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UTSC researchers win provincial innovation awards

Ruslan Salakhutdinov is one of three UTSC researchers to win the provincial innovation award.

Three UTSC faculty won Early Researcher Awards from the province in recognition of the importance of their research and its potential to make Ontario a global leader in innovation.

Kagan Kerman, assistant professor of chemistry, Natalie Rothman, assistant professor of history, and Ruslan Salakhutdinov, assistant professor of statistics, all received the $140,000 awards from the Ontario Ministry of Economic Development and Innovation.

“These awards are designed to attract and retain the best and brightest innovators from around the world. The fact that UTSC researchers received three of these grants speaks to the high quality of the research being done at this campus,” said Malcolm Campbell, UTSC vice-principal, research.

Kerman is a bioanalytical chemist who is examining the mechanisms behind HIV/AIDS, as well as neurodegenerative diseases like Alzheimer’s. He is doing pioneering work in electrochemical and optical techniques that will give insight into the structural changes in peptides and proteins caused by these diseases.

“I’m gratified,” says Kerman. “The award is very generous, and it will allow me to bring new people in.”

In fact, one of the purposes of the award is to allow researchers to recruit and train new students. In Kerman’s case, he intends to use the award to add two more students to his lab, as well as help support the three he already supervises.

Rothman is a historian who studies the early modern Mediterranean. She’s especially interested in interactions between Venice and the Ottoman Empire, and how the period can be used to critique modern ideas of a “clash of civilizations” between the East and the West.

Rothman is also interested in digital scholarship, and will use the grant to train a group of undergraduate and graduate research assistants and IT specialists to help promote collaborative digital scholarship. Rothman is also working on a web platform for scholars called ePorte, and is also head of a series of summer institutes called Roots & Routes, which is examining the pre-modern Mediterranean using digital scholarship.

Salakhutdinov works in the area of statistical machine learning, which is a subfield of artificial intelligence. He works on methods that allow computers to learn efficiently when presented with data. For instance, his work is applicable to machine vision, in which a computer has to learn to distinguish the shape of a car from that of a pedestrian, or one face from another.

Improved machine learning methods are especially important if we are to take full advantage of the flood of information that has become available in recent decades, Salakhutdinov says.

“In the last few years, so much data has become available to us,” he says. “The question is how to build machines that can intelligently analyze this data.”

© University of Toronto Scarborough