Alexander Wong, P.Eng. founder speaking about COVID-NET: Canada Research Chair in Artificial Intelligence and Medical Imaging, co-director of the Vision and Image Processing Research Group, associate professor in the Department of Systems Design Engineering at the University of Waterloo, and Chief Scientist at DarwinAI
This week, Stephen Ibaraki has an exclusive interview with Alex Wong.
Alexander Wong, P.Eng., is currently the Canada Research Chair in Artificial Intelligence and Medical Imaging, Member of the College of the Royal Society of Canada, co-director of the Vision and Image Processing Research Group, an associate professor in the Department of Systems Design Engineering at the University of Waterloo, and Chief Scientist at DarwinAI. He had previously received the B.A.Sc. degree in Computer Engineering from the University of Waterloo, Waterloo, ON, Canada, in 2005, the M.A.Sc. degree in Electrical and Computer Engineering from the University of Waterloo, Waterloo, ON, Canada, in 2007, and the Ph.D. degree in Systems Design Engineering from the University of Waterloo, ON, Canada, in 2010. He was also an NSERC postdoctoral research fellow at Sunnybrook Health Sciences Centre. He has published over 520 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, computer vision, and multimedia systems.
In the area of computational imaging, his focus is on integrative computational imaging systems for biomedical imaging (inventor/co-inventor of Correlated Diffusion Imaging, Compensated Magnetic Resonance Imaging, Spectral Light-field Fusion Micro-tomography, Compensated Ultrasound Imaging, Coded Hemodynamic Imaging, High-throughput Computational Slits, Spectral Demultiplexing Imaging, and Parallel Epi-Spectropolarimetric Imaging).
In the area of artificial intelligence, his focus is on operational artificial intelligence (co-inventor/inventor of, Generative Synthesis, evolutionary deep intelligence, Deep Bayesian Residual Transform, Discovery Radiomics, and random deep intelligence via deep-structured fully-connected graphical models).
He has received numerous awards including three Outstanding Performance Awards, a Distinguished Performance Award, an Engineering Research Excellence Award, a Sandford Fleming Teaching Excellence Award, an Early Researcher Award from the Ministry of Economic Development and Innovation, a Best Paper Award at the NIPS Workshop on NIPS Workshop on Transparent and Interpretable Machine Learning (2017), a Best Paper Award at the NIPS Workshop on Efficient Methods for Deep Neural Networks (2016), two Best Paper Awards by the Canadian Image Processing and Pattern Recognition Society (CIPPRS) (2009 and 2014), a Distinguished Paper Award by the Society of Information Display (2015), three Best Paper Awards for the Conference of Computer Vision and Imaging Systems (CVIS) (2015,2017,2018), Synaptive Best Medical Imaging Paper Award (2016), two Magna Cum Laude Awards and one Cum Laude Award from the Annual Meeting of the Imaging Network of Ontario, CIX TOP 20 (2017), Technology in Motion Best Toronto Startup (2018), Top Ten Startup at AutoMobility LA (2018), AquaHacking Challenge First Prize (2017), Best Student Paper at Ottawa Hockey Analytics Conference (2017), and the Alumni Gold Medal.
TO WATCH THE VIDEO INTERVIEW, CLICK ON THIS MP4 file link