The 2026 INTERNATIONAL workshop on

Artificial Intelligence & Cyber Security

INVITED SPEAKERS

Prof. Kaizhu Huang

Duke Kunshan University, China

Title: Bridging Data Augmentation with Model Generalization: Challenges, Methods, and Outlooks

Abstract

Data augmentation and model generalization are fundamental topics in machine learning. This talk presents a systematic overview that bridges these areas from both theoretical and practical perspectives. It examines the theoretical foundations and practical effects of data augmentation on model generalization and robustness, covering local and global augmentation techniques, extensions of adversarial training, large language model (LLM)-based augmentation, and physics-informed strategies. By clarifying the theoretical connections between these approaches and model generalization, the talk also demonstrates their practical value in medical analysis, industrial anomaly detection, and point tracking. The presentation draws primarily on the team’s recent research published at leading AI conferences, including AAAI, NeurIPS, CVPR, SIGGRAPH, and ACL.

Biography

Kaizhu Huang works in trustworthy AI and its applciation in computer vision, large language model and pattern recognition. He is a Full Professor of Electrical and Computer Engineering and Director of the Digital Innovation Research Center at Duke Kunshan University. He earned his PhD in Computer Science and Engineering from The Chinese University of Hong Kong in 2004 and subsequently held research positions at Fujitsu Research Centre, the University of Bristol, and the Chinese Academy of Sciences, and Xi’an Jiaotong-Liverpool University. His honors include the 2024 IEEE ICDM 10-Year Highest-Impact Paper Award, the 2011 Asia-Pacific Neural Network Society Young Researcher Award, and 2006 Fujitsu President Award. He has published more than 300 peer-reviewed papers including 150+ international journal articles, received over nine best-paper, runner-up, or book awards, serves in editorial roles for leading journals and book series. He has delivered over 60 keynote or tutorial talks at international conferences and workshops.

Prof. Saori C Tanaka

Nara Institute of Science and Technology (NAIST) / ATR, Japan

Title: From Sensitive Data to Trustworthy AI: Lessons from Large-Scale Multisite Brain Imaging

Abstract

Recent advances in artificial intelligence increasingly depend on large and diverse datasets. However, in sensitive domains such as neuroscience and healthcare, large-scale data collection poses fundamental challenges in privacy, security, governance, standardization, and cross-institutional data sharing. Brain imaging provides an illustrative example of these challenges: meaningful AI models often require data from multiple institutions and scanner platforms, while individual-level imaging and clinical information must be handled with particular care.

In this talk, I will introduce our efforts to construct and analyze large-scale multisite neuroimaging datasets collected across multiple institutions in Japan. I will first discuss practical challenges in data sharing, including de-identification, metadata management, standardization, quality control, and data governance. I will then describe how heterogeneity across scanners and institutions affects machine-learning models, including problems such as site effects, dataset leakage, and limited generalization to unseen sites.

Building on these large-scale datasets, I will discuss recent efforts toward self-supervised learning and foundation models for brain imaging, and the challenges involved in adapting models trained on large population datasets to heterogeneous clinical and multisite data. Finally, I will consider distributed and federated approaches that allow institutions to collaborate without necessarily centralizing all raw data.

These experiences suggest that developing trustworthy AI requires more than increasing model size or dataset size. It requires an integrated framework in which data, metadata, models, and computational processes can be shared and evaluated securely, transparently, and reproducibly. Large-scale brain imaging therefore offers a useful testbed for understanding broader challenges at the intersection of AI, data sharing, privacy, and cybersecurity.

Biography

Saori C Tanaka is a Professor at the Nara Institute of Science and Technology (NAIST) and Head of the Department of Department of Neural Computation for Decision-Making at ATR Brain Information Communication Research Lab. Group, Japan. Her research focuses on computational behavioral neuroscience, combining computational models of decision-making and learning with neuroimaging and electrophysiological data. She has worked on computational approaches to psychiatric disorders, including obsessive-compulsive disorder. Her recent research also focuses on large-scale multisite brain imaging, machine learning, and foundation models for neuroimaging, with interests in data harmonization, generalization across sites, and secure and reproducible data sharing. She is also involved in national and international initiatives for large-scale neuroimaging databases and collaborative neuroscience.

A/Prof. Nalin Arachchilage

RMIT University, Australia

Title: Building Quantum-Safe Cyber Resilience: Leveraging Agentic AI for Secure PQC Adoption

Abstract

As quantum computing advances, organisations must move beyond algorithm replacement and address the broader challenge of building quantum-safe and cyber-resilient software systems. While Post-Quantum Cryptography (PQC) standards are rapidly maturing, secure adoption depends on how developers, organisations, and software ecosystems implement these technologies in practice. This keynote presents findings from a series of studies examining PQC implementation, migration readiness, and secure software development. Through an empirical study with developers, we identified critical usability challenges in PQC APIs, including complexity, inadequate documentation, and high cognitive demands, all of which can contribute to insecure implementations.
 
Finally, I examine the emerging phenomenon of secure coding drift in AI-assisted development and discuss how Agentic AI can be leveraged to provide continuous guidance, automated security checks, and adaptive learning support. Together, these insights offer a practical roadmap for developing quantum-safe, cyber-resilient, and human-centred software systems.

Biography

Dr Nalin Arachchilage is an Associate Professor in Cyber Security in the School of Computing Technologies at RMIT University, a Regenerative Futures Fellow, and the Group Leader of the Human‑Centred Cyber Security (HCCS) research group within the RMIT Centre for Cyber Security Research and Innovation. His work advances human‑centred cybersecurity and privacy, bringing together technical, behavioural, and policy perspectives to strengthen digital trust. With a strong emphasis on building secure, resilient, and quantum‑ready critical infrastructure systems, Nalin’s research tackles some of the most pressing national and global cyber challenges As a Programme Manager, he contributed to the redevelopment of RMIT’s Master of Cyber Security program, embedding regenerative futures principles to build a sustainable, ethical, and industry‑aligned curriculum.
 
Nalin’s career spans Australia, New Zealand, the United Kingdom, Canada, Vietnam, and Sri Lanka, where he has held senior academic and research roles bridging academia, industry, and government. His previous positions include Senior Lecturer and Assistant Head of School (Research) at the University of Auckland, Senior Research Fellow at La Trobe University’s Optus Cyber Security Hub, and Lecturer at UNSW Canberra at the Australian Defence Force Academy, where he pioneered Australia’s first human‑centred cybersecurity courses for defence personnel. He has also worked as a Postdoctoral Researcher at the University of Oxford and the University of British Columbia, contributing to foundational work in systems security and usable privacy.
 
Nalin has served as an Honorary Associate Professor at the University of Warwick and is currently a Technical Advisor to DEFSAFE Cyber Security Inc. His leadership and international experience position him as a recognised thought leader at the intersection of cybersecurity research, education, and policy, committed to advancing cyber resilience across communities, industry, and national security sectors. 

To be announced soon.