Domain of Research
Machine Learning and Data mining, Big Data, Distributed Systems and Cloud Computing, Robust Agent Systems, Intrusion Detection, Bioinformatics and Medical Imaging, Logic Programming and Deductive Databases, Information Retrieval
Professor Ramamohanarao (Rao) Kotagiri received PhD from Monash University in 19080. He was awarded the Alexander von Humboldt Fellowship in 1983. He has been at the University Melbourne since 1980 and was appointed as a professor in computer science in 1989. Rao held several senior positions including Head of Computer Science and Software Engineering, Head of the School of Electrical Engineering and Computer Science at the University of Melbourne and Research Director for the Cooperative Research Centre for Intelligent Decision Systems. He served on the Editorial Boards of the Computer Journal Universal Computer Science, IEETKDE and VLDB (Very Large Data Bases) Journal. He was the program Co-Chair for VLDB, PAKDD, DASFAA and DOOD conferences. He is a steering committee member of IEEE ICDM, PAKDD and DASFAA. He received Distinguished Contribution Award by PAKDD for Data Mining; Distinguished Contribution Award in 2009 by the Computing Research and Education Association of Australasia; Distinguished Contribution Award by DASFAA for Database Research; Distinguished Service Award by IEEE ICDM for Data Mining. Rao is a Fellow of the Institute of Engineers Australia, a Fellow of Australian Academy Technological Sciences and Engineering and a Fellow of Australian Academy of Science.
Keynote Title: SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator
Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the finest level of detail. My talk will highlight major challenges faced in microscopic simulations due to the complexity of the traffic models. I will describe SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator, we are currently developing. It is a distributed microscopic traffic simulator that can achieve significant boost of simulation speed by utilising network-connected computing nodes in parallel. For example, the time for simulating one million vehicles in an area the size of Melbourne is 1:67 times faster than the real time when the traffic is updated once per second with 30 computing nodes. SMARTS supports a number of driver models and traffic rules, such as car-following model and lane-changing model, which can be driver dependent. The simulator is equipped with a wide range of features that help to customise, calibrate and monitor simulations.