Stephen Moore  Stephen Moore photo       

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Research Staff Member
Level 5/204 Lygon St, Carlton, Victoria, 3053, Australia



Dr Steve Moore is a research staff member based in the Melbourne lab, who’s area of research is centred around developing deep learning based analytics within the area of cardiology.

Steve joined IBM in 2010 as a member of the IBM Collaboratory for Life Sciences with the University of Melbourne, where he collaborated with various academic groups to allow their research efforts to leverage the IBM Blue Gene/Q supercomputer. During this time Steve worked in a number of projects within the field of life sciences including modeling coronary and cerebral blood flow and models for targeted drug delivery, developing mathematical models bio-heat transfer in human body models subject to electromagnetic loads, models of electro-osmotic flow in nano pores, and a variety of projects centred around Magnetic Resonance Imaging. Steve has also maintained an adjunct position in the Department of Mechanical Engineering and taught two graduate level courses, Computational Fluid Dynamics and Applied High Performance Computing.

Following the collaboratory Steve worked in the area of natural resources, specifically developing computational fluid dynamics models of a mineral processing technique known as froth flotation, developing the large scale elastodynamics simulations of the earth’s subsurface as part of a tomographic imaging technique known as full waveform inversion, and developing models of pollutant transport in the atmosphere.

More recently Steve has returned to research within the area of healthcare and life sciences and is focussed on developing deep learning analytics for the reconstruction of personalised computer models of the coronary arteries from X-Ray angiograms and creating near real-time simulations of the hemodynamics as a means to assess the loss in blood pressure cause by the effects of stenosis.

Throughout his time with IBM Steve has gained extensive experience in applied continuum mechanics based modeling, numerical methods for solving ordinary and partial differential equations, and implementation on a variety of high performance computing architectures, including both shared and distributed memory architectures and graphics processing units. More recent research interests include machine learning and computer vision in the context of medical image analysis and their integration with biophysical modelling.