Academic Profile

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.
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Asst Prof Mahardhika Pratama
Assistant Professor, School of Computer Science and Engineering

◾Fuzzy Machine Learning
◾Intelligent Control Systems
◾ Evolving and Intelligent Systems
◾ Data Stream Mining
◾ Big Data Analytics
◾ Real World Applications of Computational Intelligence
  • Cyber-Physical Production System (CPPS) - Towards Contextual and Intelligent Response

  • Never-Ending Deep Learning Agent for Non-stationary Data Streams
  • M. Pratama, E. Lughofer, D. Wang. (2017). Online Real-Time Learning Strategies for Data Streams. Neurocomputing, .

  • E.Lughofer, M. Pratama. (2017). On-line Active Learning in Data Stream Regression employing Evolving Generalized Fuzzy Models with Certainty Sampling. IEEE Transactions on Fuzzy Systems, .

  • C. Zain, M. Pratama, E. Lughofer, S.G. Anavatti. (2017). Evolving Type-2 Webs News Mining. Applied Soft Computing, .

  • Y. Zhong, M-J. Er, N. Wang, M.Pratama. (2017). Attention Pooling-based Convolutional Neural Networks for Sentence Modeling. Information Sciences, .

  • E. Lughofer, S. Kindermann, M.Pratama. (2017). Top-Down Sparse Fuzzy Inference Systems Modelling from Data with Improved Coverage. International Journal of Fuzzy Systems, .