AI School

Date: Tuesday 10 June, 2025

Venue: Ionic Room, Sydney Masonic Centre

Time	Sessions	Lecturers
08:00 - 17:00	Registration
08:25 – 08:30	Introduction to AI School	A/Prof Guanfeng Liu
08:30 – 09:30	Lecture 1: Diffusion models: foundations and innovations	Prof James Kwok
09:30 – 10:30	Lecture 2: Integrating Knowledge Graphs and Large Language Models: Roadmap, Algorithms, and Applications	Prof Shirui Pan
10:30 – 11:00	Tea Break (Doric Room)
11:00 – 12:00	Lecture 3: AI for Biomedicine: Trends, Algorithms and Applications	Prof Vincent S. Tseng
12:00 – 12:30	Lecture 4: LLMs Advancements by Retrieval Augmented Generation	Prof Jae-Gil Lee
12:30 – 13:30	Lunch Break (Doric Room)
13:30 – 14:00	Lecture 4: LLMs Advancements by Retrieval Augmented Generation	Prof Jae-Gil Lee
14:00 – 15:00	Lecture 5: AI for temporal medical data - Time scales and Challenges	Prof Myra Spiliopoulou
15:00 – 15:30	Tea Break (Doric Room)
15:30 – 17:00	Panel: The impact of GAI on our research and education - What should we do now for a better future	Prof Huan Liu & Lecturers
17:00 – 17.05	Closing Remarks	Prof Longbing Cao
18:00 – 20:00	Reception (Marble Foyer, Level 1)

Prof. Huan Liu

Arizona State University, USA

Panel: The impact of GAI on our research and education - What should we do now for a better future

Bio
Dr. Huan Liu is a Regents professor of computer science and engineering with the School of Computing and Augmented Intelligence in the Ira A. Fulton Schools of Engineering. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation.

Prof. James Kwok

Hong Kong University of Science and Technology, China

Bio
James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow.

Prof. Myra Spiliopoulou

Otto-von-Guericke University Magdeburg, Germany

Lecture: AI for temporal medical data - Time scales and Challenges

Abstract
The popularity of AI in healthcare is increasing, foremostly for tasks involving the analysis of images and signal, and for insight acquisition from Electronic Health Records (EHR). The temporal dimension of healthcare data is also often taken into account, whereby we must distinguish among the time scale of signal data (eg life signals in an Intensive Care Unit), the time scale of recordings during a patient's stay in the hospital and the time scale of patient monitoring over months or years. Missingness takes different forms among these scales, and we look at these forms in turn.

The solutions to temporal learning under missingness are conceptually simple to formulate: Ignore, Impute (without being able to verify), Actively ask an oracle (if we find one), Exploit (when the missingness is informative). We look at these options and discuss where they are applicable and what are their limitations.

Bio
MYRA SPILIOPOULOU is Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany. Her main research is on mining dynamic complex data. Her publications are on mining complex streams, mining evolving objects, adapting models to drift and building models that capture drift.

Prof. Vincent Tseng

National Yang Ming Chiao Tung University, Taiwan

Bio
Vincent S. Tseng is currently a Chair Professor at Department of Computer Science in National Yang Ming Chiao Tung University (NYCU). He served as the founding director for Institute of Data Science and Engineering in NCTU during 2017-2020, chair for IEEE CIS Tainan Chapter during 2013-2015 and the president of Taiwanese Association for Artificial Intelligence during 2011-2012. He also acted as the director for Institute of Medical Informatics of National Cheng Kung University during 2008 and 2011. Dr. Tseng received his Ph.D. degree with major in computer science from National Chiao Tung University, Taiwan, in 1997. After that, he joined Computer Science Division of EECS Department in University of California at Berkeley as a postdoctoral research fellow during 1998-1999. He has a wide variety of research interests covering artificial intelligence, big data mining, machine learning, biomedical informatics, mobile and Web technologies, etc. He has published more than 400 research papers in referred journals and conferences as well as 15 patents (held and filed). By Google Scholar, his publications have been cited by more than 16,000 times with H-Index 68. He has been on the editorial board of a number of top journals including IEEE Transactions on Knowledge and Data Engineering, IEEE Journal of Biomedical and Health Informatics, ACM Transactions on Knowledge Discovery from Data, Knowledge Discovery and Data Mining, etc. He has also served as chairs/program committee members for a number of premier international conferences related to data mining and intelligent computing, including KDD, ICDM, SDM, PAKDD, ICDE, CIKM, IJCAI, etc. In recent years, he has also been serving to oversee the architect on Big Data/AI technologies and applications for the governmental and industrial units in Taiwan. Dr. Tseng has received numerous prestigious honors/awards, including World Top 2% Scientist (since 2022), PAKDD Distinguished Service Contribution Award (2024), Pan Wen Yuan Foundation Outstanding Research Award (2023), IICM Medal of Honor (2021), Outstanding Research Award (2019 & 2015) by Ministry of Science and Technology Taiwan, 2019 ACM Distinguished Scientist Member (for Outstanding Scientific Contributions to Computing), FutureTech Award (2023, 2022 & 2018), 2018 Outstanding I.T. Elite Award, and 2014 K. T. Li Breakthrough Award. He is also a Fellow of IEEE and Distinguished Member of ACM.

In terms of the technical achievements, Dr. Tseng has advanced the area of big data mining/machine learning and interdisciplinary applications substantially at both algorithmic and system levels, with solving broad classes of challenging problems and enabling emerging applications. He has made significant contributions to the data mining and intelligent computing community in advancing the area of utility patterns mining and biomedical informatics analytics by not only creating new research lines but also innovating valuable applications. Dr. Tseng has applied the developed data mining/machine learning techniques extensively to interdisciplinary applications, especially in the field of smart medicine. Over more than 20 years, he has led his research team to conduct many important and pioneering research/industrial projects. The developed techniques, including artificial intelligence, big data analytics, machine learning and text mining, have been integrated for early detection/prediction of various kinds of diseases from heterogeneous biomedical data sources like medical images, vital signs, medical notes and genomes. These techniques and platforms have been extensively applied in practical healthcare domains through collaborations with various medical centers/institution in Taiwan, resulting many pioneering publications and practical applications in biomedical and healthcare domains. The representative work includes the risk detection/prediction techniques for Diabetic Retinopathy (collaborated with Industrial Technology Research Institute), which delivered AUC higher than 98% (the highest in the world in this area). The resulted and extended technologies /systems have won a number of prestigious awards, including the National Innovation Award Taiwan (2020-2023), ITRI Yearly Paper Award (2020), and the world-renowned R&D100 Award (2022) and Edison Awards (2022).

Prof. Jae-Gil Lee

Korea Advanced Institute of Science and Technology (KAIST), Korea

Lecture: LLMs Advancements by Retrieval Augmented Generation

Abstract
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for overcoming key limitations of Large Language Models (LLMs). By integrating non-parametric retrieval modules with parametric generation, RAG enables models to dynamically ground their outputs in external knowledge bases, significantly reducing hallucination and improving performance on knowledge-intensive tasks. This lecture provides a deep dive into the RAG architecture, covering its foundational components. We will explore key advanced techniques such as query rewriting techniques and reranking strategies that refine both the retrieval and generation phases for improved end-to-end performance. Furthermore, we will examine cutting-edge developments including active retrieval and self-critique or self-knowledge. The practical relevance of RAG will be highlighted through its integration in production-level systems such as Perplexity. Last, we will also discuss the broader applicability of RAG across domains and its prospective evolution, including its role in agentic systems and real-time knowledge-grounded applications.

Bio
Professor Lee is a Professor at School of Computing, Korea Advanced Institute of Science and Technology (KAIST) and leads the Data Mining Lab. Before that, he was a Postdoctoral Researcher at IBM Almaden Research Center and a Postdoc Research Associate at Department of Computer Science, University of Illinois at Urbana-Champaign.

Prof. Shirui Pan

Griffith University, Australia

Lecture: Integrating Knowledge Graphs and Large Language Models: Roadmap, Algorithms, and Applications

Abstract
Large Language Models (LLMs) such as ChatGPT and Gemini have gained significant attention for their emergent abilities and generalizability. However, as black-box models, they face limitations in capturing and accessing factual knowledge. In contrast, graphs—both as data structures and mathematical modeling tools—offer rich factual information in a structured format, enhancing the inference capabilities and interpretability of LLMs. This lecture will introduce recent research on integrating graphs with LLMs for faithful reasoning. In particular, it will present a graph-enhanced LLM framework called Reasoning on Graphs (ROG), which leverages knowledge graphs to support accurate and interpretable reasoning. ROG follows a planning–retrieval–reasoning paradigm: first, it enables LLMs to generate a plan for retrieving relevant knowledge from a knowledge graph; then, based on the retrieved information, the LLM performs faithful reasoning.
Additionally, the lecture will highlight some recent advancements in graph foundation models, which facilitate zero-shot reasoning across new domains. The session will conclude with a brief discussion on future directions in this rapidly evolving field.

Bio
Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. He is a Co-Director of TrustAGI Lab. Before joining Griffith in 2022, he was Senior Lecturer (Associate Professor) with the Faculty of Information Technology, Monash University. He received his Ph.D degree in computer science from University of Technology Sydney (UTS), Australia.

Pro. Vincent S. Tseng

Chair Professor, National Yang Ming Chiao Tung University (NYCU) Taiwan

Lecture: AI for Biomedicine: Trends, Algorithms and Applications

Abstract
This talk will introduce recent trends and developments of AI techniques for biomedicine, covering the aspects of Highly-precise Diagnostic Support, Multimodal Learning, Early Detection/Prediction, Edge Computing with Wearable/Portable Devices, Non-Invasive Sensing, and Generative AI, through various advanced big data analytics/machine learning techniques on heterogeneous types of biomedical data like medical images, vital signs, electronic health records (EHR), genome, etc. Evaluations and applications in real-world healthcare domains will also be illustrated to show the promising potential of the presented techniques in pushing forward the fields of smart medicine/healthcare. Finally, some underlying challenging issues and open opportunities will also be addressed briefly at the end.

Bio
Vincent S. Tseng is currently a Chair Professor at Department of Computer Science in National Yang Ming Chiao Tung University (NYCU) Taiwan. He served as the Steering Committee Chair for PAKDD (2020-2023), founding director for Institute of Data Science and Engineering in NYCU (2017-2020), Chair for IEEE CIS Tainan Chapter (2013-2015), President of Taiwanese Association for Artificial Intelligence (2011-2012) and the director for Institute of Medical Informatics of National Cheng Kung University (2008 – 2011).