We are inviting all CENTRA colleagues and affiliates to join us at the upcoming webinar “Operational Artificial Intelligence” by Dr. Alexander Wong, University of Waterloo. The registration is open to all, please feel free to extend the invitation to any colleagues and students you know will benefit from this opportunity. Sign up below to receive the participation link to attend the webinar.
Time and Date:
Tuesday, December 12th, 2017
3.00 PM Hawaii
5.00 PM US PST
6.00 PM US MST
8.00 PM US EST
Wednesday, December 13th, 2017
01.00 AM Portugal (apologies to friends in Europe)
07.30 AM Myanmar
08.00 AM Indonesia, Thailand, Vietnam
09.00 AM Taiwan, Malaysia, Philippines, Singapore
10.00 AM Japan, S. Korea
Deep learning has given rise to a major revolution in the field of artificial intelligence (AI). However, there are several major challenges that has prevented widespread deployment of deep learning AI in a wide variety of operational scenarios across industries, with three of the most major challenges being: i) unique AI, ii) explainable AI, and iii) scalable AI. The ability to solve these challenges can be a game-changer for many industries, as well as provide trust and confidence in the decisions and predictions made by AI. In this talk, each of these challenges will be discussed from a operational AI perspective, and major breakthroughs in my research group tackling these three key challenges will be presented, along with their applicability in enabling operational AI across a wide variety of scenarios.
Dr. Alexandar Wong
Canada Research Chair in Medical Imaging Systems
Associate Professor, P.Eng.
Co-Director, Vision and Image Processing (VIP) Research Group
Department of Systems Design Engineering, University of Waterloo
Dr. Wong has published over 400 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 evolutionary deep intelligence, Discovery Radiomics, and random deep intelligence via deep-structured fully-connected graphical models). He has received numerous awards including two 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 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), two Best Paper Awards for the Conference of Computer Vision and Imaging Systems (CVIS) (2015,2017), 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), AquaHacking Challenge First Prize (2017), Best Student Paper at Ottawa Hockey Analytics Conference (2017), and the Alumni Gold Medal.