Conference Details

The conference will open at 1:00 on Friday 30 October 2026 and conclude at noon on Sunday 1 November 2026.

Conference Venue

The conference talks will be held in the LITE Center building. LITE stands for Louisiana Immersive Technologies Enterprise. LITE is the home for innovation and technology-based business development in Louisiana. The LITE Center is located in the UL Lafayette Research Park. Visit the LITE web page to learn more (opens in a new window.)

Important Dates

Details regarding registration, financial assistance, and cancelation policies are posted on the actions page.

Registration

1. Early registration (before 1 September 2026): $350
2. Registration (on or after 1 September 2026): $400
3. Early student registration (before 1 September 2026): $150
4. Student registration (on or after 1 September 2026): $175
5. Developing country or “in need” individual registration: $200
6. Fully retired individual registration: $150

The Developing country or “in need” individual registration category was added on Sunday 30 August 2026 and will be available as an option on the registration site later in the day on Monday 31 August 2026. If you have questions about eligibility for this registration category, please use this email link to submit your question.

Financial Assistance

We are pleased to be able to provide partial support to deserving students, postdoc holders, and early career PhD holders. The basic eligibility requirements are:

1. You must be a student (undergraduate or graduate), a postdoc holder, or an early career PhD (≤ 2 years).
2. You must register for and present a talk at the conference.

If your application is approved, we will cover your registration fee. And, subject to funding constraints, we will provide partial lodging support as well.

If you meet these requirements, please submit your abstract, register for the conference, and complete the Financial Assistance Form. Follow this link to the Actions page to complete these tasks.

Submissions

1. Submissions are no longer being accepted.

Cancelation

1. Full refund of registration fees (request must be received before 1 September 2026)
2. Fifty percent refund of registration fees (request must be received before 1 October 2026)
3. No refunds for requests received on or after 1 October 2026

Papers

Sessions

Our plans allow for two types of sessions: Contributed Sessions and Invited Speaker Sessions.

Contributed Sessions

Contributed paper sessions are organized as follows. Five participants, each allotted 20 minutes for presentation and questions, will be grouped by the AISC Program Committee.

Invited Sessions

Invited sessions on special topics will typically have four or five speakers and may include a discussion period.

Schedule (Final - 30 October 2026)

Follow this link to view the schedule.

The schedule (Final - 30 October 2026) is now posted.

Titles and Abstracts

Follow this link to view the titles and abstracts.

The titles and abstracts are now posted (27 October 2026).

Titles and Abstracts for the Friday Poster Session

Follow this link to view the titles and abstracts for the poster only presentations. Some of the speakers will also present their work in the poster session.

The poster only titles and abstracts are now posted (27 October 2026).

Program Booklet (Final - 30 October 2026)

Follow this link to view the program booklet (PDF).

The pdf version of the AISC 2026 program booklet, which will be distributed in print form at the conference, [this pdf has minor revisions to the print version (Final - 30 October 2026)], is linked here for your convenience.

Committees

International Advisory Committee

Our conference Organizing Committee will take advantage of the invaluable guidance provided by the esteemed members of our International Advisory Committee.

Organizing Committee

Plenary Speakers

Katherine Bennett Ensor

Frontiers of Statistics in Science and Engineering: 2035 and Beyond National Academies of Science, Engineering, and Medicine Consensus Study

The National Academies of Sciences, Engineering, and Medicine committee convened to examine the discipline of statistics, its accomplishments, and its future directions, leading to the release of Frontiers of Statistics in Science and Engineering: 2035 and Beyond in February 2026. The project was sponsored by the National Institutes of Health (NIH), the National Science Foundation (NSF), and the National Security Agency (NSA), which gave the committee a three-part charge: assess where the statistical sciences stand today, describe where they need to go, and recommend specifically how the nation should invest to get there. The report's first sentence sums up the committee's case for the field: “Statistics is the science through which data become wisdom.” I was honored to serve as chair of this rock star committee, working alongside vice-chair Lance Waller at Emory University. I will share findings and recommendations as well as a bit about the process and the role NASEM plays in the technology infrastructure of the country.

About the Speaker

John Stufken

Data Subsampling Strategies

Subsampling from large datasets has attracted growing attention over the past decade in both the computer science and statistics literature, leading to the development of a wide range of methods. After a brief high-level overview, this presentation will examine two representative approaches in greater detail: one model-based and one model-free. The model-based method is designed to achieve efficient estimation of model parameters, whereas the model-free approach emphasizes accurate prediction of future observations.

About the Speaker

Karen Kafadar

Real-world Impacts from Statistical Research in Industry, Government, and Academe

Advances in statistics arise often, though not exclusively, through academic research. The problems that motivate these advances can come from researchers in other university departments as well as in industry, government, and public health. In this talk, I will briefly describe research that arose from projects at Hewlett-Packard, National Cancer Institute, and UVA's Dept of Neurophysiology. These examples demonstrate that research in statistics can arise unexpectedly in many scientific disciplines and require theoretical results across the spectrum of statistics to solve real-world problems.

About the Speaker

Hiya Banerjee

The Future of Drug Development: AI as a Strategic Enabler

In large outcomes trials, independent, blinded adjudication of endpoints is an important and expected methodology to ensure standardization of endpoint assessment, particularly for complex or subjective events where the subjectivity often relates to the assessment of the cause of the event (e.g. hospitalization due to heart failure, CV death, MI, stroke). Adjudication is performed by a Clinical Event Committee (CEC) consisting of impartial, clinical experts who review trial events in a blinded fashion (i.e. without knowledge of the treatment allocation) using prespecified clinical definitions which ensure cohesive outcome interpretation across varying local practices and investigator opinions. While critical to study conduct and interpretation, CEC processes rely on manual review which is time consuming, prone to subjectivity, and resource intensive, often accounting for lot of money.

This presentation explores the use of natural language processing (NLP) and artificial intelligence to support and potentially automate cardiovascular endpoint adjudication. We will discuss how AI-enabled adjudication can improve efficiency across clinical trials, observational research, and quality improvement efforts, while also addressing current limitations of automated systems. A novel multi-agent AI framework will be introduced, designed to mirror the structured, stepwise reasoning of human adjudicators by breaking the adjudication process into specialized intermediate tasks.

About the Speaker

S. Ejaz Ahmed

Divide, Shrink, and Conquer: Estimation Strategies for Large-Scale Data

Modern datasets are often too large, high-dimensional, and distributed to be conveniently stored and analyzed in a single location. This creates both computational and storage challenges and motivates statistical methods that can extract and combine information without requiring the full dataset to be centralized. In this talk, I present a divide-and-conquer approach to shrinkage estimation for sparse linear regression. The basic idea is simple: divide a large dataset into smaller subsets, perform estimation locally, and then combine the resulting information through a single round of communication. We develop pretest- and Stein-type shrinkage estimators that exploit sparsity while reducing estimation variability and substantially limiting the amount of information that needs to be stored or transferred. The theoretical properties of the proposed one-shot estimators are established, including consistency and asymptotic normality. Their finite-sample performance is investigated through extensive simulation studies and illustrated using the Million Song Year Prediction Dataset, a large-scale dataset containing audio features from one million contemporary songs. The application considers the challenging task of predicting the release year of a song from its acoustic characteristics, providing a natural setting for evaluating distributed estimation in a high-dimensional data environment. The talk demonstrates how divide-and-conquer and shrinkage can work together to address the statistical challenges created by modern large-scale data: divide the data, shrink intelligently, and conquer the estimation problem.

About the Speaker

Ari Zitin

From Insight to Impact: Agentic AI and the Decision Architecture of Trustworthy Action

As AI systems move from generating insights to executing decisions, the central challenge facing organizations is no longer model accuracy alone, but decision quality at scale. When models act, inference becomes operational. This talk examines how the rise of agentic AI changes the role of statistics, shifting emphasis from insight generation to the design of decision systems that are trustworthy, explainable, and accountable.
Building on recent domain-specific advances in areas such as clinical research and public health, the talk introduces decisioning as the missing architecture that connects statistical rigor to real-world impact. Decisioning defines how model outputs are translated into actions through evidence, constraints, human oversight, and continuous validation. Using illustrative examples across multiple industries, the keynote highlights where statisticians play a critical role as decision architects, shaping uncertainty thresholds, escalation logic, auditability, and long-term monitoring.
The session reframes agentic AI as a decision-system problem rather than a modeling problem, and argues that the future impact of AI depends on how effectively statistical principles are embedded into the systems that act on our behalf.

About the Speaker

Invited Sessions

• Innovations in Survival Analysis for Complex Biomedical Data
  Organizer: Samiran Sinha, Texas A&M University, USA
• Advances in Sampling Theory and Practice
  Organizer: Sat Gupta, University of North Carolina Greensboro, USA
• Statistical Methods and Applications for Time-to-Event, Longitudinal, and Complex Data
  Organizer: Mithun Kumar Acharjee, University of Southern Mississippi, USA
• Health Systems and Morbidity
  Organizer: Md. Shahjahan, Daffodil International University, Bangladesh
• Bayesian and Fiducial Inference
  Organizer: Kalimuthu Krishnamoorthy, University of Louisiana at Lafayette
• Recent Advances in Gini-Based Dependence Measures and Modern Classification Methods
  Organizer: Yongli Sang, University of Louisiana at Lafayette
• Statistical and Machine Learning Methods for Nonstandard Data Structures
  Organizer: Mo Li, University of Louisiana at Lafayette
• Object Data Analysis in the AI Era
  Organizer: Vic Patrangenaru, Florida State University
• Undergraduate Student Research
  Organizer: Steven Miller, Williams University
• A Panoply of Statistical Methods Spanning Inference, Machine Learning, and Bioinformatics
  Organizer: Shesh Rai, University of Cincinnati

Partners

Academic Partners

The Louisiana Chapter of the ASA

We are pleased to announce that the Louisiana Chapter of the ASA will participate in the conference. Please visit this web page to learn more about the Chapter. (This link opens in a new window.)

The University of Louisiana at Lafayette

We are pleased to acknowledge the support of the Office of Research, Innovation, and Economic Development at the University of Louisiana at Lafayette and the University itself for their support of the AISC 2026 and their commitment to support these conferences into the future. Please visit this web page to learn more about the University. (This link opens in a new window.)

Publication Partners

JSTP

We are pleased to announce that the Journal of Statistical Theory and Practice will publish a special issue devoted to papers presented at the conference. Please visit this web page to learn more about the Journal of Statistical Theory and Practice (opens in new window)

Springer

We are also please to announce our connection with the publisher of the Journal of Statistical Theory and Practice. Please visit this web page to learn more about the Springer and their many divisions. (opens in new window)