Imagine having a job where every morning when you go to work you have a genuine smile on your face as you look forward to the day.
"In the first few months, I honestly said my face is hurting from smiling," says Sandra Zilles, assistant professor in the Department of Computer Science.
Zilles was awarded a five-year, $500,000 Canada Research Chair in Computational Learning Theory on Nov. 24, one of 310 new or renewed CRCs at 53 Canadian universities announced by Industry Canada. There are now 10 Canada Research Chairs at the University of Regina, including three CRC renewals also announced by Industry Canada: Peter Leavitt, CRC in Environmental Change and Society (Tier 1); Shadia Drury, CRC in Social Justice(Tier 1); and Gordon Huang, CRC in Energy and Environment (Tier 1).
Through her work, Zilles and her team of researchers will develop efficient solutions to complex problems in artificial intelligence, using interactive machine learning models and techniques.
Ultimately, her research will advance our understanding of how we can make machines such as computers learn, in order for them to become intelligent assistants to humans.
"I'm interested in how I can use interaction between a human and a machine to speed up learning, in the sense that less data is needed. If the machine somehow knows that the data given is helpful data rather than randomly chosen data then it would be able to learn faster," emphasizes Zilles.
A machine that can be taught to exploit the quality of well-chosen data rather than a large quantity of potentially expensive data will speed up its learning process and be more economical.
"Wherever you collect data you have to analyze it, and very often it's just too complicated for a human to look at the data and answer the questions."
Applications for machine learning are wide ranging: from personalizing advertising in online markets and adapting to user preferences in web searches to analyzing genomes and molecules in bioinformatics.
Zilles is dedicated to bridging the gap between theory and practical application. "I'm constantly working on my theoretical foundations, but what is important for me is to see that the problems that we're working on in theory somehow connect to what people are really interested in and what they need in applications," says Zilles.
In December, Zilles will meet with the Canadian Information Processing Society in Regina - an association of IT professionals - to discuss possibilities for connecting her machine learning research to industry relevant applications.
For more information on Zilles' research, visit http://www2.cs.uregina.ca/~zilles/ .