Marimuthu Palaniswami | Professor, Director
University of Melbourne | Australia

Marimuthu Palaniswami, Professor, Director, University of Melbourne

Marimuthu Palaniswami is a Fellow of IEEE and a distinguished lecturer of the IEEE Computational Intelligence Society. He received his Ph.D from the University of Newcastle, Australia before joining the University of Melbourne, where he is a Professor of Electrical Engineering and Director/Convener of a large ARC Research Network on Sensor Networks and IoT (ISSNIP).  Previously, he was a Co-Director of Centre of Expertise on Networked Decision & Sensor Systems. He served in various international boards and advisory committees including a panel member for National Science Foundation (NSF). He has published more than 500 refereed journal and conference papers, including 3 books, 10 edited volumes. 
He was given a Foreign Specialist Award by the Ministry of Education, Japan.  He received University of Melbourne Knowledge Transfer Excellence Award and Commendation Awards. He is an Editor of the Journal of Medical & Biological Engineering and Computing (MBEC) and Subject Editor for the International Journal of Sensors and International Journal of Distributed sensor Networks. Previously, he served as associate editor for IEEE Transactions on Neural Networks. As an international investigator, he is involved in FP6, FP7 and H2020 initiatives in the areas of smart health, smart city and Internet of Things (IoT). He served as General Chair for over 15 IEEE and IEEE sponsored Conferences. He has given several keynote/plenary talks in the areas of sensor networks, IoT and Smart Health.  He has had successful start-ups in smart wearable devices and smart health analytics.


Phar-East Day One @ 16:00

The Internet of Medical Things (IoMT): Deep technology in patient monitoring, electronic disease surveillance and beyond

  • Generating and collecting electronic data and passive records from internet, mobile phones etc.
  • Designing participatory platforms and interactive digital infrastructure to allow better data collection
  • Combining e-health record (EHR), traditional and non-traditional digital data in disease surveillance, prediction and modelling

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