November Panel Series: Novel approaches to tackling complex data
Wednesday 9 November, 11am to 12.30pm
This is a hybrid event:
- In person at the Manhari Room at Melbourne Connect – for those attending in person, the panel will be followed by lunch and informal networking.
- Online via Zoom. The Zoom link will be provided after registration.
Please choose the correct ticket type when registering!
As the volume of data collected for research continues to grow at a rapid pace, understanding and dealing with its complexity becomes crucial. Complexity looks different across disciplines and can emerge from working with big data, using new techniques and technologies, or combining multiple data sources.
To explore these topics, Melbourne Data Analytics Platform (MDAP) and the Melbourne Centre for Data Science (MCDS) have invited experts from climate modelling, social data science, biomedical engineering, and computational cognitive science for an in-depth panel discussion.
Questions that may be explored include:
- When and how does data become complex?
- How are emerging technologies redefining data complexity?
- How have research methodologies adapted to deal with complex data?
- How does complexity change as the volume of data increases?
- How do we choose what data to collect, analyse and inform decisions?
There will be an opportunity for open discussion and Q&A, informal networking, and light lunch is provided.
Panelists (in alphabetical order)
Dr Vanessa Ferdinand, Research Fellow in Computational Cognitive Science, Melbourne School of Psychological Sciences
Vanessa works on computational approaches for understanding cognition, culture, and sociality in humans and animals. She received her PhD in the Evolution of Language from the University of Edinburgh and then took up a prestigious Omidyar Fellowship in Complex Systems Science at the Santa Fe Institute. Currently, she is a Research Fellow at the University of Melbourne’s Complex Human Data Hub. She met her favorite complex data set in 2018, when she volunteered as a field assistant for the Tambopata Macaw Project in the Peruvian Amazon rainforest and is now working as an analyst on that project.
Dr Pip Karoly, Senior Research Fellow, Biomedical Engineering, Data Scientist, Seer Medical
Philippa (Pip) is working to develop an innovative, patient- specific approach to seizure forecasting. Using sophisticated computational techniques, long-term data from brain recordings, environmental, behavioural and physiological factors can be combined and converted into useful seizure likelihood models. For three years, Pip also worked as a software engineer for the Australian medical technology company, Seer. At Seer she developed a mobile app that provides people with epilepsy insight into their seizure patterns. Now, her research combines this app data with wearable devices and neural implants to generate real-time insights into seizure likelihood.
Zeb Nicholls, Research Fellow Emissions Pathway, Geography, Earth and Atmospheric Sciences
Zebedee is an expert in reduced complexity climate modelling. His research focuses on the development, evaluation and application of reduced complexity models with a particular focus on the Model for the Assessment of Greenhouse gas Induced Climate Change (MAGICC). In the IPCC’s Sixth Assessment Report, he led the writing of Cross-Chapter Box 7.1 on reduced complexity models used for scenario classification in AR6, was a Contributing Author to WG1 Chapters 1, 4, 5, 6, 7 and Technical Summary and WG3’s Summary for Policy Makers, Chapter 3 and Annex C. He can be found on GitHub and GitLab, and tweets @NichollsZeb
Professor Michael Kirley, School of Computing and Information Systems and Co-Director, Melbourne Centre for Data Science
Michael’s research interests encapsulate artificial intelligence, machine learning techniques and game theory, with a strong focus on evolutionary computation. He has made significant contributions to research focused on the design of data-driven algorithms for optimization and decision-making. Beyond established boundaries and discipline norms, Michael’s work bridges data science and social science to identify solutions to complex social-technological-ecological problems.
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