Saman Halgamuge
Saman Halgamuge
Bio:
Prof. Saman Halgamuge, Fellow of IEEE and IET, received the B.Sc. Engineering degree in Electronics and Telecommunication from the University of Moratuwa, Sri Lanka, and the Dipl.-Ing and Ph.D. 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 was a distinguished Lecturer of IEEE Computational Intelligence Society (2018-21). He supervised 50 PhD students and 16 postdocs in Australia to completion. His research is funded by Australian Research Council, National Health and Medical Research Council, US DoD Biomedical Research program and International industry. His previous leadership roles include Head, School of Engineering at Australian National University and Associate Dean of the Engineering and IT Faculty of University of Melbourne. He served as a distinguished lecturer of the Computer Intelligence Society. He teaches courses on Algorithms and AI.
Abstracts:
Pushing the boundaries of Machine Learning for applications in Medicine
I will highlight several key deficiencies of Machine Learning that makes it challenging to use in some experimental studies in Medicine. Several new Machine Learning methods we recently proposed are briefly introduced and their applications in medical research are discussed. I will refer to three on-going projects in collaboration with clinicians: image processing in cancer, unsupervised learning applied in research based on organoids and graph neural networks applied in epilepsy.
Can AI be socially responsible?
The 21st century AI needs to be socially responsible and equipped with capabilities to face serious threats like dangerous epidemics and climate emergencies. Several major technical issues hinder the creation of such AI with democratized access that would bring most of this technology to almost all people on Earth. AI used in applications evade regulations in most parts of the world. I will introduce these major technical issues of AI as well as the opportunities current AI can create for the developing world. My talk will focus on two groups of AI applications: well-known applications of AI of value to the planet including the developing world, e.g., Health, Agriculture, Energy, Transportation and Environment and specific applications of AI mostly useful to the developing countries.
Machine Vision and 21st Century AI –our responsibilities for the planet
In this talk, I traverse unmarked territories of machine vision coupled with the conscientious use of 21st century AI keeping our responsibilities for the planet in mind. Imagine a world where every machine has the ability to see, process information, and communicate with other machines and humans. How do we shape this future world of co-existing machines and humans with socially responsible AI? I will discuss two directions of AI our research group is focusing on, which can enable machines to learn and reason fast and also to be transparent about the internal intelligence of AI systems opening up the opportunity for authorities to regulate AI when required.
You may wonder, how well can this vision of AI fit in a world already struggling to curb climate change, socio-economic inequality, and geo-political turmoil? Can Machine vision and 21st century AI help to restore the balance and cure the plane? I will articulate my thoughts in answering those questions.
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