2026 TRIADS Spring Symposium
The 2026 TRIADS Spring Symposium will bring together interdisciplinary researchers for a fast-paced session of lightning talks spanning data science, health, AI, and social impact. Presentations will explore topics such as opioid use disorder, adolescent mental health, hypertension disparities, AI trust and bias, environmental health risks, and education outcomes. The event will highlight innovative, real-world applications of data-driven research and concluding with a networking lunch.
The session opens with guest speaker Alex Bradley, Associate Professor of Earth, Environmental, and Planetary Sciences, sharing his insights into how AI is accelerating scientific discovery and helping to translate specialized research to reach a wider audience; followed by TRIADS Seed Teams sharing their projects.
The following TRIADS Seed Teams will present their work:
- Linying Zhang, Project: “Integrating Geographic Information Systems and Electronic Health Records for Scalable Real-World Evidence Generation: A Case Study on Opioid Use Disorder”
- Hannah Szlyk, Project: “Characterizing Patterns of Behavior Among Adolescent Cannabis Users with Co-occurring Depression Symptoms”
- Lindsay Underhill, Project, Project: “Investigating Geographic Disparities in Social and Environmental Determinants of Hypertension the Greater St. Louis Area”
- Zichen Wang, Project: “Machine Learning Using Cardiotocography and Other Intrapartum Data to Predict Birth Outcomes”
- Aysu Ece Sarıcaoğlu, Project: “Confronting the Next Decade of Data-Intensive Astronomy Ushered by LSST”
- Chien-Ju Ho, Project: “Understanding the Facets of Stakeholder Trust in AI Tools for Housing”
- August Li, Project: “In Vitro Neurotoxicity and Socio-Environmental Analysis for Mapping Alzheimer's Disease Risk Due to Particulate Matter Exposure”
- Wouter Kool, Project: “Accounting for human bias to improve AI-assisted decision making”
- Ana Babus, Project: “Measuring Financial Innovation via Natural Language Processing”
- Jason Jabbari, Project: Neighborhood Change, Student Mobility, and School Belonging: Novel Insights Using Advanced Methods and Algorithmic Data Linkages