Professor Fang Chen is a prominent leader in AI/data science with international reputation and industrial recognition and the leader of the Data Science Institute at UTS. She is the winner the 'Oscars' of Australian science, 2018 Australian Museum Eureka Prize for Excellence in Data Science.
She has created many innovative research and solutions, transforming industries that utilise AI/data science. She has helped industries worldwide advance towards excellence in increasing their productivity, innovation, profitability, and customer satisfaction. The transformations to industry with practical impact won her many industrial recognitions including being named as “Water Professional of The Year” in 2016.
She has actively led in developing new strategies, which prioritise the organisation’s objectives, and capitalise on any growth opportunities. She has built up a career in creating research and business plans, and executing with leadership and passion.
In science and engineering, Professor Chen has 300+ refereed publications, including several books. She has filed 30+ patents in Australia, US, Canada, Europe, Japan, Korea, Mexico and China.
Dr. Jianlong Zhou is currently a Senior Lecturer in the School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, leading the UTS Human Centred AI research lab. His current work focuses on ethics of AI, AI fairness, AI explainability, data analytics, visual analytics, behaviour analytics, human-computer interaction, and related applications.
Before joining UTS, Dr. Zhou was a senior research scientist in Data61, CSIRO and NICTA, Ausralia. He has extensive research experiences on various fields ranging from AI, visual analytics, VR/AR, to human-computer interaction in different universities and research institues in USA, Germany and Australia. Dr. Zhou is a leading senior researcher in trustworthy and transparent machine learning, and has done pioneering research in the area of linking human and machine learning. He also works with industries in advanced data analytics for transforming data into actionable operations particularly by incorporating human user aspects into machine learning and translate machine learning into impacts in real world applications.
Adam Berry is an Associate Professor in data innovation, leading work that is focused on the transformation of bits into knowledge through the application of data curation, machine learning, data visualisation and statistical approaches.
Prior to joining UTS in 2019, he was Program Director of the $20m National Energy Analytics Research Program, which focussed on dismantling barriers to energy data access and building insight through the fusion of social science, computer science and electrical engineering. Adam has more than a decade of experience in the energy sector and has designed and delivered demand-side, customer and network research for government, regulators, market operators, retailers and network businesses looking to lead in the transformation of Australia’s energy systems. Adam has worked extensively with energy data from every level of Australia’s electricity distribution system, from sub-metering through to zone substation demand, and has driven ethical and privacy processes that enable a pragmatic, transparent, consumer-focussed, approach to delivery.
Adam is a winner of the inaugural CSIRO Collaboration Medal, recognising exceptional collaboration across disciplines and industry. He is passionate about bringing together teams and industry to unlock new ways of looking at, exploring and extracting ethical and actionable insight from data in the fields of energy, infrastructure and transport.
Dr. Yang Wang is an Associate Professor at the University of Technology, Sydney. He received his Ph.D. degree in Computer Science from the National University of Singapore in 2004. Before joining Data61 (formerly NICTA) in 2006, he was with the Institute for Infocomm Research, Rensselaer Polytechnic Institute, and Nanyang Technological University. His research interests include machine learning and information fusion techniques, and their applications to asset management, intelligent infrastructure, cognitive and emotive computing, medical imaging, and computer vision.
Dr. Zhidong Li received his Ph.D. degree from the University of New South Wales, Sydney, Australia. He received the M.E. degree in computer science from the University of New South Wales in 2006, and the B.S. degree in computer science from the University of Xiamen in 2002. He is currently a senior lecturer in University of Technology Sydney. Before joining UTS, He was a senior engineer in Data61 at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which is the federal government agency for scientific research in Australia. His research interests include machine learning, data mining, pattern recognition, image processing, and Human Computer Interaction.
Dr. Kun Yu is a Lecturer in the Data Science Institute, University of Technology Sydney. His research interests include human cognitive modelling, behavior understanding, human-machine collaboration and learning analytics, with extensive publications in international conferences, journals and books. He has more than 30 patents granted on human-machine interaction. Kun currently leads the Human Performance Analytics team, whose focus is to utilize data science techniques to understand human interactive behaviors. He is serving the distinguished reviewer board of ACM Transactions on Interactive Intelligent Systems (TIIS), and the program committee member for international conferences including IUI, Interact, UMAP etc.
Sunny Verma is a Post Doctoral Research Fellow at Data Science Institute, University of Technology Sydney, and a visiting Scientist at CSIRO, Australia. He received his M.Sc. degree in Applied Operations Research from The University of Delhi, in 2012. He then worked as Senior Research Assistant at Department of Computer Science, Hong Kong Baptist University, HK. His research interests include data mining, bias in machine learning, interpretable machine learning, biometric recognition.