/New_York)
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.
Dr. 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.
Dr. 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.
Lecture Learning Objectives:

Assessing treatment effect heterogeneity is essential for understanding mechanisms behind average effects and identifying sub groups with higher or lower benefit.
Recently developed machine learning methods, including casual forests and meta-learners, can be used for evaluating such heterogeneity.
The 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.

Introduction

Bridge2AI Discussion Forum on Emerging ELSI Issues: “Making Health AI Work: Turning Principles into Real-World Impact"
2026-04-21 15:00 - 16:00 (America/New_York)
This month, Dr. Cora Han discussed how health systems can move from high-level AI principles to practical, responsible implementation. Drawing on UC Health’s experience, she covered governance, real-world use cases, and challenges around regulation, data, and workflow integration — as well as how AI can improve care delivery, reduce clinician burden, and scale innovation responsibly across healthcare
—
Registration not required!, view past recordings here.
Additional details in the attached documents and message below.
Bio:
Cora Han, JD is Chief Health Data Officer for University of California Health. As a strategic leader at the intersection of healthcare, technology, and governance, Han directs the Center for Data-driven Insights and Innovation (CDI2), a systemwide data platform that leverages health data to improve care, drive research, and foster innovation. She also directs UC-wide data governance initiatives, including the responsible development and application of AI in healthcare. Previously, Han served as a senior attorney in the Federal Trade Commission’s Division of Privacy and Identity Protection, where she played a leading role on health privacy matters in both enforcement and policy. She is a frequent speaker on artificial intelligence, data governance, and data privacy.

2026 Spring All-Hands Meeting
2026-04-28 08:30 - 2026-04-29 17:00 (America/Los_Angeles) | NIH Neuroscience Center 6001 Executive Blvd Rockville MD United States
Dear Bridge2AI Community:

We are excited to invite you to register for the 2026 Bridge2AI All-Hands Meeting, taking place April 28-29 at the NIH Neuroscience Center with Conference Chairs Dr. Mónica Muñoz Torres and Dr. Yulia Levites Strekalova.

The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program was established to solve the AI-readiness gap in biomedical data, setting the stage for the ethical use of AI in behavioral and medical research. As the program enters its fourth year, the focus has shifted from foundational architecture to tangible results. The 2026 All-Hands Meeting highlights the consortium’s landmark data releases and tools while charting its evolution from a structured consortium into a broader, collaborative community. This transition is captured in this year’s theme: From Data to Wisdom.

To register now, please visit the meeting website. Registration will close on April 15, 2026.

2026 Voice AI Symposium & Hackathon
2026-05-04 - 2026-05-06 | The Vinoy Resort & Golf Club, 501 5th Ave NE, St. Petersburg, FL, 33701, United States
Registration and Call for Science are now open for the 2026 Bridge2AI Voice Symposium + Hackathon! Join us May 4–6, 2026, in beautiful St. Petersburg, Florida, for one of the only global events dedicated entirely to voice biomarkers.
The Voice AI Symposium has become an internationally-recognized event and one of the only conferences focused solely on the research, development, and implementation of voice biomarkers to improve healthcare. Unlike some large-scale tech conferences, the Voice AI Symposium is intentionally intimate and designed to give every attendee meaningful opportunities to meet pioneers, tastemakers, and thought leaders shaping the future of healthcare through voice AI technologies. Building on the success of past years, the 2026 Voice AI Symposium will bring together the forefront innovators, researchers, clinicians, entrepreneurs, and industry leaders of the voice biomarker space for a two-day immersive experience at the forefront of voice and AI in healthcare.

Highlights will include:

Hands-on Training & Workshops using the Bridge2AI-Voice dataset and tools.
Podium presentations selected through an open Call for Science
Interactive Panels & Keynotes featuring international experts and visionaries in healthcare and AI.
Tech Fair & Demos where attendees can experience cutting-edge applications of voice AI.
Pitch Competition spotlighting groundbreaking startups.
Networking Events designed to foster real connections across disciplines.
and more!

Submit your most innovative research and technology under this year’s theme:
“Translating AI Research into Reality: Implementing Voice Biomarkers for Transformative Healthcare.”
Key Dates:
Abstract Submission Deadline: December 5, 2025
Notification of Selection: January 15, 2026
Symposium + Hackathon: May 4–6, 2026
Don’t miss your chance to be part of this global movement at the intersection of voice, data, and health innovation — and to connect with the community shaping the next era of healthcare AI.
Register early to secure your spot — there is limited capacity!
Voice AI Symposium: https://www.eventsquid.com/event.cfm?id=29517
Call for Science: https://www.eventsquid.com/event.cfm?id=30025
