Alex Wong
Biography
Alexander Wong, PhD, 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 Lab, and a professor in the Department of Systems Design Engineering at the University of Waterloo.
He received the BASc degree in Computer Engineering from the University of Waterloo in 2005, the MASc degree in Electrical and Computer Engineering from the University of Waterloo in 2007, and the PhD degree in Systems Design Engineering from the University of Waterloo in 2010. He was also an NSERC postdoctoral research fellow at Sunnybrook Health Sciences Centre. He has published over 600 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). His work in Generative Synthesis has led to the founding of DarwinAI, a leading-edge AI company focused on accelerated deep learning development.
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.
An up-to-date CV can be found here.
He received the BASc degree in Computer Engineering from the University of Waterloo in 2005, the MASc degree in Electrical and Computer Engineering from the University of Waterloo in 2007, and the PhD degree in Systems Design Engineering from the University of Waterloo in 2010. He was also an NSERC postdoctoral research fellow at Sunnybrook Health Sciences Centre. He has published over 600 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). His work in Generative Synthesis has led to the founding of DarwinAI, a leading-edge AI company focused on accelerated deep learning development.
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.
An up-to-date CV can be found here.
Research Interests
- Artificial Intelligence
- Image processing and data analytics
- Medical imaging
- Scanning
- Sensors and devices
- Drinking water
- Autonomous and connected car
- Automotive
- Operational Artificial Intelligence
- Robotics
- Smart Infrastructure
Education
- 2010, Doctorate Systems Design Engineering, University of Waterloo, Canada
- 2007, Master of Applied Science Electrical and Computer Engineering, University of Waterloo, Canada
- 2005, Bachelor of Applied Science Computer Engineering, University of Waterloo, Canada
Teaching*
- SYDE 372 - Introduction to Pattern Recognition
- Taught in 2019, 2020
- SYDE 572 - Introduction to Pattern Recognition
- Taught in 2021, 2022, 2023
- SYDE 770 - Selected Topics in Communication and Information Systems
- Taught in 2021, 2022, 2023
* Only courses taught in the past 5 years are displayed.
Selected/Recent Publications
- Yu, Jingxin and Tang, Song and Zhangzhong, Lili and Zheng, Wengang and Wang, Long and Wong, Alexander and Xu, Linlin, A Deep Learning Approach for Multi-Depth Soil Water Content Prediction in Summer Maize Growth Period, IEEE Access, 199097, 2020
- Shafiee, Mohammad Javad and Jeddi, Ahmadreza and Nazemi, Amir and Fieguth, Paul and Wong, Alexander, Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement, IEEE Signal Processing Magazine, 22, 2020
- Fewster, Kayla M and Haider, Shahid and Gooyers, Chad E and Callaghan, Jack and Wong, Alexander, A computerised system for measurement of the radial displacement of the intervertebral disc using a laser scanning device, Computer Methods in Biomechanics and Biomedical Engineering: Imaging \\& Visualization, 287, 2020
- Kronfli, Nadine and Young, Jim and Wang, Shouao and Cox, Joseph and Walmsley, Sharon and Hull, Mark and Cooper, Curtis and Martel-Laferriere, Valerie and Wong, Alexander and Pick, Neora and others, Liver fibrosis in HIV-Hepatitis C virus (HCV) co-infection before and after sustained virologic response: what is the best non-invasive marker for monitoring regression?, Clinical Infectious Diseases, , 2020
- Gunraj, Hayden and Wang, Linda and Wong, Alexander, Covidnet-ct: A tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images, Frontiers in Medicine, , 2020
- S. Schwartz A. Wong, D. A. Clausi, Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance, Journal of Visual Communication and Image Representation, 26, 2015
- Shimon Schwartz, Chenyi Liu, Alexander Wong, David A. Clausi, Paul Fieguth, and Kostadinka Bizheva, Energy-guided learning approach to compressive FD-OCT, Optics Express, 329, 2013
- Shimon Schwartz, Alexander Wong, David A Clausi, Compressive fluorescence microscopy using saliency-guided sparse reconstruction ensemble fusion, Optics Express, 17281, 2012
- Schwartz, S., A. Wong, and D. A. Clausi, Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements, 2012 Ninth Conference on Computer and Robot Vision (CRV), , 2012
- Shimon Schwartz, Alexander Wong, David A Clausi, Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements, Computer and Robot Vision (CRV), 2012 Ninth Conference on, 1, 2012
In The News
- Geological Society elects Prof as fellow
- Researchers use AI to enhance trust in disease diagnosis
- Researchers use AI to improve microplastic identification
- Prof discusses role of AI in rural healthcare on CBC
- Prof named fellow to world's largest medical society
- Prof earns entry into group of global health fellows
- Prof earns prestigious physics fellowship
- Profs receive top Ontario engineering prizes
- Just say the word: New voice tech could run home appliances
- Redirecting research during the pandemic
- Engineering professor earns international fellowship
- AI models identify COVID-19 patients at the greatest risk of death, injury
- Food-tracking AI system developed to reduce malnutrition in LTC homes
- Farming crickets for food
- Projecting into the future
- Helping doctors manage COVID-19
- DarwinAI makes global list of promising AI startups
- Waterloo tech company helping farm crickets for food
- Screening for COVID-19 with AI
- Key advancement announced in COVID X-ray project
- Help pours in from around the world to develop new COVID-19 test
- Key advancement announced in COVID x-ray project
- Help pours in from around the world to develop new COVID-19 test
- New touchless device makes earlier detection of heart problems possible
Graduate studies
- Currently considering applications from graduate students. A completed online application is required for admission; start the application process now.