The 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) Concludes Successfully in Guangzhou, China
From March 6 to 8, 2026, the 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) was successfully held at the Aloft Guangzhou University City. The symposium was jointly hosted by the Artificial Intelligence and Higher Education Research Branch of the Guangdong Provincial Association of Higher Education and Guangzhou University. Centered on big data and applied statistics, the conference focused on cutting-edge advancements and practical applications in key areas such as big data analytics, large language models, intelligent visual inspection, generative intelligence, and industrial big data. The event aimed to establish a pragmatic and efficient international platform for academic exchange, facilitate the sharing of research outputs and scholarly collaboration, and collectively drive the deepening of research and industrial implementation in big data and statistical sciences.
01 Opening Ceremony and Addresses
On the morning of March 7, at the opening ceremony, Mr. Zongwei Luo, Secretary-General of the Artificial Intelligence and Higher Education Research Branch of the Guangdong Provincial Association of Higher Education, and Professor Hongliang Dai from Guangzhou University delivered addresses on behalf of the hosts. They welcomed the attendees and wished the symposium productive and successful outcomes.
02 Keynote Speeches
The keynote session brought together several experienced scholars, offering substantial and forward-looking academic presentations.
Professor Nianyin Zeng from Xiamen University presented intelligent visual defect detection methods for aero-engine core components under complex conditions, exploring the application pathways of intelligent detection technology in high-end equipment manufacturing.
Professor Han Huang from Sun Yat-sen University delivered a report on micro-cost computing, introducing practical solutions for low-cost, high-efficiency AI algorithms across various scenarios.
Professor Hongliang Dai from Guangzhou University focused on generative intelligence for industrial big data, sharing practical frameworks for intelligent industrial data analysis, ranging from adaptive clustering to modified flow fault diagnosis.
Associate Professor Zongwei Luo from Beijing Normal University-Hong Kong Baptist University United International College reviewed the development trajectory of AI models and large language models, analyzing key challenges and future directions in technological evolution.
Associate Professor Azhar Imran Mudassir from Beijing University of Technology discussed computational intelligence in the medical field, offering a rational analysis of the opportunities and challenges in technological deployment.
03 Oral Presentations and Academic Exchange
During the oral presentation sessions, early-career researchers from various universities and research institutions domestically and internationally presented their latest findings. The presentations covered diverse topics including green building innovation cooperation, unsupervised person re-identification, turbine blade defect detection, streaming data learning, multilingual semantic search, XR application optimization, and unmanned aerial vehicle network control. The sessions balanced theoretical innovation with practical engineering applications, fully demonstrating the academic vitality and exploratory spirit of young researchers in the field of big data and applied statistics.
04 Awards and Recognition
Following the oral presentations, the conference held an awards ceremony. The organizing committee presented two Outstanding Paper Awards, two Outstanding Oral Presentation Awards, and two Outstanding Poster Awards. These honors recognized researchers and their achievements that stood out at the conference, encouraging young scholars to continue their dedicated and innovative work.
05 Conference Conclusion
With the successful completion of all scheduled sessions, the 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) came to a fruitful close. The symposium provided a stable and pragmatic exchange platform for researchers in this field, effectively facilitating the sharing of cutting-edge achievements, the collision of academic ideas, and collaborative connections. Participating scholars expressed their hope that this symposium would serve as a new starting point to continue advancing research and translating outcomes in big data and applied statistics, contributing greater strength to digital technology innovation and high-quality industrial development.