The 24th ASA-CT Mini-Conference

The conference will be hybrid, with in-person sessions held at DWTN 109 (A1), University of Connecticut-Stamford Campus, 1 University Pl, Stamford, CT; and all sessions broadcast live via Zoom.

This annual event is organized by the Connecticut Chapter of the American Statistical Association (ASA-CT). We aim to

• engage our current and prospective ASA members;
• discuss emerging topics in statistics and data science;
• provide a platform to connect statisticians and data scientists in the State of Connecticut and beyond and to promote statistics and data science to the next generation.

This conference promotes discussion on the evolving roles of AI and Bayesian methods in innovative decision-making. It highlights the growing synergy between modern statistical methodology and AI-driven technologies to address complex, real-world challenges. It also emphasizes how Bayesian approaches support transparent uncertainty quantification, adaptive learning, and evidence-based decisions, as reflected in recent guidance from the U.S. Food and Drug Administration encouraging their use in clinical research. By fostering dialogue across various sectors, including industry, academia, and government, the conference aims to advance responsible innovation and strengthen data-driven decision-making across fields.

For more details of the conference, please refer to:

https://asa-ct.github.io/miniconf2026/

Ticket price:

$25 (online) or $50 (in person) for ASA Chapter members

$45 (online) or $70 (in person) for non-ASA Chapter members

Note: an Eventbrite fee will be added to the ticket price.

All times are in EDT.

• 8:00 am – 9:00 am: Registration
• 8:00 am – 9:00 am: Refreshment
• 9:00 am – 9:10 am: Opening Remarks:
• • Birol Emir, ASA CT Chapter President
  • Ming-Hui Chen, University of Connecticut
• 9:10 am – 12:00 pm: Scientific Session I
• • 9:10 am – 9:50 am: Sheraz Khan, Pfizer
  • • Automating Clinical Trial Regulatory Documents with Agentic AI: Generation and Evaluation of SAPs, CSRs, and LoTs
  • 9:50 am – 10:30 am: Yanxun Xu, Johns Hopkins University
  • • Closing the Loop in Evidence Synthesis: High-Fidelity IPD Reconstruction from Kaplan-Meier Plots and Robust Inference
  • 10:30 am – 11:10 am: Jingfeng Zhang, Insilicom LLC
  • • From Hallucination to Trust: Building Knowledge Graphs for Reliable and Explainable AI in Biomedical Discovery
  • 11:10 am – 11:50 am: Pending
  • • Pending Title
• 12:00 pm -– 1:00 pm: Lunch
• 1:00 pm – 3:45 pm: Scientific Session II
• • 1:00 pm – 1:40 pm: Pending
  • • Pending
  • 1:40 pm – 2:20 pm: Meizi Liu, Takeda
  • • BEAM: Bayesian Hybrid Design With Adaptive Sample Size Through Multisource Exchangeability Modeling
  • 2:20 pm – 3:00 pm: Ming-Hui Chen, University of Connecticut
  • • Quantifying Prior Information via Kullback-Leibler Divergence: A New Perspective on Effective Sample Size
• 3:00 pm – 3:45 pm: Panel discussion on draft FDA guidance
• • “Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products”
• 3:45 pm – 4:00 pm: Closing Remarks: Junxian Geng, ASA CT Chapter Vice President

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