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Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

I am a postdoc at the Computational Imaging Group, led by Steffen Bollmann. I recently finished my Ph.D. in Computational Imaging at UQ. Specifically, my Ph.D. work involved predicting the functional organization of the human visual cortex from underlying anatomy using geometric deep learning. To tackle this and other research questions, I am leveraging my interdisciplinary background in Biophysics (Bachelor's degree; University of Sao Paulo, Brazil), Neuroscience (Master's degree; Federal University of ABC, Brazil), and now the intersection of AI and imaging. I am interested in (geometric) deep learning, vision, neuroscience, and explainable and fair AI research.