Saman Halgamuge, University of Melbourne, Australia
Prof. Saman Halgamuge is currently a visiting Professor at the School of Mechanical and Aerospace Engineering of Nanyang Technological University of Singapore. He is on sabbatical leave from University of Melbourne where he is a Professor of the Department of Mechanical Engineering and a member of the school wide initiative on Biomedical Engineering. He was an Associate Professor and Reader (2002-2008) and Senior Lecturer (1997-2001) in the same Department. He is also the Assistant Dean (International) in the Melbourne School of Engineering. Saman has received his PhD from Technical University of Darmstadt in Germany in 1995.
Saman is the co-author of over 200 research papers, 8 books and 15 book chapters with over 2500 citations and h-factor of 21. In Melbourne, he has completed supervision of 18 PhD students and 5 Masters by Research students. He serves on the editorial boards of 6 journals including ACTA journal on Control and Intelligent systems and BMC Bioinformatics. He has chaired 12 conferences and served as a member of about 70 conference program committees.
Saman leads a research group working on Pattern Recognition and Optimization looking into problems in Mechatronics, Biomedical Engineering and Sustainable Energy. His research breakthroughs include: highly cited work in Evolutionary Optimization, Sensor Scheduling; Protein Motif extraction; applied research in marker gene identification from cancer data; and the development of an "artificial brain" that can grow with various levels of experience and articulate learned knowledge as a set of rules.
Learning from Ants and Bees: Nature Inspired Problem Solving
In the first part, the focus is on “swarm algorithms” that are based on the use of food searching behaviours of ants or bees. Examples will be shown on how these algorithms extended by the group of the speaker are used in sensor networks and in the development of vehicle engines. In the second part, the concept and the algorithm development on near unsupervised learning and the application in various biological data mining problems are discussed.