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Biomedical signals—including physiological time-series data (e.g., EEG, ECG, MEG, wearable sensor data), longitudinal Electronic Health Records (EHRs), and spatially structured imaging signals (e.g., MRI, CT, PET)—are fundamental to modern biomedical research and clinical practice. These data provide complementary perspectives on human physiology and pathology across temporal and spatial scales. With the rapid advancement of artificial intelligence (AI), particularly deep learning and emerging foundation models, there is growing potential to transform how biomedical signals are analyzed for disease diagnosis, early detection, and mechanistic understanding. Despite these advances, significant challenges remain. Biomedical signals are often heterogeneous, noisy, and high-dimensional, with complex temporal dynamics and spatial structures. Models developed for one signal type often fail to generalize across settings, and the lack of interpretability limits clinical trust and adoption. Furthermore, current research is often fragmented across subfields (e.g., time series signal processing vs. medical imaging), limiting the development of unified methodological frameworks. This workshop aims to address these challenges by providing a focused venue for advancing AI methodologies for biomedical signal analysis, broadly defined to include both time-series and imaging data. We emphasize the development of robust, generalizable, and interpretable AI approaches that can operate across diverse biomedical signal domains while maintaining strong clinical relevance. Rather than restricting to a specific modality or paradigm, this workshop encourages both modality-specific innovations and cross-domain methodological insights.

Topics

Overview: We invite submissions to our workshop on AI for Biomedical Signals, focusing on advanced methodologies and applications for analyzing biomedical data, including physiological time-series (e.g., EEG, ECG, MEG) and imaging signals (e.g., MRI, CT, PET). The workshop aims to foster interdisciplinary research that bridges methodological innovation and real-world clinical impact.

Topics of Interest: We invite original contributions on topics including, but not limited to:

1. Representation learning for biomedical signal data
2. Image classification, reconstruction, segmentation, and anomaly detection
3. Temporal modeling of biomedical time-series and longitudinal data (e.g., EHR)
4. Multimodal biomedical signal modeling
5. Sensor-based analysis for disease monitoring and early prevention
6. Shapelet and pattern discovery in biomedical signal data
7. Explainable and interpretable AI in healthcare
8. Drug development/repurposing
9. Virtual trial applications
10. Digital twin technologies in healthcare
11. AI for disease diagnosis, detection, and personalized medicine
12. Robust, generalizable, and trustworthy AI in healthcare
13. Graph and network-based modeling of biomedical data
14. Generative models (e.g., LLM, diffusion models, GAN) for biomedical data synthesis (e.g., time-series data, medical imaging)
15. Large language models, multi-modal large models, and foundation models in biomedical data
16. Clinical translation, deployment, and real-world evaluation
17. Agentic AI for modeling, integrating, and reasoning over biomedical signals

Submissions

Please submit either a full-length paper of up to 8 pages or a short paper of up to 4 pages in IEEE two-column format. The IEEE formatting instructions and templates are available here.

The page limit includes all content, including the main paper, references, and any appendices. Authors do not need to include an Ethics Statement or an Acknowledgment section in the submission. The paper submission is double blind. Please do not include any authors and/or affiliations in the paper.

Electronic submissions in PDF or PostScript format are required. The online submission system is: here.

Presentations

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Important Dates

• Paper submission open: July 5, 2026
• Tentative Paper submission deadline: Sep. 27, 2026
• Notification of acceptance: Oct. 18, 2026
• Camera-ready deadline: Nov. 8, 2026
• Workshop date: TBD

Organizers

Program Chairs

Haoteng Tang, PhD Assistant Professor University of Texas Rio Grande Valley

Pengfei Gu, PhD Assistant Professor University of Texas Rio Grande Valley

Li Zhang, PhD Assistant Professor University of Texas Rio Grande Valley

Ying Lin, PhD Associate Professor University of Houston

Sheteng Niu, PhD Assistant Professor Mayo Clinic

Haozhe Jia, PhD Senior Engineer Xiaomi Corporation

Yawen Wu, PhD Applied Scientist Amazon Web Services (AWS)

Steering Chairs

Liang Zhan, PhD Associate Professor University of Pittsburgh

Li Shen, PhD Professor University of Pennsylvania

Program Committee

Keynote Speakers

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Agenda

Accepted Papers

Photos

Contact

If you have any questions regarding the workshop, feel free to reach out to us:

• Email: haoteng.tang@utrgv.edu (Dr. Haoteng Tang)
• Email: li.zhang@utrgv.edu (Dr. Li Zhang)