Data Tech 2026 has ended

Data Tech 2026

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9:30am CDT

Accessibility in Data Visualizations: Tips and Tricks (a) TheaterBarbara Monaco, MS • John Zaharick, MS evaluAIte: Where Feedback Meets Intelligence (g) MinnetonkaChristopher Cortes Greene, MS

10:15am CDT

Agent Improv (a) TheaterLuke Schlangen, MS Structure Before Speed: Spec-First Engineering for High-Stakes AI (h) Proverb / EdisonJustin Grammens, MSS

11:00am CDT

Beyond Rows and Columns — Building a Multi-Modal Data Lakehouse with Bedrock Data Automation and Apache Iceberg (g) MinnetonkaGopalakrishnan Marimuthu, MEng Enhancing Credit Risk Monitoring: Integrating Diverse Data Pipelines and Advanced Modeling (h) Proverb / EdisonLinh Nguyen, MS, PhD • Marissa Munley Zen & the Art of Mario Kart Maintenance (c) AlaskaDr. Jimmy, PhD

11:45am CDT

Are TSFMs a Magic Wand? A Deeper Dive into How to Choose the Right Model (h) Proverb / EdisonSruthi Pisipati, MS Right-Sizing AI: Architectural Choices Beyond Model Selection (e) HarrietStephen Boniface • Cameron Lis Marcott, PhD

1:15pm CDT

A Beginner's Guide to Building a Recommendation System with Vector Search (b) CafeteriaAaron Ploetz, MS The Computable Phenotype Library (CPL): Managing computable phenotypes for better real-world evidence (e) HarrietSpencer Stromback, PharmD, MS

2:00pm CDT

LLMs as Data Scientists: What Actually Works (a) TheaterBen Lengerich, PhD, MS Single, Multi, Orchestrator: Choosing the Right Agent Architecture (c) AlaskaAhmed Ashraf Sadek, MS Why Your Model Lies (And What to Do About It): Causal Reasoning in Product Analytics (b) CafeteriaSabin Pradhan, MS

2:45pm CDT

From Microphone to Insight: Extracting Structured Data from Voice (c) AlaskaJaim Zuber Using Utility Theory and Discrete Choice Modeling to Model Revenue Retention (d) NokomisTyler Bybee, MS • Ye Wang, MS

3:30pm CDT

Enhancing Warranty Claims Interpretation with Large Language Models at Trane Technologies (d) NokomisKrishna Jarugumilli, MS • Wendy Foslien, MS From Full Deployments to Selective Releases: Safer CI/CD for Data Pipelines (f) Bde Maka SkaSauhard Bhatt, PGD From Tylenol to Cancer Drugs: Demand Forecasting for 15,000 Drugs at 26 Locations (a) TheaterDavid Bradley, PhD The Calibration Blindspot: Why AI Models Can't See Their Own Errors (c) AlaskaEmmanuel Chea, MPH