Here’s a sneak peak into ARIA.
At one booth, a video game controller will help you gather and analyze forensic evidence in a virtual crime scene. At another, a live demonstration will reveal how mathematical modelling can help pinpoint the optimal time to exercise a financial option.
These are two of the more than 120 student-led projects that will be on display when the University of Toronto’s Department of Computer Science (DCS) hosts its Applied Research in Action (ARIA) Showcase. Spanning undergraduate, MSc, MScAC and PhD research, the Nov. 10 event highlights work across DCS, much of it developed with industry partners.
Here are two projects offering a glimpse of ARIA 2026.
Learning forensic science through gaming
In a real crime scene, there is little room for error. In Forensics Pursuit, getting it wrong is part of learning.
Janna Lim, an undergraduate student and project manager on the team, helped develop the video game alongside U of T computer science and U of T Mississauga forensic science students. Forensics Pursuit gives learners a low-stakes environment to practice investigative techniques they might otherwise have limited opportunities to try.
“Video games are a fun way to motivate students and give them hands-on material to learn from, which cannot be offered through traditional ways of studying,” says Lim, who joined the team, supervised by Steve Engels and Vivienne Luk, three years ago.
At ARIA, attendees can try one of the game’s levels. New student cohorts continue to expand and refine Forensics Pursuit, which now includes an AI lawyer character for a courtroom scene. After testing the game with groups including Peel Regional Police and summer camps, the team hopes to integrate it into U of T courses by next September.
“We can code and change things to match different educational levels and scenarios, so there could be a range of opportunities to help students practice investigation techniques,” Lim says.
Calculating the optimal time to exercise an American option
When is the right time to exercise a stock option? Yifan Qu, a PhD student in the Numerical Analysis group supervised by Professor Christina Christara, is using mathematical modelling to find a more precise answer.
Her research focuses on developing numerical methods to identify the optimal point to exercise an American option — a financial contract that lets its holder buy or sell a stock at a set price before the contract expires. This means finding the exercise boundary: when exercising the option becomes preferable to holding it.
“This is a real-world problem that a lot of people are facing every day,” says Qu. “I want to use my knowledge in partial differential equations to help them decide when to exercise the option because everyone wants to gain more money, right?”
On Nov. 10, Qu will present her project, Numerical Approximation of Exercise Boundary of American Options. She’ll compare numerical approaches, illustrating her ongoing efforts to pinpoint the optimal exercise point more accurately.
Qu’s longer-term goal, she says, is to develop an app that translates her model’s calculations into guidance for people without a strong mathematical background.
“I hope an app like this will help people who don’t know much about math understand how to trade options and maximize their profit,” she says.
Register for ARIA 2026 to join Qu, Lim and their fellow student researchers, U of T faculty and industry partners in exploring more than 120 projects showcasing technological advances that can reshape our world.