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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

Exterior view of LITE

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 (preliminary - watch for updates)

Follow this link to view the preliminary schedule.

The preliminary schedule (30 September 2026) is now posted. Please check back for updates.

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.

Open this tab to view the Advisory Committee member list.

Organizing Committee

Open this tab to view the Organizing Committee member list.

Additional details are coming soon! Please check back for updates.

Plenary Speakers

Katherine Bennett Ensor

Katherine Ensor
Katherine Bennett Ensor
Professor, Rice University

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

About the Speaker

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Dr. Katherine Bennett Ensor is the Noah G. Harding Professor of Statistics at Rice University where she serves as director of the Center for Computational Finance and Economic Systems. Ensor, a leading national voice in artificial intelligence and data science, develops statistical methods for practical problems with specific interests in finance, energy, environment, health, community and risk analytics. Ensor served as the 117th president of the American Statistical Association and is a fellow of ASA, AAAS, and an elected member of ISI. She has been recognized for her leadership, scholarship, and mentoring and, in 2021, was inducted to the Texas A&M College of Science Academy of Distinguished Former Students. In 2024 she was honored with the ASA Founder’s Award, the highest honor given by ASA to its members. She holds a Ph.D. in Statistics from Texas A&M University, and an M.S. and B.S.E. in Mathematics from Arkansas State University.

Visit Katherine Ensor's Rice University webpage to learn more (opens in a new window.)

John Stufken

John Stufken
John Stufken
Professor, George Mason University

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

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John Stufken is an expert in experimental design and data science, known for his work on orthogonal arrays, crossover designs, and optimal design methods. He has authored influential books and held leadership roles at major universities and the NSF. Stufken is an Elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Visit John Stufkin's webpage to learn more (opens in a new window.)

Karen Kafadar

Karen Kafadar
Karen Kafadar
Commonwealth Professor, University of Virginia

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

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Professor Kafadar is a leading expert in exploratory data analysis and robust methods. Her research focuses on uncertainty characterization in physical and biological sciences, earning awards from the CDC, ASA, and American Society for Quality. She served as the 114th president of the American Statistical Association and was elected an ASA Founder for her outstanding leadership. She also served as president of the International Association for Statistical Computing.

Visit Karen Kafadar's profile page at the University of Virginia to learn more (opens in a new window.)

Hiya Banerjee

Hiya Banerjee
Hiya Banerjee
Senior Director, Eli Lilly and Company

The Future of Drug Development: AI as a Strategic Enabler

About the Speaker

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Dr. Hiya Banerjee is a Senior Director at Eli Lilly and Company, where she focuses on external engagement, statistical innovation, and implementation in clinical drug development. She has extensive experience in cardiometabolic diseases and oncology, with expertise in survival analysis, causal inference, missing data methodologies, and real-world evidence. Dr. Banerjee is actively involved in advancing statistical collaborations across academia, industry, and regulatory agencies. She serves in leadership roles within the American Statistical Association Biopharmaceutical Section and the International Indian Statistical Association, and is passionate about fostering innovation and mentoring the next generation of statisticians and data scientists.

S. Ejaz Ahmed

Ejaz Ahmed
S. Ejaz Ahmed
Professor, Brock University, Ontario, Canada

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

Open this tab for some information about the speaker.

An update of this biosketch will be posted shortly.

Dr. Ejaz Ahmed is professor of Mathematics and Statistics in the Faculty of Mathematics and Science. Before joining Brock, he was a professor and head of Mathematics at the University of Windsor and University of Regina. Prior to that, he had a faculty position at the University of Western Ontario. Further, he is a Senior Advisor to Sigma Analytics (Data Mining & Research), Regina.

He is an internationally known scholar and an established researcher. Dr. Ahmed's research interests concentrate on big data, predictive modeling, data science, and statistical machine learning with applications in many walks of life. His research has been supported by a variety of grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada since 1987, the Canadian Institute of Health Research, Ontario Centre for Excellence (OCE) and other sources throughout my academic career. Importantly, his NSERC grant was renewed in 2017 for another five years, with “Outstanding (O)” in all three categories. According to that year’s competition statistics, only two applications overall from ALL small universities received a ranking of “OOO” or higher.

Ari Zitin

Ari Zitin
Ari Zitin
SAS Institute, North Carolina

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

Open this tab for some information about the speaker.

Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low-energy physics data to better understand the neutrino. He taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, he has worked to develop courses on agentic AI, quantum computing, and optimization.

There are more details to come! Please check back for updates.

Sessions

We are currently organizing contributed and invited sessions covering a wide range of contemporary research areas. The invited session titles and organizers will be posted below as they are finalized.

More details about the sessions are coming soon, so please check back for updates.

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)