żě˛ĄĘÓƵ Biology Professor Kohitij Kar, in the Faculty of Science, will host the recipient of a prestigious (MSCA) Postdoctoral Global Fellowship in his lab for two years starting this November to advance cutting-edge work in artificial intelligence and visual tracking.

The recipient, from the University of Udine in Italy, received a fellowship valued at C$340,000 from MSCA, which empowers researchers wishing to carry out their work abroad.

Dunnhofer is an award-winning researcher at the Machine Learning & Perception Lab in the Department of Mathematics, Computer Science, & Physics of the University of Udine.

His fellowship project, titled “Towards primate-like artificial neural networks for visual object tracking,” will focus on studying algorithms at the intersection of artificial intelligence and neuroscience in the field of visual object tracking. Visual object tracking is the use of algorithms in continuously recognizing objects, such as people and vehicles, and has applications in fields like autonomous driving, robotics and medicine.

The project will draw on Kar’s expertise as a Canada Research Chair in Visual Neuroscience and his experience leading the Visual Intelligence & Technological Advances Laboratory, which is a core part of the Vision: Science to Technology Application Program, the Centre for Integrative & Applied Neuroscience, and the Centre for Vision Research at żě˛ĄĘÓƵ. His research lies at the intersection of neurophysiological investigations of visual intelligence in non-human primates and artificial intelligence systems. He is using his findings to develop artificial intelligence systems that mimic the primate brain in hopes of developing treatment strategies for neurological disorders.

As typically encouraged by the Marie Curie fellowships, Dunnhofer will augment his expertise in computer vision with Kar’s lab’s expertise in studying computations within biological vision systems like non-human primates.

For more information about the fellowship program, visit the .

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, a professor in żě˛ĄĘÓƵ’s Department of Mathematics & Statistics at the Faculty of Science, and a member of the Laboratory of Mathematical Parallel Systems, has had a U.S. patent approved for an algorithm that will reduce the training time of artificial intelligence (AI) machine learning (ML).

The patent, titled “,” was inspired by a 2018 paper titled “.” Both were based on collaborations with Ricky Fok, a former postdoctoral Fellow student; Aijun An, a professor in the Department of Engineering & Computer Science; and Zana Rashidi, a former graduate research assistant who carried out some of the computing experiments.

The now-patented algorithm, approved this year, was a result of six months of research at York. It was submitted to the United States Patent and Trademark Office in 2019. The algorithm’s framework is based on mathematical arguments that helps significantly reduce the training time of machine learning, as it absorbs, processes and analyzes new information. It does so by using a mathematical formula to allow residual networks – responsible for the training of AI – to compute in parallel to each other, thereby enabling faster simultaneous learning.

Wang’s desire to accelerate machine learning’s abilities is driven, in part, by a specific area of AI applications. “I want to apply all the algorithms I develop to health care,” Wang says. “This is my dream and mission.”

Wang has especially focused on using AI to improve care for seniors and that work has previously earned him the Queen Elizabeth II Platinum Jubilee Award from the House of Commons for initiatives during COVID-19 to mitigate the spread of the virus in long-term care facilities.

Wang plans to use the patented algorithm in ongoing projects that aim to provide smart monitoring of biological signals for seniors. For example, it could be used in long-term care to continuously monitor electrocardiogram signals at night to register heartbeats that have stopped. To move towards that goal, Wang is also working on building an AI platform that will complement those ambitions, and expects it to be ready in several years.

He is deeply invested in the social impact of AI as a member of the York organized research unit Centre for Artificial Intelligence & Society, where researchers at York who are collectively advancing the state of the art in the theory and practice of AI systems, governance and public policy.

“I can use the machine learning to help the long-term care facilities improve the quality of care, but also help out with the struggles of the Canadian health-care system,” says Wang.

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