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 an Assoicate Professor in the Data Science Institute/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, China, and Australia. Dr. Zhou is a leading 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 in different sectors such as agriculture and health.
Professor Andreas Holzinger leads the Human-Centered AI Lab (HCAI), University of Natural Resources and Life Sciences, Vienna, Austria, and is a visiting professor at the Alberta Machine Intelligence Institute in Edmonton, Canada. Andreas pioneered in interactive machine learning with the human-in-the-loop. For his achievements he was elected a member of Academia Europea in 2019, the European Academy of Science. He is member of the European Laboratory for Learning and Intelligent Systems (ELLIS) since 2021. He is paving the way to multimodal causability, promoting robust, interpretable and trustworthy medical AI. He advocates a synergistic approach to put the human-in-control of AI, and align AI with human values, ethical principles and legal requirements, ensuring privacy, security, and safety.
Prof. Biecek's personal mission is to enhance human capabilities by supporting them through access to data-driven and knowledge-based predictions. He executes it by developing methods and tools for responsible machine learning, trustworthy artificial intelligence and reliable software engineering. He is a full professor at Warsaw University of Technology and the University of Warsaw. He graduated in software engineering and mathematical statistics and now work on model visualisation, explanatory model analysis, predictive modelling and data science for healthcare. In 2016, he formed the research group MI2 which develops methods and tools for predictive model analysis
Dr. Yang Wang is a 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 an Associate Professor 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 Senior 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.
Personalised feedback in E-learning.
Uncertainty Management in Machine Learning.
Cognitive Machine Learning.
AI Fairness and Visual Analytics.
Conversational Agent in health coaching systems.
Trust and AI explanation quality.
Anomaly detection and digital finance.
Robustness of machcine learning.
AI ethics in digital marketing.
Blockchains in digital finance.
AI interpretability.
AI security.
AI ethics in smart homes.
AI for beehive health monitoring.
- Yiqiao Li, PhD, 2024, thesis title: Faithful and Fair Generative Explainers for Graph Neural Networks
- Boyuan Zheng, PhD, 2024, thesis title: Explaining Imitation Learning: Potentials in Preprocessing, Language for Explanations and Frame-wise Importance
- Wenhao Zhuo, MPhil, 2024, thesis title: A Decentralized Multi-Agent Online Planning Algorithm and Application on Smart Grid
- Jamie Wu, MPhil, 2024, thesis title: Situation Awareness-Based Evaluation Framework for Quality of Machine Learning Explanations
- Brett A. Hansard, MPhil, 2024, thesis title: Mundus Intelligibilis: Mitigating the ethical considerations of Artificial Intelligence through Visual Analytics