Dr. Mahardhika Pratama
Dr. Mahardhika Pratama received his PhD degree from the University of New South Wales, Australia in 2014. Dr. Pratama is a tenure-track assistant professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He worked as a lecturer at the Department of Computer Science and IT, La Trobe University from 2015 till 2017. Prior to joining La Trobe University, he was with the Centre of Quantum Computation and Intelligent System, University of Technology, Sydney as a postdoctoral research fellow of Australian Research Council Discovery Project. Dr. Pratama received various competitive research awards in the past 5 years, namely the Institution of Engineers, Singapore (IES) Prestigious Engineering Achievement Award in 2011, the UNSW high impact publication award in 2013 and 2014, IEEE TFS prestigious publication award in 2018, Amity researcher award. Dr. Pratama has published in top journals and conferences and edited one book, and has been invited to deliver keynote speeches in international conferences. Dr. Pratama has led five special sessions and two special issues in prestigious conferences and journals. He currently serves as an editor in-chief of International Journal of Business Intelligence and Data Mining and a consultant at Lifebytes, Australia. Dr. Pratama is a member of IEEE, IEEE Computational Intelligent Society (CIS) and IEEE System, Man and Cybernetic Society (SMCS), and Indonesian Soft Computing Society (ISC-INA). His research interests involve autonomous deep learning, data stream, control system, predictive maintenance and autonomous vehicle.
Autonomous Deep Learning
The era of big data in highly complex environments calls for algorithmic development of advanced machine learning techniques and visualizations to transform massive amounts of information into useful references to help decision making process in real-time. This talk aims to discuss online real-time strategies for data stream analytics that provide concrete solutions to unsolved issues in data streams analytics, namely uncertainty in data distribution, uncertainty in data representation, uncertainty in data dimensions, uncertainty in data processing, and uncertainty in data visualization.