Featured Sessions

Organizers:

• Asim H. Gazi, Harvard University, USA
• Rayan Bahrami, University of Maryland, College Park, USA
• Omer T. Inan, Georgia Tech, USA
• Jin-Oh Hahn, University of Maryland, College Park, USA

Abstract:

Neuromodulation technologies such as nerve and brain stimulation are promising non-pharmacological therapies for central and peripheral nervous system disorders. A key challenge with neuromodulation interventions is the variability in their therapeutic effects that are often person-specific and context-specific, differing from timepoint to timepoint due to psychophysiological state changes. To tackle this challenge, solutions are needed to better monitor psychophysiology to estimate state changes, dynamically model intervention effects for digital twin-informed decision making, and control neuromodulation delivery in closed-loop to optimize for precision support.

Invited Speakers:

• Jose del R. Millan, University of Texas at Austin, USA
• Mayuresh Kothare, Lehigh University, USA
• Theodoros Zanos, Feinstein Institutes for Medical Research, USA
• Asimina Kiourti, Ohio State University, USA
• Rayan Bahrami, University of Maryland, College Park, USA
• Asim H. Gazi, Harvard University, USA

Organizers:

• Asim H. Gazi, Harvard University, USA
• Ziping Xu, Harvard University, USA
• Susan Murphy, Harvard University, USA

Abstract:

To treat and prevent chronic illnesses, traditional healthcare relies on synchronous clinical support that can only track the dynamics of 1% of daily life (e.g., 1 hour/week of psychotherapy). Yet, many of the challenges faced and changes required in behavior are most critical during the remaining 99% of daily life. Novel mobile health interventions to support individuals during daily life are promising, but interventions are often delivered without AI-driven personalization. Advances in ubiquitous sensing enable biobehavioral tracking during daily life to inform personalization, but principled approaches to process sensor data and infer actionable information are limited.

Invited Speakers:

• Edison Thomaz, University of Texas at Austin, USA
• Laura E. Barnes, University of Virginia, USA
• Varun Mishra, Northeastern University, USA
• Kelly W. Zhang, Imperial College London, UK
• Ziping Xu, UNC Charlotte, USA
• Asim H. Gazi, Harvard University, USA

Organizers:

• Alessandra Galli, Eindhoven, University of Technology, Netherlands
• Ahsan H. Khandoker, Khalifa University, UAE

Fetal-maternal cardiovascular coupling represents a frontier in biomedical research, where the integration of physiological monitoring, signal processing, and clinical insight can lead to breakthroughs in maternal and fetal health. This coupling refers to the dynamic and bidirectional interactions between maternal and fetal cardiovascular systems, influenced by autonomic nervous system activity, respiration, endocrine factors, and developmental processes. Understanding these mechanisms has significant clinical implications, particularly for predicting and preventing complications such as fetal distress, intrauterine growth restriction, and preeclampsia.

Despite its promise, this field remains underrepresented in the biomedical engineering community due to the complexity of simultaneous monitoring and the lack of standardized tools for interpreting coupling phenomena. However, recent advances, including wearable sensors, multimodal physiological data integration, and personalized AI-driven modeling, make this an ideal time to bring focused attention to the topic within a translational context.

By gathering leading researchers from engineering, clinical medicine, and physiology, this session will showcase state-of-the-art research, highlight emerging diagnostic strategies, and foster interdisciplinary collaborations toward safer pregnancies and healthier birth outcomes.

Invited Speakers:

• Giulio Steyde, Politecnico di Milan, Italy
• Jacopo Pavan, University of Virginia, USA
• Anita Krishnan, Children’s National Medical Center, USA
• Namareq Widatalla, Khalifa University, UAE
• Lochana Mendis, University of Melbourne, Australia

Organizers:

• Yun Wang, Emory University, USA
• Ruiying Liu, Emory University, USA
• Xuzhe Zhang, Columbia University, USA

The pediatric AI gap represents one of healthcare’s most pressing inequities. While AI transforms adult medicine, only 17% of FDA-cleared AI/ML devices include pediatric labeling, with most lacking transparent validation for children. This affects every pediatric specialty—from congenital heart disease detection to epilepsy monitoring, from sickle cell genomics to cancer treatment optimization. Children’s unique developmental physiology demands purpose-built AI solutions, yet most algorithms train exclusively on adult data, risking both missed opportunities and potential harm. This special topic session assembles leading experts from hospital and academia who are pioneering pediatric AI applications across diverse clinical domains. From addressing EHR integration challenges to ensuring AI deployment in these populations, from leveraging real-world data for optimal outcomes to advancing computational modeling of developing brains, our speakers will share cutting-edge approaches that bridge the gap between AI’s promise and pediatric reality. Together, we’ll explore how to accelerate safe, effective AI adoption for every child.

Invited Speakers:

• Evan Orenstein, Children’s Healthcare of Atlanta/Emory University, USA
• Lakshmanan Krishnamurti, Children’s Healthcare of Atlanta/Emory University, USA
• Robert C.Gross, Children’s Healthcare of Atlanta/Emory University, USA
• Hao Huang, University of Pennsylvania/Children’s Hospital of Philadelphia, USA
• Geraldine Dawson, Duke University, USA

Organizers:

• Minhaj Nur Alam, UNC Charlotte, USA
• Kaveri Thakoor, Columbia University, USA
• Yu Gan, Stevens Institute of Technology, USA

Abstract:

With the advent and proliferation of medical artificial intelligence (AI) in vision and eye care, numerous innovations are emerging with the potential for transformative impact. These innovations include developing foundational models for different downstream tasks, using federated learning for collaborative model training without sharing multi-site data, integrating multiple modalities (image, text, gaze, sensors etc.) for enhanced model training, and exploring the utility of generative AI in ophthalmic diagnostic applications.  In this special session, world-renowned experts will cover the latest developments in the field of ophthalmic diagnostics.

Invited Speakers:

• Minhaj Alam, UNC Charlotte, USA
• Kaveri Thakoor, Columbia University, USA
• Yali Jia, Oregon Health & Sciences University, USA
• Xincheng Yao, University of Illinois at Chicago, USA
• Yu Gan, Stevens Institute of Technology, USA

Organizers:

• Muhammad Salman Haleem, Queen Mary University of London, UK
• Giussepe Fico, Universidad Politecnica de Madrid, Spain
• Dimitrios Fotiadis, University of Ioannina, Greece

Abstract:

The integration of artificial intelligence (AI) into personalized holds the potential to revolutionize personalized health management. With the advent of modern technology, it is now possible to acquire real-time signals via wearables and wireless sensors which enable individuals to track their well-being, allowing them to adjust their daily routine based on their underline comorbidities and chronic conditions (e.g. diabetes, cancer etc.). Research is being conducted based on design and development of novel multimodal techniques which allow training the real-time signal variations based on underline comorbidities acquired through medical testing, electronic health records, imaging techniques etc. However, despite technological advancements, the design and development of multimodal AI encounters challenges such as information fusion, prediction explainability, practicalities in real-time implementation etc. In this special session, we will discuss these challenges, cutting-edge research, innovative applications, and successful case studies of multimodal AI design. From the insights provided by our expert speakers, participants will gain a comprehensive understanding of the role of multimodal AI in personalized health management and precision medicine.

Invited Speakers:

• Muhammad Salman Haleem, Queen Mary University of London, UK
• Liangxiu Han, Manchester Metropolitan University,
• Edward Sazonov, University of Alabama, USA
• Giussepe Fico, Universidad Politecnica de Madrid, Spain
• Eleni Georga, University of Ioannina, Greece

Organizers:

• Jessilyn Dunn, Duke University, USA
• Md Mobashir Hasan Shandhi, Arizona State University, USA
• Anita Silver, Duke University, USA

Abstract:

DBDP-Autonomic (DBDP-A) is a proposed extension of the Digital Biomarker Discovery Pipeline (DBDP) framework, aimed at fostering reproducible research on autonomic nervous system (ANS) signals collected via mobile or wearable technologies. This special session will emphasize current challenges in analyzing ANS signals from mobile or wearable devices, and opportunities for integrating context from multimodal data. A motivating question to prompt discussion is: Can we disambiguate physiological signals arising from different sources, such as psychological stress, physical activity, or chronic health conditions. to avoid misattribution in downstream analysis? The session will focus on identifying potential benchmark challenges (such as Kaggle competitions), open-source codebases, and shared datasets that could serve as testbeds for evaluating disambiguation strategies, ultimately working toward more transparent, personalized, and reproducible ANS signal research.

Invited Speakers:

• Douglas Bremner, Emory University, USA
• Matthew Goodwin, Northeastern University, USA
• Varun Mishra, Northeastern University, USA
• Nil Gurel, Reality Labs Meta, USA
• Jessilyn Dunn. Duke University, USA
• Mobashir H. Shandhil. Arizona State University, USA

Organizer: Jessilyn Dunn, Duke University

Presenters: Jessilyn Dunn (Department of Biomedical Engineering, Duke University, jessilyn.dunn@duke.edu), Hayoung Jeong (Department of Biomedical Engineering, Duke University, hayoung.jeong@duke.edu), Leeor Hershkovich (Department of Biomedical Engineering, Duke University, leeor.hershkovich@duke.edu), Harrison Kane (Department of Biomedical Engineering, Duke University, harrison.kane@duke.edu), Eric Hurwitz (Department of Genetics, University of North Carolina at Chapel Hill ehurwitz@email.unc.edu

As wearable devices become increasingly popular in biomedical research and health monitoring, addressing missingness in their data streams is essential for valid and reliable analysis. Missingness due to nonwear or irregular sampling is common, yet remains under-characterized and poorly handled in most analytical workflows. This tutorial responds to a growing need in the BHI community to rigorously address missing data challenges in longitudinal observational studies. We will integrate foundational concepts, real-world case studies, and a hands-on session using the All of Us dataset. The tutorial is designed for biomedical researchers and data scientists working with wearable data, digital health, and missing data methodologies. Participants will gain practical tools and conceptual frameworks to identify, interpret, and utilize missingness patterns for more robust health research outcomes.

Organizer: La Chiara Landrum
Presenters: La Chiara Landrum, Meharry Medical College, Dr. Uttam Ghosh , Meharry Medical College, Dr. Puspita Chatterjee, Meharry Medical College

As artificial intelligence continues to reshape the healthcare landscape, this session will explore how agentic and bounded AI can assist in secure, real-time decision-making in resource-constrained environments like portable emergency rooms (PERs). We introduce a multimodal data fusion framework deployable on edge devices. We are combining multiple modalities to support critical decisions without reliance on cloud infrastructure.

Attendees will gain insight into AI-driven solutions designed for reliability, cybersecurity, and offline functionality in disaster response settings. This session brings together healthcare professionals, data scientists, and researchers to engage with practical innovations at the intersection of AI, cybersecurity, and biomedical informatics.

Whether you’re building AI systems, managing sensitive health data, or exploring robust architectures for secure care delivery, this tutorial will equip you with actionable tools and interdisciplinary perspectives shaping the future of intelligent, human-centered healthcare.

Organizer: Farzaneh Nikbakhtsarvestani
Presenters: Farzaneh Nikbakhtsarvestani (Ontario Tech University)

The proposed topic for the tutorial introduces a novel optimization framework that leverages Riemannian manifold geometry to address the complexity and non-linearity inherent in high-dimensional biomedical data. By navigating the solution landscape through geodesic convexity, this approach enhances the ability to find global optima in constrained many-objective problems. The integration of interactive, population-based AI optimization allows practitioners to guide the search process based on clinical priorities, overcoming the limitations of one-size-fits-all pre-trained models. Additionally, real-time calibration of model performance metrics ensures alignment with decision-makers’ expectations throughout the optimization. This tutorial is ideal for researchers, clinicians, and developers interested in advanced AI methods for personalized healthcare solutions

Organizer: Khashayar Namdar
Presenters: Ernest Namdar, (Department of Medical Imaging, University of Toronto; Department of Pharmacology, Faculty of Medicine, Dalhousie University), Pascal Tyrrell, (Department of Medical Imaging, Institute of Medical Science, Department of Statistical Sciences, University of Toronto)

• Learning Curves and Data Sufficiency
•  Confidence
•  Calibration
•  Proper Validation
•  Uncertainty Quantification and Statistical Significance

Organizer: Ryan McGinnis
Presenters: Brett Meyer (Medidata Systems, brett.meyer@3ds.com )
Thurmon Lockhart (School of Biological and Health Systems Engineering, Arizona State University, Thurmon.Lockhart@asu.edu)
Ryan McGinnis (School of Medicine, Wake Forest University, ryan.mcginnis@advocatehealth.org), Oguz Akbilgic (School of Medicine, Wake Forest University, oguz.akbilgic@advocatehealth.org)
Asim Gazi (School of Engineering and Applied Sciences, Harvard University, agazi@seas.harvard.edu)
Jiaee Cheong (Depatrment of Psychiatry, Harvard University, jcheong1@bidmc.harvard.edu) (representing PI John Torous)
Megan O’Brien (Feinberg School of Medicine, Northwestern University, mobrien@ricres.org) (representing PI Arun Jayaraman)
Sameer Neupane (Department of Computer Science, The University of Memphis, Sameer.Neupane@memphis.edu) (representing PI Santosh Kumar)
Sina Masoumi Shahrbabak (Department of Mechanical Engineering, University of Maryland, smasoumi@umd.edu) (representing PI Jin-Oh Hahn)
Varun Mishra (Khoury College of Computer Sciences, Northeastern University, v.mishra@northeastern.edu)
Laura Barnes (Department of Systems and Information Engineering, University of Virginia, lb3dp@virginia.edu)
Orson Xu (Department of Biomedical Informatics, Columbia University, xx2489@cumc.columbia.edu)

Organizers: Yayun Du, Yuanwen Jiang

Presenters: Nilanjan Sarkar, nilanjan.sarkar@vanderbilt.edu, Vanderbilt University; Yayun Du, yayun.du@vanderbilt.edu, Vanderbilt University; Yuanwen Jiang, ywjiang@seas.upenn.edu, University of Pennsylvania; Anthony Banks, tbanks@northwestern.edu, Northwestern University and Company Neurolux; Marco Rolandi, mrolandi@ucsc.edu, UC Santa Cruz

Organizer: Nenad Filipovic

Presenters: Nenad Filipović (Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia, Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia, fica@kg.ac.rs); Dejan Krsmanović (CardioMed Technology Consultants, Miami, FL, USA dan@cardiomedtech.com); Tijana Geroski (Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia, Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia, tijanas@kg.ac.rs), Dejana Popovic (Mayo Clinic, Rochester, Minnesota, USA, Popovic.Dejana@mayo.edu)

Organizer:Sevgi Zubeyde Gurbuz, Edgar Lobaton, and Ravi Chilukuri

Presenters:Dr. Edgar Lobaton, NC State University, Dr. Omer Inan, Georgia Tech and Biozen, Dr. Brinnae Bent, Duke University, Dr. Chenhan Xu, NC State University, Dr. Sevgi Z. Gurbuz, NC State University, Bill Kutsche, Murata, Bharath Rajagopalan, ST