Amii welcomes Linglong Kong as new Canada CIFAR AI Chair

We’re thrilled to welcome Amii Fellow Linglong Kong as our newest Canada CIFAR AI Chair.

Kong is one of eight new chairs announced today by CIFAR. The Canada CIFAR AI Chair program provides some of the top AI researchers in the country with long-term funding to support their work and allow them to train the next generation of AI leaders.

The program is a cornerstone of the Pan-Canadian AI Strategy, which brings together the country’s three national AI institutes to drive innovation in Canada forward and make it a leader in the development and commercialization of responsible artificial intelligence.

“Artificial intelligence is one of the greatest technological transformations and economic opportunities of our age. That is why our government has placed fostering AI research at the heart of the Pan-Canadian Artificial Intelligence Strategy," says The Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry. "We continue to support and grow the Chairs program so Canada can continue to retain, attract and develop academic research talent in AI. Congratulations to our eight new chairholders – you join other world-leading researchers who are driving efforts to build a stronger economy, develop cleaner energy, improve public health, and increase innovation in Canada."

A professor in the Math and Statistical Sciences Faculty at the University of Alberta, Kong joins 31 other CIFAR Canada AI Chairs affiliated with Amii and is part of a rapidly growing AI research community in Alberta.

Read the full CIFAR announcement here.

About Linglong Kong

Kong’s research interests are vast- he has worked with using AI to analyze neuroimaging data and better protect patient privacy. His work with quantile regression and robust statistics has him studying how to improve datasets to help optimize machine learning, such as reducing racial and gender bias in data used for social work. Kong’s deep knowledge of statistics has formed the foundation of his work in areas such as deep learning and distributal reinforcement learning

Kong started his statistics training at Beijing Normal University, before getting his M.Sc. in Probability and Statistics from Peking University. He then moved to the University of Alberta to finish his PhD in Statistics. Much of his early work focused on biostatistics research with the University of Alberta, and later, The University of North Carolina at Chapel Hill. Kong returned to the University of Alberta to teach math and statistics in 2012. He is a Canada Research Chair in Statistical Learning and is active as an associate editor for several journals, including the Canadian Journal of Statistics, the Journal of the American Statistical Association, Applications & Case Studies, and Frontiers in Neuroscience.

"Amii is pleased to welcome Linglong Kong as one of the latest Canada CIFAR AI Chairs. He further strengthens a rapidly-growing research community, both in Alberta and across the country, that is making Canada a hub of artificial intelligence development," says Cam Linke, Amii CEO. "The work of Linglong and his fellow Canada AI Chairs is vital to the Pan-Canadian AI Strategy and will help push forward our goal: the responsible development of AI that will benefit us all."

Other named Canada CIFAR AI Chairs

“The new Canada CIFAR AI Chairs joining Amii and the Vector Institute are an extraordinarily talented group of researchers who will continue to educate and inspire the next generation of AI leaders and advance research in exciting and important areas," says Elissa Strome, Executive Director, Pan-Canadian AI Strategy at CIFAR. "We look forward to seeing how their research will advance the development of artificial intelligence and its applications for the benefit of Canadians and the world.”

The latest round of Canada CIFAR AI Chairs named seven other researchers working with Toronto’s Vector Institute. Learn more about them:

Wenhu Chen (Vector Institute; University of Waterloo).

With a focus on natural language processing, deep learning and multimodal learning, Chen designs models and algorithms that incorporate world knowledge into deep neural networks, making AI models more trustworthy.

Jeff Clune (Vector Institute; University of British Columbia). Clune’s work in the realms of evolving deep learning and deep neural networks explores evolutionary biology as a model for producing AI that could improve itself through continuous innovation and learning.

Gillian Hadfield (Vector Institute; University of Toronto’s Schwartz Reisman Institute for Technology & Society). Hadfield’s research is focused on the challenges of AI governance. She brings extensive legal, scientific and humanistic knowledge to the scrutiny of emerging technologies including AI, designing next-generation methods of regulation to ensure that the global technological transformation now underway will continue to achieve human goals of fairness, stability, prosperity, and human dignity.

Xi He (Vector Institute; University of Waterloo). He’s research focuses on the areas of privacy and security for big data, including the development of usable and trustworthy tools for data exploration and machine learning with provable security and privacy guarantees.

Parvin Mousavi (Vector Institute; Queen’s University). Mousavi is advancing techniques for developing and leveraging machine learning in computer-assisted medical interventions and precision medicine. She also leads a multi-institutional program that trains the next generation of AI researchers on innovative computational approaches and intelligent systems that can predict and explain complex biological processes. Anatole von Lilienfeld (Vector Institute; University of Toronto). Von Lilienfeld leads an interdisciplinary team at the University of Toronto, working on theoretical and computational methods for the exploration of chemical compound space using quantum mechanics.

Vered Shwartz (Vector Institute; University of British Columbia). Shwartz’s research focuses on natural language processing, with the fundamental goal of building models capable of human-level understanding of natural language. She is particularly focused on the implicit meaning (“reading between the lines”) that is abundant in human speech, and on developing machines with advanced reasoning skills.

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