2026 IEEE BIBM Workshops
The 2nd International Workshop on Trustworthy AI for Biomedical Discovery
A full online forum for reliable, interpretable, reproducible, and generalizable AI methods that can move biomedical discovery from promising models to meaningful research impact.
- Style
- Full online
- Workshop Dates
- Dec. 1-4, 2026
- Submission Due
- Sept. 27, 2026
Background
Trustworthy AI for real biomedical research settings
Artificial intelligence has become a major driving force in biomedical discovery, enabling advances in drug discovery, disease modeling, biomarker identification, and multi-omics data analysis. Despite rapid progress, many proposed AI models remain difficult to trust and adopt in real biomedical research settings.
Key challenges include heterogeneous and multimodal biomedical data, limited interpretability, insufficient robustness, poor reproducibility, and weak generalizability across datasets and tasks. Trustworthy AI addresses these challenges through reliable evaluation, transparent and biologically meaningful modeling, and careful validation in realistic biomedical applications.
This workshop brings together researchers and practitioners across artificial intelligence, bioinformatics, computational biology, and biomedical science to discuss methods, benchmarks, evaluation frameworks, data integration strategies, and applications in biomedical discovery.
Scope
Research Topics
AI for drug discovery, therapeutic design, and drug response prediction
AI for disease modeling, disease mechanisms, and biomarker discovery
Multi-omics data integration and analysis
Multimodal learning for biomedical data
Foundation models, large language models, and generative AI for biomedicine
Knowledge-guided and literature-informed AI for biomedical discovery
Causal learning and biologically informed AI methods
Model evaluation, benchmarking, and reproducibility in biomedical AI
Interpretability, explainability, robustness, and uncertainty estimation
Graph learning, molecular representation learning, and network-based biomedical modeling
Privacy-preserving, federated, ethical, and responsible AI for biomedicine
Schedule
Important Dates
All dates are from the workshop proposal document.
- Due date for full workshop papers submission
- Notification of paper acceptance to authors
- Camera-ready of accepted papers
- Workshops
Leadership
Program Chairs
Dr. Wenjing Yin
Tianjin Chengjian University, China
Wenjing Yin received her Ph.D. degree from China University of Petroleum, China. She is currently an Assistant Professor with the School of Computer and Information Engineering, Tianjin Chengjian University, China. Her research interests include bioinformatics, AI for biomedical data analysis, and molecular biocomputing circuits.
She has published papers in venues such as IEEE IoTJ, IEEE JBHI, IEEE TCE, and JCIM, and has been actively engaged in interdisciplinary research and academic activities related to bioinformatics and AI-enabled biomedical analysis.
Dr. Yuanyuan Zhang
Qingdao University of Technology, China
Yuanyuan Zhang received the PhD degree from Xidian University, Xi'an, China. She is an Associate Professor in the School of Information and Control Engineering at Qingdao University of Technology. Her research interests focus on computational bioinformatics, AI in drug discovery, and Human-Machine Interaction.
She has published over 40 papers in journals such as IEEE JBHI, FGCS, JCIM, KBS, EAAI, BIB, CMPB, and BSPC. She has led national and provincial research projects and served as Guest Editor for a special issue on integrating multi-omics data for cancer diagnosis and treatment.
Program Committee
Committee Members
- Shudong Wang China University of Petroleum, China
- Shams Al Ajrawi San Diego State University, USA
- Zhihan Lyu Xidian University, China
- Sunil Prajapat Gachon University, Republic of Korea
- Jinxing Liu University of Health and Rehabilitation Sciences, China