Brendan Frey

Computer Science

Six researchers at the �ؿ�ζSM with expertise in areas such as machine learning, medicine, engineering and statistics are joining the Vector Institute.

They’re joining a highly accomplished team of world-class researchers who helped launch the Vector Institute in March, including U of T's University Professor Emeritus Geoffrey Hinton; Associate Professor and leader of Uber’s Advanced Technologies Group Raquel Urtasun; and Deep Genomics founder and electrical and computing engineering Professor Brendan Frey.

“My top priority since Vector’s launch has and continues to be to build a collaborative, talented team,” said one of the original Vector Institute co-founders, Richard Zemel, a U of T computer science professor and the research director for Vector. “I am thrilled that these individuals will join a diverse and growing team of faculty committed to making Vector a global leader in AI.”

The new members are:

David J. Fleet, U of T professor of computer science, department of mathematical sciences at the �ؿ�ζSM Scarborough
Anna Goldenberg, U of T assistant professor of computer science (computational biology group) and scientist in the Genetics and Genome Biology Lab at SickKids Research Institute
Frank Rudzicz, U of T assistant professor of computer science (status) and scientist at the Toronto Rehabilitation Institute-UHN
Jimmy Ba, assistant professor of U of T computer science, joining U of T in fall of 2018
Murat Erdogdu, joint appointment as U of T assistant professor of computer science and statistical sciences, joining U of T in fall of 2018
Marzyeh Ghassemi, joint appointment as U of T assistant professor of computer science and medicine, joining U of T in fall of 2018

Originally from Turkey, Erdogdu (left) is coming to U of T from Microsoft Research New England, where he is a postdoctoral researcher. He has a PhD in statistics and a master’s degree in computer science from Stanford University, as well as bachelor degrees in electrical engineering and mathematics from Boğaziҫi University.

His specialty is the design of optimization algorithms for machine learning models, such as deep learning models and recommender systems. By using more efficient algorithms, model training times can be significantly reduced from weeks (depending on the size of the datasets) to just hours, enabling researchers to efficiently test and select the best model for the problem at hand.

“I’m really looking forward to designing efficient algorithms for real-world problems,” Erdogdu said, adding he’s an avid hiker and camper and is looking forward to exploring the city and surrounding countryside when he’s not in the lab.

“When I visited Canada, I found it extremely friendly and it was really diverse,” he said.

Invited to speak on campus last March, he said he was blown away by the “energetic” and collaborative nature of the computer science department and all of the activity on the downtown Toronto campus. He was also invited to attend the official launch of the Vector Institute.

“The �ؿ�ζSM is one of the best universities in my area of research, which is artificial intelligence, machine learning and optimization,” he continued. “It will be a pleasure to be around experts like Geoff Hinton and Radford Neal – I think it’s safe to say they changed the world. There are also lots of young faculty that I’d be very excited to work with and collaborate with.

“And that’s why I chose the �ؿ�ζSM and Vector.”

Ba (left), whose research focuses on the development of learning algorithms for deep neural networks, is returning to U of T – this time in computer science – after completing his undergraduate, master’s and PhD here in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering.

He was supervised at U of T by some leading researchers in machine learning, including Hinton, Frey and Ruslan Salakhutdinov, who is now a computer science professor at Carnegie Mellon University.

Among his many accomplishments, Ba developed the Adam Optimizer, one of the go-to algorithms to train deep learning models. He was also one of the first students from a Canadian institution to win a Facebook PhD Fellowship, and in 2015 his team achieved the highest place among academic labs in the image caption generation competition at the Conference on Computer Vision and Pattern Recognition. He is currently a postdoctoral researcher at MIT.

Ghassemi (left), a recent PhD graduate from MIT, is a visiting researcher with Alphabet’s Verily and a part-time postdoc at MIT. Her hiring at U of T signals a new partnership between computer science and medicine: She is the first joint hire in computational medicine.

“The �ؿ�ζSM computer science department has established experts in machine learning,” said Ghassemi, “as well as really exciting junior faculty doing work in important areas of machine learning like Raquel [Urtasun] and driverless vehicles."

She has focused her research on machine learning applications in health care. This means using machine learning as a tool in her work exploring clinical data with algorithms to “predict interesting and important human risks” and anticipate patient needs and decrease mortality rates, she explained.

For example, clinical data can be mined to determine what kind of patients in the intensive care unit will require a ventilator or a blood cell transfusion. It can also predict a patient's length of stay and their risk of death within a year of leaving care.

Ghassemi is also interested in using machine learning in non-invasive patient monitoring. She’s worked on detecting voice disorders that impact a subject's ability to speak using an electromechanical device called an accelerometer, coupled with a machine learning algorithm.

Instead of placing a camera in a patient’s throat that detects polyps and other damage to strained vocal chords, the accelerometer is simply taped to their neck.

“We were able to non-invasively detect which patients had nodules, which was very exciting,” she said. Her clinical collaborators at the Massachusetts General Hospital Voice Center are currently exploring the use of a cellphone prompting system based on findings of the non-invasive accelerometer. When subjects are in danger of straining their chords, they are sent an alert.

In addition to its machine learning expertise, the ability to work with “world-class clinical collaborators” at U of T’s nine partner hospitals was another important draw for Ghassemi.

“All of the people I met at the Faculty of Medicine were excited to work on clinically meaningful problems,” she said. “They really wanted to understand how we could apply machine learning techniques and develop new algorithms to be useful in a clinical setting. That combination of a fantastic technical school and a collaborative, world-class clinical environment is pretty rare.”

U of T's Deep Genomics raises US$13 million to fund expansion

Christopher.Sorensen — Tue, 26 Sep 2017 15:17:47 +0000

U of T's Deep Genomics raises US$13 million to fund expansion

Christopher.Sorensen Tue, 09/26/2017 - 11:17

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Brendan Frey, a professor in U of T's Faculty of Applied Science & Engineering, married machine learning and genomic science to develop genetic medicines (photo by Johnny Guatto)

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Deep Genomics, an artificial intelligence-powered health startup co-founded by �ؿ�ζSM's Brendan Frey, has raised US$13 million from a Silicon Valley venture capital firm.

The startup, launched in 2015, combines artificial intelligence, or AI, and genomics research to help develop genetic medicines to treat a myriad of disorders – everything from autism to cancer.

Frey, a professor in U of T's Faculty of Applied Science & Engineering, told the Globe and Mail the cash raised through an equity investment by Khosla Ventures of Menlo Park, Calif. will be used to hire a range of scientific experts, doubling the firm's current 20-person staff.

“We are aiming to flip medicine on its back and do something completely different,” said Frey, who is also Deep Genomics's CEO.

Learn more about Entrepreneurship at U of T

The AI revolution spreads its wings in Toronto, and at U of T

Christopher.Sorensen — Thu, 31 Aug 2017 19:13:39 +0000

The AI revolution spreads its wings in Toronto, and at U of T

Christopher.Sorensen Thu, 08/31/2017 - 15:13

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U of T's Geoffrey Hinton is optimistic about the future of deep learning, an AI field he helped pioneer (photo by Johnny Guatto)

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Geoffrey Hinton

Geoffrey Hinton may feel a bit sheepish about his “godfather” of deep learning moniker, but there’s little doubt the University Professor Emeritus at the �ؿ�ζSM helped spark a revolution in artificial intelligence, or AI, that could eventually touch everyone on the planet.

“I feel slightly embarrassed by being called the godfather,” Hinton admits in a recent profile published by London’s The Daily Telegraph, the first in a three-part series on the explosion of AI research and startups in Toronto.

Read The Daily Telegraph story on Hinton

Hinton’s early life – raised in Great Britain, a brief stint as a carpenter, then into academia with a focus on AI – is recounted. So are the years toiling in relative obscurity as he worked on a machine learning approach at U of T that mimics the way human toddlers learn.

Hinton, who is an engineering fellow at Google and the chief scientific adviser at the newly created Vector Institute, also voices optimism about the impact AI will have on fields like medicine and why the automation of the economy shouldn’t be feared.

“In a sensibly organized society, if you improve productivity, there is room for everybody to benefit,” he says in the article. “The problem isn’t the technology, but the way the benefits are shared out.”

That’s to say nothing of the benefits provided by AI technologies themselves. In the series’ second instalment, the paper details how Frank Rudzicz, a scientist at the Toronto Rehabilitation Institute and an assistant professor at U of T’s department of computer science, and his computer science colleague Liam Kaufman are using AI algorithms to examine human speech to better track the health of patients suffering from neurological disorders.

Their startup, WinterLight Labs, was co-founded with Katie Fraser, who recently received a PhD from the Faculty of Arts & Science, and Maria Yancheva, who completed a master’s degree in computer science at U of T.

Rudzicz also talks about the robot he built – named Ludwig – that uses machine learning algorithms to talk to patients and assess their mental health.

Read The Daily Telegraph story on WinterLight Labs and Deep Genomics

Brendan Frey of U of T's department of electrical & computer engineering, is a co-founder and CEO of Deep Genomics (photo by Johnny Guatto)

The paper also talks to Brendan Frey about his startup Deep Genomics, which uses machine learning techniques to help develop drugs that target the root causes of diseases and disorders, from cystic fibrosis to cancer.

“The basic fact is no human or group of humans will ever understand how the genome works,” says Frey, an engineering professor at U of T with cross-appointments in the department of computer science and the Donnelly Centre for Cellular and Biomolecular Research. He is also a co-founder of the Vector Institute.

“We have an exponentially growing set of data to allow us to peer into cells and read out what is changing,” he says. “There is only one solution: artificial intelligence. It’s the best technology we have in our systems to understand complex data.”

U of T's Deep Genomics applies AI to accelerate drug development for genetic conditions

ullahnor — Wed, 03 May 2017 21:10:58 +0000

U of T's Deep Genomics applies AI to accelerate drug development for genetic conditions

ullahnor Wed, 05/03/2017 - 17:10

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U of T Engineering Professor Brendan Frey is the founder and CEO of Deep Genomics, a startup company applying deep-learning techniques to revolutionize genomic medicine (photo courtesy of Deep Genomics)

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U of T spinoff company combines leading research in both machine learning and genomic science to accelerate development of highly tailored medical treatments

Genetic mutations are the cause of countless diseases and disorders, from cancer to autism to cystic fibrosis.

Now, startup company Deep Genomics is applying decades of research into machine learning and genomic science to develop genetic medicines – accelerating treatments that address the root causes of these conditions.

“If you have smoke billowing out of the tailpipe of your car, you don’t just put a filter on the tailpipe – you have to look under the hood and address the original problem,” says Brendan Frey, the co-founder and CEO of Deep Genomics, and a U of T engineering professor with cross-appointments in the department of computer science and the Donnelly Centre for Cellular and Biomolecular Research. “That’s what we’re doing: applying our platform for the discovery-phase development of medicines that address genetic problems.”

Developing new drugs is expensive, slow and inefficient – when researchers identify a protein involved in a disease, pharmaceutical companies often use a ‘guess-and-test’ approach to see whether any of the known drug molecules in their arsenal is a match to the protein’s unique shape. Often, thousands of molecules need to be screened in order to generate a match.

Frey’s team at Deep Genomics is looking at the first biological step in the process: at the genes that contain the blueprints for proteins and instructions on how and when to produce them.

“There are many ways a protein could be causing a problem, resulting from different changes to the genome. We can see those changes at the level of individual genes,” says Frey. “Instead of focusing on proteins, we’re focusing on the genetic mutations that are the source of the problem.”

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Tracking proteins using AI: U of T scientists develop deep learning algorithm

ullahnor — Wed, 03 May 2017 15:56:25 +0000

Tracking proteins using AI: U of T scientists develop deep learning algorithm

ullahnor Wed, 05/03/2017 - 11:56

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Yeast cells (purple) with DNA-containing nuclei (pink) and protein (green) residing in a cell's waste compartment

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