Prof Dr.-Ing Saman Halgamuge, FIEEE

Artificial intelligence (AI) has seen explosive growth over the last 20 years, largely through advances in data-driven machine learning (ML). ML research is contributing to almost every discipline thanks to the advent of new directions including Large Language Models (LLMs), Graph Neural Networks and Physics Informed Neural Networks (PINNs).  AI/ML intelligence is learned from training data in ways unfathomable even by experts, and it is therefore impossible to interpret their decisions, missing the opportunity to have effective human-AI collaboration. When learning from training data that may harbor dangerous prejudices—an inevitable but often concealed presence in our lives—such intelligence hidden in a maze of mathematical constructs amounts to serious ethical, safety, and security challenges for various stakeholders from government to industry. Explainable AI (XAI) aids in reducing such challenges by promising transparency and interpretability enabling stakeholders to gain a greater understanding of the AI model's decisions. This explanation should be made available using one or more of the modalities, e.g., textual, mathematical.

Biography:

Prof Saman Halgamuge, Fellow of IEEE, IET, AAIA and NASSL received the B.Sc. Engineering degree in Electronics and Telecommunication from the University of Moratuwa, Sri Lanka, and the Dipl.-Ing and Dr.-Ing. degrees in data engineering from the Technical University of Darmstadt, Germany. He is currently a Professor of the Department of Mechanical Engineering of the School of Electrical Mechanical and Infrastructure Engineering, The University of Melbourne. He is listed as a top 2% most cited researcher for AI and Image Processing in the Stanford database. He is a distinguished Visitor appointed by IEEE Computer Society (2024-26) and was a Distinguished Lecturer appointed by IEEE Computational Intelligence Society (2018-21). He supervised 50 PhD students and 16 postdocs on AI and applications in Australia to completion. His publications can be viewed at https://scholar.google.com.au/citations?hl=en&user=9cafqywAAAAJ&pagesize=80&view_op=list_works&sortby=pubdate