Advanced Data and Visual Analytics (ADAVA) Lab
Advanced Data and Visual Analytics (ADAVA) Lab combines expertise from two closely related fields: data analytics and visual analytics. The ADAVA Lab creates new opportunities for advancing research, particularly in the field of healthcare analysis, with a focus on AI-driven diagnostics. This research hub brings together national and international experts in data science and visual analytics from diverse disciplines to explore critical issues in public health, technology policy, and societal well-being.
Data Analytics
We employ a unique, participatory approach to data analytics in developing models for healthcare. This methodology enhances transparency by making both the models and their underlying assumptions more understandable and accessible. Our models are designed to capture the complex and dynamic nature of health and social issues, providing robust, data-driven evidence to inform and support effective decision-making.
Visual Analytics
Visualization and visual analytics have been introduced both in academia and industry: 1) to provide a clear view of users diverse behavior, transactions monitoring, premium fluctuations, and in complex everyday decision-making, 2) to characterize data, user and task, and 3) discovering imbalances and monitoring risk.
From our research group, we continuously publish research papers on these two topics and apply the developed techniques to help our industry partners with their business problems. Examples including anti-selection detection (Zurich One-Path), spam behaviour detection (Toutiao), and product/action recommendations (Yozo,https://www.yozo.com.au/). Specifically, our strengths include
- Identifying misbehaviours or abnormal behaviours from a population
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Discovering the underlying behaviour patterns in a population
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Design a new visual analytics solutions (VAS)
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Recommending actions/items based on the underlying patterns
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Techniques: topic modelling, representation learning and embedding, rule mining, sequential pattern mining, anomaly detection, time series analysis.
Behaviour modelling and social computing
Behaviour modelling and social computing are two fundamental research directions. Behaviour modelling is to derive user behavioural patterns, preferences, profiles from user behaviours and user generated textual data, while social computing is to analyse the social characteristics and trends demonstrated from intra-human or human-computer interactions.