Brendan Frey

Canada Research Chair in Information Processing and Machine Learning

Tier 1 - 2012-10-01
Renewed: 2019-10-01
University of Toronto
Natural Sciences and Engineering Research Council


Research involves

Developing new algorithms that allow computers to decipher the massive amounts of data being produced by medical professionals, which will allow us to better understand and treat disease.

Research relevance

Building new mathematical frameworks for these algorithms will help a variety of industries and sectors make better decisions and better understand challenges and problems.

A Different Sort of Lab: A Different Sort of Breakthrough

You car breaks down. What do you do? Get out and look? Call for help? Walk for help?

When confronted with a problem, we go through many different thought processes to come up with a solution: we analyze a lot of information, decide what to do based on that information, and then, often, modify those decisions in relation to other circumstances. We do this quickly, and unconsciously. If we had to put this process down in a flowchart, it would take pages just to sketch out. There are dozens, often hundreds, of questions to answers and rules to invent when solving even our simplest problems.

Dr. Brendan Frey has a problem of his own. As Canada Research Chair in Information Processing and Machine Learning, he knows that, as quickly as computers process information, humans can still work through problems in ways that computers can’t. Humans can define the right questions and work out the rules.

As we unlock more and more secrets of genetics, molecular biology and biotechnology, we accumulate vast amounts of information, but all that information is useless without computers that can process this information. To do this, we need intelligent sets of rules, called algorithms, for computers to follow, and what we really need are algorithms that allow computers to build up their knowledge, to learn and then to develop new algorithms on their own, a process called machine learning.

Already on the cusp of introducing new algorithms that do just this, Frey is working on even more advanced algorithms that are vital to medical progress. Frey will help ensure that medical progress is not held back by inadequate software, while at the same time giving us new tools to understand complex medical conditions like HIV-AIDS.