Tzyy-Ping Jung of UC San Diego

NeuroAI is an exciting interdisciplinary field that aims to deepen our understanding of both neuroscience and artificial intelligence. We will explore two main aspects of NeuroAI: first, how existing knowledge of brain function drives breakthroughs and creates opportunities to improve AI; second, how AI applications are advancing neuroscience research, leading to breakthroughs that could enable truly personalized medicine.

We'll present a survey of our current progress with successful and unsuccessful attempts to build a brain model. Next, we'll review how advances in neuroimaging and signal processing, including the development of Large Brain Models (LBM), have helped us overcome traditional data bottlenecks in neuroscience and AI. Finally, we'll explore the use of brain-computer interfaces (BCIs) in developing Human-AI Symbiosis, the next step in merging humans and AI into a unified system. 

The presentation will end with projections of our expectations for 2030. It is vital to emphasize our ethical duty to protect neural and physiological data. Finally, we will present our vision for a unified NeuroAI Foundation Model and explain how to achieve it through a collaborative, transparent, and open-science ecosystem.

Biography:

Tzyy-Ping Jung is currently the Co-Director of Center for Advanced Neurological Engineering, an Associate Director of the Swartz Center for Computational Neuroscience and an Adjunct Professor of Department of Bioengineering at University of California San Diego, CA, USA. Dr. Jung's research emphasis has been placed on the integration of the cognitive science, neuroscience, bioengineering, and electrical and computer engineering. Dr. Jung established transformative techniques for applying blind source separation to decompose multichannel biomedical signals (e.g. EEG, MEG, ERP, MRS, and fMRI) and was elevated to an IEEE Fellow for his contributions to blind source separation for biomedical applications in 2015. Dr. Jung and his colleagues have also spearheaded the instrumentation and research in real-world neuroimaging and brain-computer interface technologies to study the brain activities from unconstrained, freely moving individuals in naturalistic positions and postures within real-world environmen

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Chaur-Jong Hu of Taipei Medical University

Dementia is a syndrome characterized by progressive cognitive decline and loss of daily living abilities. In Taiwan, the prevalence rate is approximately 8% among those aged 65 and older, with over 300,000 people seeking treatment. Alzheimer's disease is the most common subtype of dementia, with amyloid and tau pathology beginning 10–20 years before the onset of clinical symptoms. Recent rapid advancements in brain technology have significantly improved early detection capabilities through biomarkers such as blood pTau217, Aβ, CSF levels, Amyloid/Tau PET, and digital biomarkers (voice, gait, eye movement, EEG, AI models). Emerging antibody drugs, including Lecanemab and Donanemab, have shown potential to slow disease progression, but carry the risk of cerebral hemorrhage or edema. Furthermore, non-pharmacological treatments such as rTMS, tDCS, sensory stimulation and photobiological modulation, and focused ultrasound also demonstrate promise. Early diagnosis, precision medicine, and preventative intervention are key directions for future dementia care.

Biography:

Dr. Chaur-Jong Hu, MD, PhD
Current Position: Dean of the College of Medicine, Taipei Medical University (TMU)
Dr. Chaur-Jong Hu is a distinguished figure in Taiwanese neurology, specializing in the diagnosis, treatment, and research of dementia and stroke. He currently holds significant administrative and clinical roles within the Taipei Medical University system.
 
Education
Dr. Hu received his medical degree in 1990 from Taipei Medical College (now Taipei Medical University). He pursued advanced research training as a research fellow at the Stroke Center in the Department of Neurology at Washington University in St. Louis, Missouri, USA.
 
Professional Roles and Appointments
Dr. Hu balances his academic leadership with active clinical practice and professional association management:
Dean: Dean of the College of Medicine, Taipei Medical University.
Vice Superintendent: Vice Superintendent of Research, Ministry of Health and Welfare (MOHW) Shuang Ho Hospital (a TMU-affiliated hospital).
Director: Director of the Dementia Center, Shuang Ho Hospital.
Attending Physician: Attending Physician, Department of Neurology, Shuang Ho Hospital.
President: President of the Taiwan Neurological Society.
President: President of the Taiwan Clinical Dementia Society.
 
Areas of Expertise
Dr. Hu is a highly respected clinician and researcher in the following fields:
Dementia and Memory Disorders
Stroke Care and Prevention
Neurodegenerative Diseases (e.g., Parkinson's Disease)
Neuropathic Pain
 
Key Achievements
Dr. Hu has been instrumental in developing high standards of neurological care in Taiwan:
Stroke Care Certification: He led Shuang Ho Hospital to become one of the few hospitals in Taiwan capable of providing advanced acute stroke treatments (IV thrombolysis and mechanical thrombectomy), achieving Joint Commission International (JCI-CCPC) certification for its Comprehensive Stroke Center.
Integrated Dementia Care: He established an acclaimed interdisciplinary dementia center, recognized by Taiwan's Health Promotion Administration for its comprehensive care model involving neurology, psychiatry, dentistry, and nursing staff.
Research Output: Dr. Hu is a prolific researcher with over 200 published academic papers in peer-reviewed journals, actively contributing to the understanding of the molecular mechanisms behind dementia and stroke.

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Chin-Hui Lee of School of ECE, Georgia Tech

A recent study on high-density cortical surface recordings in humans while they listen to continuous speech reveals that distinctive phonetic features, or speech attributes, are directly related to the superior temporal gyrus (STG) representations of the entire phonetic inventory, in contrast to some conventional notion that phonemes are encoded in brain speech perception. This finding agrees to our proposed approach to speech information extraction via integrating multiple speech cues into speech processing with automatic speech attribute transcription (ASAT) which offers a number of new processing applications, including bottom-up automatic speech recognition and language-universal speech modeling for multilingual speech recognition. In the second part of the talk, we discuss other potential brain studies and their potential implications on new speech and language processing applications.

Biography:

Chin-Hui Lee is a professor at School of Electrical and Computer Engineering, Georgia Institute of Technology. Before joining academia in 2001, he had accumulated 20 years of industrial experience ending in Bell Laboratories, Murray Hill, as the Director of the Dialogue Systems Research Department. Dr. Lee is a Fellow of the IEEE and a Fellow of ISCA. He has published over 600 papers and 30 patents, with more than 33,000 citations and an h-index of 89 on Google Scholar. He received numerous honors, including five Signal Processing Society (SPS) Best Paper Awards, three IEEE Proceedings papers, the Bell Labs President's Gold Award in 1998, the SPS Technical Achievement Award for “Exceptional Contributions to the Field of Automatic Speech Recognition'' in 2016, and the ISCA Medal in Scientific Achievement for “Pioneering and Seminal Contributions to the Principles and Practice of Automatic Speech and Speaker Recognition'' in 2012. His two pioneering papers, published in 2014 and 2015, on deep regression for speech enhancement accumulated over 2700 citations, recognized as top downloaded papers in SPS publications, and won an SPS Best Paper Award in 2019.

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Yao-Chuang Chang of Synchron

This presentation provides a concise overview of the rapidly evolving Brain-Computer Interface (BCI) industry, spanning both invasive and non-invasive technologies. We will analyze the current market dynamics, driven by significant investment in both medical applications (e.g., restoring motor and sensory function) and consumer/wellness sectors (e.g., neural interfaces for augmented cognition).

A primary focus will be placed on the current status of human clinical trials, highlighting key technological milestones achieved in major BCI programs worldwide. Crucially, the talk will address the critical challenges currently facing these trials, including:

Regulatory Hurdles: Navigating complex global medical device approvals.

Long-term Reliability: Ensuring the stability and biocompatibility of implanted devices over decades.

Ethical and Privacy Concerns: Managing neural data and establishing standards for safe deployment.

Understanding these dynamics and challenges is essential for translating BCI innovations from the laboratory bench to safe, reliable, and widely accessible clinical solutions.

Biography:

Yao-Chuang has 10+years experience in biomedical engineering, neural engineering, neurostimulation, and neuroscience, and experience across central, peripheral, and retina nervous system.
He has provided technical leadership in the design and development of technically advanced neuromodulation therapies, through combination of the physiological study and computational modeling to optimize the device and therapy. He is currently leading the system design, integration, and verification of new generation of Brain-Computer Interface (BCI) medical device.
Yao-Chuang received the Best Student Paper Award of 8th International IEEE EMBS Conf. on Neural Engineering in 2017 and Reviewer of Quarter of Bioelectronic Medicine in 2019, and served as Guest Editor for Neural Technology - Frontiers in Neuroscience 2022-2023. He is also the recipient of Medtronic Technical Excellence Award FY24.

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Chung-Chuan Lo of National Tsing Hua University

Explaining behavior begins with uncovering the neural circuits that produce it. Computational modeling offers a powerful framework for revealing the principles of circuit computation, formulating mechanistic hypotheses, and generating testable predictions about brain function. Importantly, many of these modeling approaches are directly relevant to NeuroAI: by understanding how biological circuits solve navigation, control, and prediction problems, we gain new inspiration for building more robust and adaptive artificial systems.

In this talk, I will present several studies from my lab that illustrate how circuit-level models can advance both neuroscience and NeuroAI. First, I will discuss our work on animal navigation and steering control. We developed a circuit model that explains how animals maintain spatial orientation, and we validated its key predictions through optogenetic disruption of spatial memory. A follow-up study identified downstream premotor circuits responsible for flexible steering control, offering insight into how internal goals are transformed into motor commands.

In another collaborative project, we constructed a detailed biophysical model of cerebellar circuits incorporating realistic ion-channel dynamics. This model elucidates the mechanisms underlying essential tremor and predicts how specific pharmacological interventions may suppress pathological oscillations.

Together, these studies demonstrate how computational models not only clarify the principles of neural computation and the mechanisms of brain disorders, but also provide biologically grounded motifs that can inspire next-generation NeuroAI systems.

Biography:

Chung-Chuan Lo, Ph.D., is a Professor and the Director of the Institute of Systems Neuroscience, as well as the Associate Dean of the College of Life Sciences and Medicine at National Tsing Hua University (NTHU), Taiwan. He received his Ph.D. in Physics from Boston University.

Dr. Lo’s research primarily focuses on Computational Neuroscience, specializing in neuroinformatics and the development of data-driven spiking neural network models of the fruit fly (Drosophila) brain. His major contributions lie in modeling spatial orientation memory and the underlying neural circuit mechanisms.

He is also actively involved in interdisciplinary work on neuromorphic engineering, translating principles of neuroscience into low-power visual and cognitive systems for applications such as robotic navigation and obstacle detection. Dr. Lo is a recognized leader in the field, having founded the Taiwanese Society for Computational Neuroscience.

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Syu-Jyun Peng of Taipei Medical University

This lecture will present real-world research outcomes and clinical implementations of artificial intelligence in medical imaging diagnostics and prognostic modeling. Topics include brain tumor segmentation and quantitative analysis, seizure-risk prediction for low-grade gliomas, FDG-PET metabolic lateralization in epilepsy, and EEG-based machine-learning models for forecasting antidepressant treatment response. Practical experiences in deploying AI-driven clinical decision-support systems within hospital workflows will also be discussed, highlighting opportunities and challenges in translating data-driven technologies into routine clinical practice.

Biography:

Dr. Syu-Jyun Peng is an Associate Professor in the Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, at Taipei Medical University (TMU). He also serves as the Director of the program and is a member of TMU’s Research Center of Artificial Intelligence in Medicine and Health.

Dr. Peng obtained his Ph.D. from the Department of Electrical Engineering at National Central University in 2014.

His core research focuses on the intersection of engineering, neuroscience, and medicine, specifically in:

• Neuroimaging Processing and Analysis (Epilepsy, Ischemic Stroke)
• Artificial Intelligence in Medicine
• Neurophysiology Analytics
• Brain Connectome Modeling
• Translational Medicine and Medical Image and Signal Analysis

Dr. Peng has a prolific research record, including the publication of 16 SCI articles and 60 international conference papers, and has secured 18 invention patents. His work has been recognized with multiple accolades, including the Outstanding Paper Award from the Taiwan Epilepsy Society and the Taiwan Pain Society.

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Ming-Chin Lin of Taipei Medical University

Background: Prognostication of consciousness recovery after brain injury remains challenging. Electroencephalography (EEG), as a non-invasive neurophysiological technique, contains rich information about consciousness states. Deciphering these complex neural signals to predict consciousness recovery has become a critical frontier in neuroscience. Objective: To employ artificial intelligence for decoding EEG functional connectivity features and establish a multimodal predictive model integrating clinical variables. Methods: EEG was recorded from 111 patients with impaired consciousness. Functional connectivity was computed using weighted Phase Lag Index (wPLI) and coherence across frequency bands. Machine learning algorithms were applied to decode prognostic information embedded within neural signals. Results: Alpha-band wPLI was significantly higher in patients with good outcomes (p<0.05). Multimodal feature sets achieved optimal predictive performance (AUC = 0.83-0.84). Conclusion: AI-assisted EEG deciphering technology successfully revealed neural network signatures of consciousness recovery, establishing a scientific foundation for neurology pioneering the future society through precision prognostication.

Biography:

Dr. Ming-Chin Lin is a distinguished leader in medical informatics, bridging international governance, national healthcare transformation, and clinical innovation. As Vice President of the International Medical Informatics Association (IMIA) and President of the Taiwan Association for Medical Informatics (TAMI), he drives global collaboration and national standardization initiatives. His successful leadership of MedInfo 2025—one of IMIA's most successful conferences—demonstrated his exceptional organizational capabilities and vision for healthcare AI integration.

As a practicing neurosurgeon and Vice President of Wan Fang Hospital, Dr. Lin uniquely combines clinical excellence with administrative leadership and medical informatics expertise. His research focuses on building the Smart Medical Information Mart for Neuro Intensive Care (MiNiC), integrating real-time physiological signals with electronic medical records to develop AI-driven predictive models for neurological outcomes. He serves as a certified ROSA Robotic Arm trainer at Asia's only training center, advancing precision neurosurgery across the region.

CURRENT POSITIONS

ž   Vice President, International Medical Informatics Association (IMIA), 2023-2026

ž   President, Taiwan Association for Medical Informatics (TAMI), 2023-Present

ž   MedInfo 2025 Local Organizing Committee Chair, Taipei, Taiwan (Successfully completed)

ž   MedInfo 2027 Supervisor, Dubai, UAE

ž   Vice President 2025, Wan Fang Hospital, Taipei Medical University

ž   Associate Professor, Graduate Institute of Medical Informatics, Taipei Medical University

ž   Neurosurgeon & Certified ROSA Robotic Arm Trainer, Shuang Ho Hospital (only training center in Asia)

Po-Han Chiang of National Yang Ming Chiao Tung University

This talk will present cutting-edge advancements in wireless neuroengineering, focusing on the innovative technique known as MagTIES (Magneto-Thermal Interrogation of the Electrical System). MagTIES enables highly localized and targeted modulation of neural activity across multiple distinct brain regions simultaneously.

The core of the research addresses the challenge of understanding and controlling the dynamics of multi-region brain oscillations—a fundamental mechanism underlying complex cognitive functions and dysfunctions. Prof. Chiang will demonstrate how these engineered oscillatory patterns can be precisely controlled and utilized.

The presentation will cover:

The MagTIES Platform: Principles and design of a fully wireless, non-invasive system for multi-site neural modulation.

Oscillation Engineering: Methods for precisely tuning inter-regional phase-amplitude coupling and oscillatory coherence.

NeuroAI Applications: Discussion on translating these engineered brain dynamics into novel NeuroAI applications, particularly in the development of adaptive neurofeedback systems and highly selective therapeutic interventions for neurological disorders.

This work offers a significant step forward in developing next-generation brain-machine interfaces capable of fine-tuning the brain's internal communication network.

Biography:

Po-Han Chiang, Ph.D., is an Assistant Professor at the Institute of Electrical and Computer Engineering (ECE) at National Yang Ming Chiao Tung University (NYCU). He is affiliated with the Biomedical Division of the institute.

Dr. Chiang leads research at the intersection of Neuroscience, Biomedical Electronics, and Control Systems, focusing on developing advanced tools for interacting with the nervous system.

His primary research interests include:

ž   Wireless Neuromodulation Techniques (such as deep brain magnetic stimulation and remote neural control technologies).

ž   Magnetogenetics (MagTIES technology).

ž   Neuro-Electrophysiology and Nano-Biomaterials.

ž   Applying engineering principles to study neurological disorders, including Parkinson's disease and Autism.

His work emphasizes translating fundamental neuroscience knowledge into low-power, targeted biomedical electronic solutions and NeuroAI applications.

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