ICAS2026

ICAS 2026

The International Conference on Applied Statistics 2026 (ICAS 2026)

“Driving the AI Era: The Synergy of Statistics and Data Science”

28-29 May 2026

Bangkok, Thailand

Hosted by

Statistics and Data Science Program, Department of Statistics,

Chulalongkorn Business School, Chulalongkorn University

Introduction

The International Conference on Applied Statistics 2026 (ICAS 2026) will be held on 28-29 May 2026 in Bangkok, Thailand. The theme of ICAS 2026 is “Driving the AI Era: The Synergy of Statistics and Data Science.” The conference is hosted by Statistics and Data Science Program, Department of Statistics, Chulalongkorn Business School, Chulalongkorn University. The conference highlights the important role of applied statistics and data science in addressing real-world challenges and promoting data-driven solutions at both regional and global levels.

This year, the conference focuses on the cooperation between statistics and data science in developing innovative methodologies, enhancing evidence-based decision making, and promoting interdisciplinary collaboration in response to the evolution of artificial intelligence (AI) and its impact on society and the academic world. While the primary emphasis remains on statistics and data science, the conference will also explore the role of AI in advancing analytical methods and complementing traditional statistical approaches to shape the future of research and data-driven decision-making.

Bringing together researchers, academicians, and practitioners from around the world, the conference aims to foster the exchange of ideas, academic experiences, and research findings in the fields of statistics and data science. In addition, it serves as a platform to explore research collaboration opportunities across academic disciplines.

Conference Date: 28-29 May 2026: 9.00 a.m. – 4.30 p.m. (Time zone: GMT+7)

Venue: Onsite Conference at Mandarin Hotel Bangkok, Thailand

Important Dates

Abstract/Full Paper Submissions	Nov 1, 2025 - March 15, 2026 April 15, 2026
Submission Deadline	March 15, 2026 Submission deadline extended to March 31, 2026 Final extension deadline is April 15, 2026
Notification of Abstract/Full Paper Acceptance	Until April 10, 2026 Until April 30, 2026
Notification of Abstract/Full Paper Acceptance	Authors are required to complete their registration and submit the payment by April 20, 2026. May 5, 2026
Conference Registration	Early Bird: Nov 1, 2025 - April 15, 2026
Conference Registration	Regular: April 16, 2026 - May 28, 2026
Distribution of the e-Proceedings	August 2026 (or later)

Call for Papers

The International Conference on Applied Statistics 2026 (ICAS 2026) is an interdisciplinary forum for the presentation of new ﬁndings and research results in the ﬁelds of statistics and data science. Topics of interest for submission include (but are not limited to):

Statistics
Applied Statistics
Data Science
Data Analytics
Business Analytics
Big Data Analytics
Artiﬁcial Intelligence
Machine Learning
Deep Learning
Reinforcement Learning
Operations Research
Actuarial Science
Risk Management

Certificates and Awards

Certificate of Participation
Best Paper Award (5,000 Baht) with Certificate
Student Best Paper Award (3,000 Baht) with Certificate

Keynote Speakers

Professor Xiaodan Fan, PhD
Director, MSc in Advanced Studies in Statistics and Data Science Programme

Department of Statistics

The Chinese University of Hong Kong, Hong Kong
Associate Professor Han-Ming Wu, PhD
Department of Statistics

College of Commerce

National Chengchi University, Taiwan

Invited Speakers

Professor Jung Jin Lee, PhD
Professor in Mathematics.

New Uzbekistan University

Tashkent, Uzbekistan

Founder, eStat Software

Conference Chairperson

Associate Professor Vitara Pungpapong
Director, Statistics and Data Science Program, Department of Statistics,

Chulalongkorn Business School, Chulalongkorn University, Thailand

Conference Advisory Board

Professor Saowanit Sukparungsee, PhD
President of the Thai Statistical Association
Associate Professor Tartat Mokkhamakkul, PhD
Dean, Chulalongkorn Business School,

Chulalongkorn University, Thailand
Associate Professor Janjao Mongkolnavin, PhD
Head, Department of Statistics, Chulalongkorn Business School,

Chulalongkorn University, Thailand

Academic Committee

Professor Jirawan Jitthavech, PhD
Professor Kamon Budsaba, PhD
Professor Winai Bodhisuwan, PhD
Professor Yupaporn Areepong, PhD
Associate Professor Montip Tiensuwan, PhD
Associate Professor Wararit Panichkitkosolkul, PhD
Associate Professor Katechan Jampachaisri, PhD
Associate Professor Piyapatr Busababodhin, PhD
Associate Professor Manad Khamkong, PhD
Associate Professor Autcha Araveeporn, PhD
Assistant Professor Klairung Samart, PhD
Assistant Professor Wuttichai Srisodaphol, PhD
Assistant ProfessorSukuman Sarikavanij, PhD
Assistant Professor Suvimol Phanyaem, PhD
Assistant Professor. Wilasinee Peerajit, PhD
Dr. Bongkoj Wibultananum, PhD

Conference Organizing Committee

Professor Seksan Kiatsupaibul, PhD
Assistant Professor Akarin Phaibulpanich, PhD
Assistant Professor Anupap Somboonsavatdee,PhD
Assistant Professor Suronapee Phoomvuthisarn, PhD
Assistant Professor Nat Kulvanich, PhD
Assistant Professor Nuttirudee Charoenruk, PhD
Assistant Professor Poontavika Naka, PhD
Assistant Professor Narongrid Asavaroungpipop
Dr. Sawitree Boonpatcharanon, PhD
Dr. Tita Vanichbuncha, PhD
Dr. Busayasachee Puang-Ngern, PhD
Dr. Dhiti Osatakul, PhD
Dr. Keerati Suibkitwanchai,PhD

Sponsored by

Google Cloud Logo KBTG_Logo Minitab_Logo

Abstracts and Full Papers Submission

The conference organizers invite submissions of abstracts or full papers in accordance with the provided template files. Submissions should be made through the form on this website below.

The template files for the abstract Abstract_Template_ICAS2026 and full paper Full_Paper_Template_ICAS2026

Guidelines for Submission

Full paper or abstract must be submitted through the form provided on the website below.
An author who is not a student can submit more than one paper. However, each accepted full paper or abstract must be individually registered.
A student can register only one full paper or abstract.
To be published in the conference e-proceedings, the author(s) must submit a full paper and present their work at the conference.

Selected papers will be recommended for further peer review and potential publication in the Thailand Statistician journal (TCI Tier 1, SCOPUS).

Guidelines for Abstract Submission

The abstract must be submitted using the submission form provided on the website below.
The abstract should summarize the context, content, and conclusions of the paper.
Please provide an abstract of 150 to 300 words, along with 3 to 5 keywords.
Instructions are available in Abstract_Template_ICAS2026.
The abstract must be submitted as a PDF file.

Acceptance Notification of Abstract

All submitted abstracts will be reviewed by the program committee according to standard review procedures.
Notification of acceptance will be sent by email a few weeks after abstract submission.

Guidelines for Full Paper Submission

The length of a full paper should not exceed 8 pages. The first page must include the title, author names and affiliations, their email addresses, abstract, and 3 to 5 keywords. Please identify the corresponding author and provide their contact information, including an email address.
Instructions are available in Full_Paper_Template_ICAS2026.
The full paper must be submitted as a PDF file.

Acceptance Notification of Full Paper

All submitted full papers will undergo a peer-review process conducted by three expert reviewers in the relevant field.
Notification of acceptance or conditional acceptance will be sent by email after the full paper submission deadline.
Papers will be accepted for presentation only if at least two reviewers provide positive recommendations and the authors have made revisions based on the reviewers’ suggestions and any additional requirements.

Submit Your Paper – ICAS 2026

Conference Program

Date: 28-29 May 2026

Venue: Mandarin Hotel Bangkok

Thursday May 28th, 2026

8.00 – 9.00	Registration
9.00 – 10.15	Welcome address & opening ceremony Keynote Presentation: “Embracing AI for Bioinformatics Research: Statistician’s Opportunities and Mission” by Prof. Xiaodan Fan, Department of Statistics, Chinese University Hong Kong
10.15 – 10.30	Break
10.30 – 12.00	Keynote Presentation: “S-IGTD: Supervised Image Generator for Tabular Data for CNN-based Deep Learning Multiclass Classification” by Assoc. Prof. Han-Ming Wu, Department of Statistics, National Chengchi University Sponsor Talk: “Beyond the Prompt: Architecting Autonomous AI Agents for Complex Problem Solving” by Mr. Felix Goh, Gemini Adoption Lead for Higher Education, Google
12.00 – 13.00	Lunch
13.00 – 14.30	Parallel Presentation
14.30 – 14.45	Break
14.45 – 16.15	Parallel Presentation
17.00 – 19.00	Evening social event at Chulalongkorn Business School

Friday May 29th, 2026

8.30 – 9.00	Registration
9.00 – 10.15	Special Session (CBS, TSA and CRN session): “Web-based Education for Statistics and Data Science using eStat, R, and Python” by Prof. Jung Jin Lee, School of Business, ADA University, eStat (Founder)
10.00 – 10.15	Break
10.30 – 12.00	Special Talk: “Leveraging InsurTech and Digital Transformation in AI Era for Thailand's Insurance” by Dr. Chupun Gowanit, Chief Executive Officer (CEO): Casmatt Company Limited - Technology Support for Insurance Industry, Chief Transformation Officer (CTO): TQM Alpha PLC, Committee Member of Center of InsurTech Thailand - An Innovation Unit under OIC Sponsor Talk: “Beyond Accuracy: Aligning Statistical Objectives with Human Value-Driven AI in Modern Finance and Healthcare” by Dr. Monchai Lertsutthiwong, Deputy Director, KBTG Labs
12.00 – 13.00	Lunch
13.00 – 14.30	Parallel sessions
14.30 – 14.45	Break
14.45 – 16.15	Parallel sessions

Meet the Speakers

Keynote Speaker

Professor Xiaodan Fan, PhD

Embracing AI for Bioinformatics Research: Statistician’s Opportunities and Mission

Abstract
Statisticians have long been key players in bioinformatics, benefiting from the fact that bioinformatics is a big data area. But Artificial intelligence (AI) has recently excelled with its staggering predictive power in most big data areas including bioinformatics. However, there is still a critical gap between predictive performance and scientific understanding - a gap that the statisticians are uniquely positioned to fill. This talk explores statistician’s opportunities and mission in bioinformatics research. On one hand, we can leverage highly flexible AI architectures to infer complex relationships from heterogeneous datasets that traditional parametric models cannot capture. I will use both a genomic sequence example in virus mutation study and a protein structure example in computational immunology to illustrate our efforts in this direction. On the other hand, it is our mission to integrate core statistical principles with modern AI to produce more accurate and reproducible results. In bioinformatics, where decisions may directly impact human health, uncertainty quantification is especially important. We will discuss how to perform Bayesian uncertainty quantification efficiently for deep learning frameworks.

Biography
Xiaodan Fan is a professor in Statistics at the Chinese University of Hong Kong. He earned a BE in Automation and an MS in Pattern Recognition & Intelligent Systems from Tsinghua University, China, followed by a PhD in Statistics from Harvard University. His research interests encompass probabilistic modeling, machine learning, statistical computing, and their applications in biomedicine. He has served as an associate editor for the Journal of Computational and Graphical Statistics since July 2012 and as an editorial board member for Frontiers in Genetics since April 2015. Additionally, he was a guest editor for the IEEE/ACM Transactions on Computational Biology and Bioinformatics from November 2014 to April 2018. Fan has reviewed for several prestigious journals, including the Journal of the American Statistical Association and Bioinformatics, and has actively participated in organizing numerous academic conferences in his field.

Keynote Speaker

Associate Professor Han-Ming Wu, PhD

S-IGTD: Supervised Image Generator for Tabular Data for CNN-based Deep Learning Multiclass Classification

Abstract
The application of Convolutional Neural Networks (CNNs) to tabular data is fundamentally constrained by the permutation invariance of features, which lack intrinsic spatial locality. While recent methods like the Image Generator for Tabular Data (IGTD) map features onto 2D grids, their unsupervised nature prioritizes global similarity, often preserving non-discriminative noise at the expense of class-specific signals. This paper introduces the Supervised Image Generator for Tabular Data (S-IGTD), a framework that restructures feature rearrangement through between-group correlation matrices to embed discriminative signals into the grid topology. We provide a rigorous theoretical development of the method, establishing the asymptotic consistency of the S-IGTD estimator and deriving specific topological divergence conditions under which the supervised topology strictly enhances the signal-to-noise ratio of local image patches. Empirical validation on simulated and real biological datasets confirms that S-IGTD yields statistically significant improvements over unsupervised topology generators and established baselines. These results elucidate that treating the structural representation of data as a supervised optimization problem enables CNNs to effectively leverage spatial inductive biases, offering a statistically grounded methodology for representation learning of tabular data.

Biography
Han-Ming Wu is an Associate Professor in the Department of Statistics at National Chengchi University, Taipei, Taiwan. He received his Ph.D. in Statistics from National Chiao Tung University and later worked as a postdoctoral researcher at the Institute of Statistical Science, Academia Sinica. Before joining NCCU, he served as a professor in the Department of Statistics at National Taipei University. Prof. Wu has been actively involved in the international statistical community and served as the Chair of the Young Statisticians Committee of the International Statistical Institute (ISI) from 2017 to 2019, during which he organized the 2nd ISI Young Statisticians Workshop (YS-ISI2019) in Kuala Lumpur. He has long been promoting statistical data analysis using the R programming language. His research interests include high-dimensional data analysis, information visualization, microarray data analysis, statistical computing, R/Java software development, machine learning, and symbolic data analysis.

Invited Speaker

Professor Jung Jin Lee, PhD

Web-based Education for Statistics and Data Science using eStat, R, and Python

Abstract
Well-known statistical packages, such as SAS, SPSS, R, and Python, are widely used for Statistics education. They are good for data processing and research but do not have enough modules for Statistics education. A web-based free software at www.estat.me, eStat, specially designed for teaching Statistics. The eStat has numerous simulation modules and web data processing capabilities, including all statistical distributions, the central limit theorem, confidence intervals, hypothesis testing, analysis of variance, nonparametric tests, regression analysis, and forecasting. The eStat is also linked to a web-book system with embedded modules that include many examples, presentation files, and lecture videos. The eStat system has recently been extended to support Data Science education as well as Statistics, encompassing data visualization, supervised learning, and unsupervised learning. The Data Science modules also include R and Python commands as well as eStat for working with data, such as multidimensional tables, principal component analysis, decision trees, Bayes classification, neural networks, and k-means clustering. The demonstration of teaching Statistics and Data Science will be followed by a brief introduction to eStat. The eStat system has been adopted for teaching at several universities worldwide, including Korea, Japan, Azerbaijan, and Uzbekistan, and it has proven very useful for both students and lecturers. We invite scholars to exchange interesting ideas, experiences, and modules to eStat for a desirable Statistics and Data Science education.

Biography
Professor Jung Jin Lee is an accomplished statistician, computer scientist, and educator with more than four decades of contributions to research, teaching, and academic leadership. His expertise spans multivariate statistical analysis, classification methods, data mining, information retrieval, multiple-criteria decision making, and statistical software development. He earned his Ph.D. in Operations Research from Case Western Reserve University, USA. Professor Lee previously served for many years at Soongsil University, Korea, where he held several leadership positions including Chair of the Statistics Department, Dean of Academic Affairs, Vice President for Foreign Affairs, and Dean of the Graduate School. He is currently a Professor in the Department of Mathematics and Advisor to the Rector at New Uzbekistan University. Professor Lee is also recognized as the founder and developer of eStat, an innovative web-based statistical education and analysis system designed to support interactive learning of statistics.

Special Session Speaker

Dr. Chupun Gowanit
Chief Executive Officer (CEO): Casmatt Company Limited
-Technology Support for Insurance Industry
Chief Transformation Officer (CTO): TQM Alpha PLC,
Committee Member of Center of InsurTech Thailand- An Innovation Unit under OIC

Industry Sponsor Speaker

Felix Goh
Gemini Adoption Lead for Higher Education
Southeast Asia & Greater China
Google for Education

Industry Sponsor Speaker

Dr. Monchai Lertsutthiwong
Deputy Director
KBTG Labs

Type of Registration	Conference Registration Fees	Early Bird	Regular
Author Registration	TSA Member	6,000 Baht	7,000 Baht
	Non-TSA Member	8,000 Baht	9,000 Baht
	Student	4,000 Baht	5,000 Baht

Type of Registration	Conference Registration Fees	Early Bird	Regular
Participant Registration	TSA Member	4,000 Baht	5,000 Baht
	Non-TSA Member	6,000 Baht	7,000 Baht
	Student	3,000 Baht	4,000 Baht