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Vector Institute appoints 8 new faculty members

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Announces plans to recruit 3 new faculty in deep learning with University of Toronto

The Vector Institute announced that it will work with the University of Toronto to recruit three new tenure-stream faculty positions in deep learning. Announced at the Vector Institute’s Evolution of Deep Learning Symposium, the positions will be created in recognition of Dr. Geoffrey Hinton, the Vector Institute’s Chief Scientific Advisor, and one of three 2018 ACM A.M. Turing Award laureates named earlier this year.

The announcement comes just as eight new Faculty Members and 29 affiliate researchers holding appointments at universities across Canada are cross-appointed into the Vector Institute. Researchers cross-appointed into the institute gain access to a collaborative community based in Toronto’s MaRS Discovery District and computing resources to catalyze foundational research and specific applications in areas such as health care and business.

Pioneering research by training graduate students

“Our community is a magnet for talented individuals, enabling us to continue building on Dr. Hinton’s pioneering research by training graduate students, upskilling industry professionals, and leading consortium projects to enable AI adoption in Canadian companies and institutions,” says Garth Gibson, President & CEO, Vector Institute. “We are thrilled to have these highly accomplished and collaborative individuals join and advance Vector’s vision.”


“Thanks in no small part to Professor Hinton’s work, the University of Toronto is recognized as a global hub of AI research and excellence,” said University of Toronto Vice-President and Provost Cheryl Regehr. “It’s only fitting that we pay tribute to his achievements by re-doubling our efforts to recruit top AI and machine learning talent. These new positions and their integration with the Vector Institute will serve growing interest in AI among students, prepare them for careers in this burgeoning field, and strengthen the ability of both institutions to advance innovation and knowledge in AI.”

The announcement was part of the Vector Institute’s Evolution of Deep Learning Symposium, held in celebration of the deep learning contributions of Dr. Hinton. Taking the audience on a four-decade tour of his career and collaborators, the symposium included a conversation between Dr. Hinton and Eric Schmidt, Technical Advisor to Alphabet Inc.

Chief AI Scientist at Facebook

In March of this year, Dr. Hinton, who is Vice President and Engineering Fellow at Google, Chief Scientific Advisor at the Vector Institute, and Professor Emeritus at the University of Toronto, was a recipient of the 2018 ACM A.M. Turing award, often described as the “Nobel Prize of computer science,” alongside Yoshua Bengio, Professor at the University of Montreal and Scientific Director at Mila, and Yann LeCun, Professor at New York University and VP and Chief AI Scientist at Facebook. The award recognized the trio for their fundamental work in deep learning and ground-breaking contributions to the field of AI.

Deep learning is a subfield of AI that uses artificial neural networks to learn to identify patterns in data and make predictions. The work of Dr. Hinton and his colleagues has been integral to the widespread adoption of neural networks. “With the ever-increasing volume of data and the rapid growth of computing power, society has woken up to the transformational power of neural networks and their potential to revolutionize many different industries,” says Dr. Hinton. “Our current neural network technology came from dedicated professors pursuing basic research with brilliant graduate students in a few leading universities. With today’s announcement, the University of Toronto and the Vector Institute are ensuring that they will continue to play a major role in the future of this game-changing technology.”

The three new tenure-stream faculty will be appointed as follows:

  • One position to be held 100% in the Department of Computer Science in the Faculty of Arts and Science.
  • One position to be shared between the Department of Computer Science in the Faculty of Arts and Science and the Edward S. Rogers Sr. Department of Electrical and Computer Engineering in the Faculty of Applied Science and Engineering.
  • One position to be shared between the Department of Computer Science in the Faculty of Arts and Science and the Department of Laboratory Medicine and Pathobiology in the Faculty of Medicine.

Successful candidates are to be eligible for nomination as Canada CIFAR AI Chairs through the Vector Institute.

About the Vector Institute

The Vector Institute is an independent, not-for-profit corporation dedicated to advancing artificial intelligence, excelling in machine and deep learning. The Vector Institute’s vision is to drive excellence and leadership in Canada’s knowledge, creation, and use of AI to foster economic growth and improve the lives of Canadians. The Vector Institute is funded by the Province of Ontario, the Government of Canada through the Pan-Canadian AI Strategy administered by CIFAR, and industry sponsors from across the Canadian economy.


The eight new Vector Faculty Members bring with them expertise in statistical machine learning, reinforcement learning, computer vision, robotics, knowledge representation, automated reasoning, systems, computer architecture, complexity theory, and differential privacy.

The Vector Institute is a community of more than 400 active researchers, including:

  • 34 Faculty Members
  • 85 Faculty Affiliates
  • 41 Postgraduate Affiliates
  • 240+ Post-Doctoral Fellows and students

The eight new Vector Faculty Members are:

Jakob Foerster

Jakob is starting as an Assistant Professor at the University of Toronto, Scarborough campus in the fall of 2020. During his PhD at the University of Oxford he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He currently works as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto.

Animesh Garg

Animesh is an Assistant Professor of Computer Science at the University of Toronto and leads the Toronto People, AI, and Robotics (PAIR) research group. He works on enabling robot learning through efficient interaction, imitation, and lifelong learning. He studies machine learning methods in robotics for interactive perception, reinforcement, and control. His work applies to health care and industrial robotics. He is also affiliated with Mechanical and Industrial Engineering, Toronto Robotics Institute, and Nvidia.

Chris Maddison

Chris will be joining the Departments of Computer Science and Statistical Sciences at the University of Toronto in the summer 2020. He is a member at the Institute for Advanced Study in Princeton and a Research Scientist at DeepMind. Chris works on methods for machine learning, with an emphasis on those that work at scale in deep learning applications. He was previously a student of Geoffrey Hinton’s and was a founding member of the AlphaGo project.

Sheila McIlraith

Sheila is a Professor with the Department of Computer Science at the University of Toronto. Prior to joining the University of Toronto, Sheila spent six years as a Research Scientist at Stanford, and one year at Xerox PARC. Her research is in the area of knowledge representation and automated reasoning, with a focus on sequential decision making and human-aligned AI. Sheila is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).

Gennady Pekhimenko

Gennady is an Assistant Professor with the University of Toronto’s Department of Computer Science. He was previously a Researcher in Systems Research Group at Microsoft Research in Redmond, WA and received his PhD in Computer Science from Carnegie Mellon University. Gennady’s research generally focuses on areas of computer architecture, systems, and applied machine learning.

Toniann Pitassi

Toniann is a Professor and Bell Canada Chair in Information Systems with the Department of Computer Science at the University of Toronto. She was named an ACM Fellow in 2018. Her areas of research include complexity theory, mathematical models for privacy-preserving computation, and non-discriminatory machine learning.

Angela Schoellig

Angela is an Assistant Professor at the University of Toronto Institute for Aerospace Studies (UTIAS), where she heads the Dynamic Systems Lab. She is also an Associate Director of the Center for Aerial Robotics Research and Education (CARRE). She conducts research at the interface of robotics, controls, and machine learning with a goal to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and from each other.

Yaoliang Yu

Yaoliang is an Assistant Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research areas include machine learning, statistics, and optimization, with applications in computer vision and multimedia.




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