Bio: Professor Frada Burstein is an active researcher with outstanding international and national reputation in decision support and digital health. She leeds pioneering research projects in mobile decision support combining information and knowledge management technologies to address people’s immediate, time-critical information needs. Frada is experienced in multi-disciplinary, innovative research and postgraduate students’ supervision. Frada initiated multiple research projects involving industry, government and community collaborators. She has written many articles co-authored with these collaborators reflecting on the ”lessons learned” from both sides. Originally trained as a computer scientist, her passion is in finding the ways digital technologies can be better designed, implemented and used for long term social impact.
Prof Burstein is a Fellow of Australian Computer Society and a winner of the Victoria State ICT Educator of the Year iAward for her innovations in teaching.
Bio: Albert Bifet is Professor at University of Waikato, and Institut Polytechnique de Paris. Previously he worked at Huawei Noah’s Ark Lab in Hong Kong, Yahoo Labs in Barcelona, and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the leaders of MOA, scikit-multiflow and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2019-2012), and ACM SAC Data Streams Track (2019-2012).
Bio: Divesh Srivastava is the Head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM), the Vice President of the VLDB Endowment, on the Board of Directors of the Computing Research Association (CRA), on the ACM Publications Board and an associate editor of the ACM Transactions on Data Science (TDS). He has served as the managing editor of the Proceedings of the VLDB Endowment (PVLDB), as associate editor of the ACM Transactions on Database Systems (TODS), and as associate Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE). He has presented keynote talks at several international conferences, and his research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India.
Bio: James Baumeister is a AR/VR and human-computer interaction researcher at the University of South Australia’s (UniSA) Australian Research Centre for Interactive and Virtual Environments (IVE). Dr Baumeister’s research incorporates neurophysiological measures of human cognition and performance that stem from a background in both psychology and computer science. His primary interests are in exploring the cognitive impacts of using AR/VR, and brain-computer interfaces.
After being awarded the Mike Miller medal for the most outstanding PhD thesis, Dr Baumeister undertook a postdoctural research fellowship at UniSA. During this fellowship, Dr Baumeister investigated how measured brain activity with EEG can be utilised in real time to distribute tasks to multiple people in a VR environment.
Bio: Yuval Yarom is a Senior Lecturer in the School of Computer Science at the University of Adelaide and a Researcher at Data61, CSIRO. His main research interests are computer security and cryptography, with a current focus on microarchitectural attacks and their mitigation. He received his PhD from the University of Adelaide and an M.Sc. and a B.Sc. from the Hebrew University of Jerusalem.
Bio: Associate Professor Luxton-Reilly has taught over 20,000 students. His teaching approach uses a wide range of innovative practices to improve engagement and learning. He is an active member of the computing education research community. He has made significant contributions to curriculum development and design, and to the scholarship of teaching and learning in the computing discipline.