Data Tech 2026 has ended

Data Tech 2026

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8:00am CDT

Registration, networking & coffee

9:00am CDT

Kickoff (b) Cafeteria

9:30am CDT

LEHRN Panel Part I: The MSP AI and Data Scientist Job Market in 2026: Truths, Trends and (actionable) Takeaways (b) CafeteriaSteve Jewell • Lauren Hernandez • Steve Lewis • Robert Richardson Building an Enterprise Foundation to Support Your AI Journey (c) AlaskaRyan Rosenberg, MBA • Nick Pergande AI, Automation, and Quantum: Three Computing Models Walk into an Enterprise (e) HarrietJason Hunt Accessibility in Data Visualizations: Tips and Tricks (a) TheaterBarbara Monaco, MS • John Zaharick, MS evaluAIte: Where Feedback Meets Intelligence (g) MinnetonkaChristopher Cortes Greene, MS Inverse Molecular Design for Selective Adsorption (h) Proverb / EdisonAndrew Abi-Mansour, PhD Longitudinal Framework for Measuring Long-Term Campaign Impact (d) NokomisSwapnil Deshpande, MS • Nicole Santolalla, MS Startup Showcase & VC Panel (f) Bde Maka SkaGraeme Thickins • Grant Gibson • Garrett Lauderdale, MBA • Michael Peterson, MBA, MSE • Pat Dillon, MBA • CanaryAI • Gudea • Intelligible • Rocket‎‎‎‎ Inventory • SaffronHealth‎‎‎‎ AI • Agent Agnes • Print Profit

10:15am CDT

LEHRN Panel Part II: Now that we know the landscape of the AI and Data Scientist Jobscape, how do we navigate it successfully? (b) CafeteriaSteve Jewell • Lauren Hernandez • Steve Lewis • Robert Richardson Lessons Learned Filling a Supercomputer with Crappy (Manure) Data (e) HarrietNancy Bohl Bormann, PhD Modern Data Stack Meets AI: Real-World Integration for Analytics Innovation (c) AlaskaEric Lealos, MBA • Daniel Engh, PhD Spreadsheet Modeling Design - Case study: Building Robust Retirement Planning Models (g) MinnetonkaRob Rowbotham, PhD Agent Improv (a) TheaterLuke Schlangen, MS Structure Before Speed: Spec-First Engineering for High-Stakes AI (h) Proverb / EdisonJustin Grammens, MSS vLLM - LLM serving with PagedAttention (d) NokomisSona Maniyan, MS

11:00am CDT

Data for Agents: Building the Trusted Data Spine for AI (b) CafeteriaMaz Chaudhri, MBA, MS • Jonathan Weiss, MS Developing a new Grateful Patient score for the Vet Med Center (e) HarrietMatthew Haas, PhD AI-Assisted Engineering in 2026: Workflows, Taste, and What Comes Next (a) TheaterPiers Concord Secure Data Collaboration: Data Cleanroom in Practice (d) NokomisKishor Patil, MCA • Vikas Jaiswal, MS 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

A Game Theorist's Guide to AI Governance (b) CafeteriaMatthew Walz, PhD From Data Strategy to Deal Value: How AI-Ready Data Environments Drive Exit Multiples (c) AlaskaMayank Agarwal SAR Q-Learning for Multi-Regional Inventory Optimization (g) MinnetonkaJong-Min Kim, PhD Score Big with the Right Data Strategy (d) NokomisScotty Hagen 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 Can Enterprise AI Agents Actually Collaborate? Lessons from SimRetail - A Multi-Agent AI Simulator for Enterprise-Scale Retail Assortment Planning (a) TheaterSowmya Podila, MS • Andrew Yang

12:15pm CDT

Lunch (b) Cafeteria

1:15pm CDT

Beyond Technical Skills: Practical Habits for Effective Data Professionals (a) TheaterAnna Kisting Crowd-Sourced Truth (c) AlaskaJohn Hogue, MS From Batch to Streaming: Data Architecture for Context-Aware Personalization (g) MinnetonkaJayakumar Ramalingam Practical Workflow Capture: Reduce Rollout Risk in AI Adoption Initiatives (d) NokomisSteffen Meyer, MBA 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 Flow Based and Score Based Generative Models (f) Bde Maka SkaMehdi Hakim-Hashemi, PhD

2:00pm CDT

High-Signal Networking in a Noisy, AI-Driven Market (f) Bde Maka SkaBenjamin Diaz AI in Drug Discovery (e) HarrietAlex Melton From Black Boxes to Open Glass: Democratizing Marketing Mix Models with GenAI and Open-Source analytics (d) NokomisShakti Kothari, MS TRASH TALKING- Smart Autonomous Connected Refuse Collection Vehicle (g) MinnetonkaBrian Thomas, MBA 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

Why Manufacturing Data Fails to Deliver ROI and How to Fix It (e) HarrietMeade Erickson, PhD False Positives, Real Consequences: Adventures in Government ML (b) CafeteriaRichard Bamattre, PhD • Jessie Roushar From No Code to No Monitoring (f) Bde Maka SkaJuan Carlos Ghiringhelli, MsC Systematic Innovation as Context Architecture: Matrix Morphology and the Future of Model Augmentation (g) MinnetonkaDavid Quimby, MBA When AI Stops Asking and Starts Acting: The Rise of Autonomous Intelligence (a) TheaterTrish Lugtu, MS 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

Beyond the Hype: The Real Missing Layer in Agentic AI Analytics (e) HarrietGeetesh Iyer, MS Physics, Not Clicks: 5 Realities of Industrial Data Science in the Age of Prompts (g) MinnetonkaGirija Parthasarathy, PhD 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

4:15pm CDT

Networking Social Mallards