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About the symposium
Knowledge in Data Science Symposium brings together the St. Jude community and researchers across the biomedical, clinical and computational sciences to share data-driven research, foster collaboration, and explore innovative approaches to advancing the understanding and treatment of catastrophic pediatric diseases.
This year's theme is Building an Integrated Data Science and AI Ecosystem for Pediatric Discovery, Care, and Innovation.
- Keynote Presentations: Learn from pioneering leaders in biomedical data science as they share groundbreaking research, emerging technologies, and insights shaping the future of the field.
- Research Presentations: Hear from St. Jude researchers as they showcase innovative research, novel methodologies, and real-world applications advancing biomedical data science.
- Interactive Learning: Participate in workshops, poster sessions, and collaborative discussions that foster learning, innovation, and cross-disciplinary collaboration.
- Networking Opportunities: Connect with colleagues from St. Jude and the broader scientific community in breakout sessions to exchange ideas, build collaborations, and expand your professional network.
Abstracts
Data-Driven Scientific Discovery: methods, analyses, and data integration approaches that generate biological and biomedical insight.
Data Science for Clinical Care and Patient Outcomes: applications that improve diagnosis, prognosis, treatment, clinical decision-making, survivorship, and patient experience.
Responsible, Trustworthy, and Reproducible AI: approaches that advance transparency, validation, governance, privacy, security, fairness, reproducibility, and human oversight.
Scientific Computing, Data Infrastructure, and Research Enablement: tools, platforms, pipelines, cloud and high-performance computing, data-sharing environments, and scalable research support.
AI, Machine Learning, and Intelligent Systems: novel AI tools, models, foundation models, agentic systems, automation, multimodal AI, and advanced analytical tools.
Therapeutic and Translational Data Science: work that connects molecular, clinical, imaging, and population data to therapeutic development and real-world impact.
Community, Education, and Workforce Development: training, mentoring, communities of practice, career development, interdisciplinary collaboration, and capability building.
Global, Equitable, and Resource-Aware Data Science: data science approaches designed for global health, diverse populations, limited-resource settings, equitable access, and sustainable implementation.
Overview
Event: 4th Annual Knowledge in Data Science Symposium (KIDS26)
Date: November 16-18, 2026
Location:
St. Jude Children's Research Hospital
Memphis, Tennessee, USA
Virtual option available
Department host: Office of Data Science
Event contact:
Ariel Maclin
ariel.maclin@stjude.org
Eligibility
Accommodations
If you are visiting from out of town, we recommend the following hotels:
The Peabody Memphis – 149 Union Ave, Memphis, TN 38103 (has a shuttle that can take you directly to campus)
The River Inn of Harbor Town – 50 Harbor Town Square, Memphis, TN 38103
Shuttle transportation to and from campus can be arranged for guests staying at either hotel, with service provided at the beginning and end of each symposium day. If you plan to stay at one of these recommended hotels and would like to use the shuttle service, please notify the event contact in advance so transportation can be coordinated.
KIDS advisory committees
Explore Archived Lectures
Our lecture archive features recordings from the 2025 KIDS Symposium, with additional symposium content to be added as it becomes available.
Scenes from KIDS25
Registration deadlines & fees
This symposium will be hosted in a hybrid format, giving you the option to attend in-person or virtually. Registration is free.
ODS: Data Science at St. Jude
The Knowledge in Data Science (KIDS) Symposium is part of the broader mission of the Office of Data Science at St. Jude to accelerate scientific discovery through data science, AI and cross-disciplinary collaboration.