Join global thought leaders as they share insights on cutting-edge research, ethical AI, and data-driven innovation for societal progress.
Vice President, East China Normal University
Founding Dean, School of Data Science and Engineering
Professor of Applied Informatics, Victoria University
Data Mining & Health Informatics Expert
Professor of Computer Science, Singapore Management University (SMU)
Director, Artificial Intelligence & Data Science Cluster
Associate Professor, Trustworthy Data Science & AI Lab, Science Tokyo
Trustworthy AI & Data Privacy Researcher
Explore insights from global thought leaders at the forefront of AI, data science, and ethical innovation. Our esteemed speakers bring expertise in big data, machine learning, social analytics, and trustworthy AI.
Vice President of East China Normal University,
Founding Dean of the School of Data Science and Engineering,
Professor of East China Normal University
Speaker Bio:
Professor Zhou obtained a Bachelor’s degree in Computer Applications and a Master’s degree from Chengdu University of Science and Technology (now Sichuan University) in 1985 and 1988, respectively, and received a Ph.D. from the Department of Computer Science at Fudan University in 1993. He has been selected as the principal investigator for the National Outstanding Youth Fund and a distinguished professor under the Changjiang Scholars Program.
Currently, he serves as a member of the 7th Academic Evaluation Group of the State Council, Vice Chairman of the Database Professional Committee of the China Computer Society, Fellow of the China Computer Society, and Associate Editor of the Journal of Computer Science. He has previously served as the Chair of the ER’2004 Conference, Vice Chair of the Program Committee for ICDE’2009 and ICDE’2012, and Co-Chair of the Program Committee for VLDB’2014.
Title:
The Impact of AI to Education and Our Strategies
Abstract:
The current Artificial Intelligence is essentially data-driven AI, so called data intelligence. The surge in data intelligence has brought profound insights, prompting us to rethink our understanding about science and data. AI itself is a new technological revolution, characterized by two significant features: first, the development of science and technology has shifted from the traditional “science leading technology” to “technology driving science”; second, the “new empiricism” calls for “new rationalism,” with new sciences on the horizon. Data is a new power; data is to digital transformation just likes what electricity is to electrification, data will usher humanity into the digital civilization. In the AI era, there is a need for new educational philosophies, with innovation becoming the theme of education. The digital transformation of education presents an opportunity for its development, and data empowering and technology boosting is a prerequisite for achieving REally Smart Online Learning (RESOLE) platforms.
Professor of Applied Informatics,
Victoria University
Speaker Bio:
Professor Zhang is an international research leader in databases, data mining, health informatics, web information systems, and web services. He has published over 300 research papers in international journals and conferences proceedings, and authored/edited 20 books.
His research has been supported by a number of Australian Research Council (ARC) linkage projects and discovery project grants. His research has made significant impacts on society. For example, the multidisciplinary research into e-health has produced software systems and mapping tools to assist relevant government/industry organisations establish health needs, allowing the development of policy based on firm evidence.
Title:
Smart Medicine: Medical Data Analysis / AI Applications for Patient Monitoring, Disease Diagnosis，Prediction and Health Management
Abstract:
Recent development or maturation of big data analysis and AI technology has impacted many areas. As one of the most promising areas, Health care and medical service is now becoming more data-intensive, evidence-based and AI-guided.
In this talk, we will introduce several innovative data mining techniques and case studies to address the challenges encountered in e-health and medical big data. This includes techniques and development on medical data streams, correlation analysis, abnormally detection and risk predictions, including diagnosis of sleeping and mental health. We will also discuss the future directions of applying AI in Medicine and Health research.
Professor of Computer Science,
Director, Artificial Intelligence & Data Science Cluster,
School of Computing and Information Systems,
Singapore Management University (SMU)
Speaker Bio:
Professor Lim received the Ph.D. degree from the University of Minnesota, Minneapolis, MN, USA. His research expertise include social media mining, social or urban data analytics, and information retrieval. He is currently the Lee Kong Chian Professor with the School of Computing and Information Systems, Singapore Management University, Singapore. He is also the Director of Living Analytics Research Centre in the School, a research centre focusing developing personalized and participatory analytics capabilities for smart city and smart nation relevant applications. He was the recipient of the Distinguished Contribution Award at the 2019 Pacific Asia Conference on Knowledge Discovery and Data Mining, and the Test of Time Award at 2020 ACM Conference on Web Search and Data Mining.
Title:
Harnessing AI for Digital Health
Abstract:
Artificial Intelligence (AI) has rapidly transformed how we interact with technology, fueled by advancements in Deep Learning (DL) and Large Language Models (LLMs). While these breakthroughs have garnered significant attention across academia, industry, and society, their full potential is still unfolding, with many application-inspired research challenges awaiting exploration. In this keynote, we will walk through the transformative role of AI in digital health, where cutting-edge research is bridging technology and well-being to build a smarter, healthier future. Focusing on critical domains like food health and mental health, I will share examples of AI technologies, including food image recognition, food segmentation, and AI-powered mental health counseling. We will also explore the unique challenges in data and model development, offering insights into innovative solutions that address these hurdles. Finally, I will outline future research directions to inspire new possibilities at the intersection of AI and digital health, reshaping the landscape of health and wellness.
Associate Professor, TDSAI Lab, Department of Computer Science,
Institute of Science Tokyo
Speaker Bio:
Dr. Cao is an Associate Professor at the Department of Computer Science, Institute of Science Tokyo (Science Tokyo, formerly Tokyo Tech), and directing the Trustworthy Data Science and AI (TDSAI) Lab. He is passionate about studying and teaching on algorithmic trustworthiness in data science and AI. Two of his papers on data privacy were selected as best paper finalists in top-tier conferences IEEE ICDE 2017 and ICME 2020. He was a recipient of the IEEE Computer Society Japan Chapter Young Author Award 2019, Database Society of Japan Kambayashi Young Researcher Award 2021. His research projects were/are supported by JSPS, JST, MSRA, KDDI, LINE, WeBank, etc.
Title:
Privacy in Fine-tuning and Prompting for Large Language Models: Attacks, Defenses, and Future Directions
Abstract:
Fine-tuning and Prompting have emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performance across various domains. However, the fine-tuning and prompting process often involves sensitive datasets, introducing privacy risks that exploit the unique characteristics of this stage. In this tutorial, I will provide a comprehensive view of privacy challenges associated with fine-tuning and prompting LLMs, highlighting vulnerabilities to various privacy attacks, including membership inference, data extraction, and backdoor attacks. We further review defense mechanisms designed to mitigate privacy risks, such as differential privacy, federated learning, and knowledge unlearning, discussing their effectiveness and limitations in addressing privacy risks and maintaining model utility. By identifying key gaps in existing research, we highlight challenges and propose directions to advance the development of privacy-preserving methods for leveraging LLMs, promoting their responsible use in diverse applications.
