 | Prof. Saman K. Halgamuge (IEEE Fellow)The University of Melbourne, AustraliaProf Saman Halgamuge, Fellow of IEEE, IET, AAIA and NASSL is a Professor of the Department of Mechanical Engineering, The University of Melbourne. Previously, he was a member of the Australian Research Council grant assessment panel and the Head of Engineering School at Australian National University. He obtained the Dipl.-Ing and Ph.D. degrees in data engineering from the Technical University of Darmstadt, Germany. 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 the IEEE Computer Society (2025-27) and was a distinguished Lecturer of IEEE Computational Intelligence Society (2018-21). His research is funded by Australian Research Council, National Health and Medical Research Council, US DoD Biomedical Research program and international industry (e.g. Bosch Germany, Google US). In China, he held visiting appointments at Tong Ji and Hebei universities. Currently he is a visiting professor of SLIIT, UM, UTP and ITB. Abstract: As the major disruptive technology today, Artificial Intelligence (AI), which is the focus of our research group at University of Melbourne, is transforming every sector and therefore every workplace including teaching and learning. The transformation power of AI was not very apparent until the OpenAI released its now famous tool Chat GPT. However, AI encompasses lot more than ChatGPT. In my talk I will introduce and explore the key branches of Machine Learning, summarising the ideas behind other landmark innovations that changed the world (and still changing) in the last 20 years: Convolutional Neural Network and the clever idea of “attention” used in image processing, Transformers that utilised the same clever idea of “attention” giving rise to Large Language models, Physics Informed Neural Networks and its use in Sciences, Graph Neural Networks and how it can incorporate lateral features. New research work in all these areas at our research group including several applications will be presented. Title: Advancements in AI: Changing the Scientific Landscape and Education. |
 | Prof. Shahram Latifi (IEEE Fellow) University of Nevada, Las Vegas Shahram Latifi received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Louisiana State University in 1986 and 1989, respectively. He is a Professor of Electrical Engineering at the University of Nevada, Las Vegas (UNLV), where he also serves as Co-Director of the Center for Information Technology and Algorithms (CITA). For nearly four decades, Dr. Latifi has designed and taught a wide range of undergraduate and graduate courses spanning Computer Science, Computer Engineering, and Electrical Engineering. He is an internationally recognized educator and researcher who has delivered invited keynotes, plenary lectures, and seminars on Machine Learning, Artificial Intelligence, and Information Technology across the globe. Dr. Latifi has authored more than 300 technical publications in networking, AI/ML, cybersecurity, image processing, biometrics, fault-tolerant computing, parallel processing, and data compression. His research has been supported by major federal agencies and industry leaders, including NSF, NASA, DOE, DoD, Boeing, and Lockheed Martin. He has held several prominent leadership roles, including Associate Editor of the IEEE Transactions on Computers (1999–2006), IEEE Distinguished Speaker (1997–2000), Co-founder and Chair of the IEEE International Conference on Information Technology (2000–2004), and Founder and Chair of the International Conference on Information Technology – New Generations (2005–present). Dr. Latifi is the recipient of numerous research awards, most recently the Barrick Distinguished Research Award (2021). In 2020, he was recognized among the top 2% of researchers worldwide, according to the Stanford/Elsevier global citation database. He is a Fellow of the IEEE (elected 2002) and a Registered Professional Engineer in the State of Nevada. Abstract: Over the past two decades, artificial intelligence has advanced at an extraordinary pace. Breakthroughs in Deep Learning, Generative Adversarial Networks, Transfer Learning, and Large Language Models have not merely accelerated progress—they have fundamentally reshaped how we live, work, and make decisions. Nearly every sector has felt the impact: education, healthcare, aerospace, manufacturing, national security, e-commerce, and the arts have been transformed in ways difficult to imagine just a generation ago. Yet with these achievements come serious responsibilities. Whose data trains these systems, and whose voices are missing? How do we protect individual privacy in a world where every digital interaction leaves a trace? And most urgently—how do we ensure that as AI systems grow more autonomous and consequential, humans remain meaningfully in control of critical decisions? This talk offers a concise overview of AI, Machine Learning, and Deep Learning—where the field has been, where it stands, and where it is heading. It examines the technical and ethical challenges of building not just capable AI, but systems that are safe, transparent, and trustworthy. It also surveys national and international efforts to establish responsible AI frameworks—because getting this right is not optional, and the window for shaping it wisely may be narrower than we think. Title: Capable Is Not Enough: The Case for Safe, Transparent, and Trustworthy AI. |