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Techweek TV: AI in Health Interviews

Harnessing artificial intelligence and machine learning for better health outcomes.

This event does not require registration.

Tuesday 25 May
11:00am - 11:30am

VIRTUAL

LOCATION:
Nationwide (more than one region)
Free event
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What's it all about?

AUT researchers are working on multiple applications to improve diagnosis and treatment for a range of physical and mental health conditions using AI and machine learning.  

Join researchers from the Faculties of Design and Creative Technologies and Health and Environmental Sciences to find out more. 

Speakers

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Nikola Kasabov

Professor, Founding Director, Knowledge Engineering and Discovery Research Institute (KEDRI) , AUT

Nikola Kasabov is Professor of Knowledge Engineering in the School of Engineering, Computing and Mathematical Sciences at AUT. His main interests are in the areas of: computational intelligence; neuro-computing; bioinformatics; neuroinformatics; speech and image processing; data mining; knowledge representation and knowledge discovery.

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Maryam Doborjeh

co-founder, Neuinfomatics research group, lecturer, AUT

Dr Maryam Doborjeh is the co-founder of the "Neuinfomatics research group" that holds regular seminars for knowledge dissemination across interdisciplinarity fields for brain data modelling. This research interest includes deep learning, spiking neural networks, cognitive computation, mental/brain health informatics, spatiotemporal brain data analysis, and personalised modelling. Over the last 6 years, she has been principally researching in the field of Neuroinformatics, where she developed new methods based on brain-inspired artificial intelligence technologies to improve decision-making and decision support in various applications.

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Mangor Pedersen

Senior Lecturer, Psychology & Neuroscience, AUT

Dr Mangor Pedersen is a cognitive neuroscientist working as a Senior Lecturer at Auckland University of Technology (Psychology & Neuroscience). His research interest is to develop and validate new technologies for quantifying human brain networks using functional MRI, including machine learning and AI, graph theory and dynamical systems theory. These approaches have significantly contributed to our ability to model brain dysfunction in people who suffer from epilepsy, and professional athletes with mild Traumatic Brain Injury (concussion).

  • Artificial Intelligence
  • Health Technology
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