Thursday, January 24th, 2019

Eric Grewal is a fourth-year student in the Math and Engineering program, specializing in computing and communications. This year he put his sophisticated modelling skills to work with the Queen’s Genetically Engineered Machine team (QGEM).

QGEM is an undergraduate research and design team with a prestigious pedigree. Each year, a new team is chosen to plan and execute a project using synthetic biology to tackle real world problems in medicine, sustainability, and more. In several recent years the team has won gold at the International Genetically Engineered Machine jamboree held in Boston. This year they were the only team in Ontario to receive a gold medal.

This year, the team developed a pacifier that detects the stress hormone cortisol in babies’ saliva, and transfers the levels to a mobile device. It’s not hard to imagine how much parents would appreciate this innovation. Team leader Elisha Krauss told the Queen’s Journal that he met with members of Autism Ontario over the summer. He said parents of children with autism who are non-communicative were "blown away" by this potential tool for understanding their children’s wellbeing.

So how did Eric’s mathematical skills contribute to this ingenious device? The team comprised biologists, computer scientists, and mathematician Eric. Being able to communicate across disciplines was crucial, Eric says. "As a team we all had to understand what the problem was that we were trying to solve. I had little biology knowledge beyond high school, but had team members who were very knowledgeable in the subject. We were able to perform effectively because the team was able to teach others the relevant knowledge to their field, which allowed everyone to have a more in-depth view. For example, a biologist would explain a concept to a Biology and Computing student, who could then explain the problem to me in terms that I understood, allowing me to create a model." Specific mathematical learning that Eric applied to the problem included ordinary differential equations, Brownian motion and random walks.

More broadly, he applied the mindset that math and engineering has taught him; stepping up to solve big, open-ended problems by breaking them down into smaller, more manageable problems. Eric loves the challenge of the Math and Engineering program. He finds it equips students with skills that give them a head start in the job market. When he worked a summer job at a financial firm, he was recognized for problem solving ability way beyond what was expected from an undergraduate. The skills he has learned have a wide range of applications, from modelling complex solutions to measure babies’ hormone levels, to applying machine learning to the stock market – Eric’s undergraduate thesis project.   

For anyone interested in joining the QGEM team, Eric recommends asking for volunteer positions and being persistent – the team is competitive to get into, but very rewarding if you do.

Credit: Queen’s Journal November 9 2018.