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When Madeleine Bonsma-Fisher bikes through Toronto, she sees where her research meets the road.

Each street she pedals down presents as a series of data points: She’ll count 15 people weaving past one another on the sidewalk, while three cars cruise down a road that takes up 80 per cent of the space.

A cycling activist, Bonsma-Fisher is studying traffic patterns as part of her post-doctoral research at the University of Toronto’s , an that is a tri-campus hub for number crunchers across disciplines. Before that, she modelled evolutionary interactions between microbes.

The common thread? Data and data analysis.

“I don't want to say that data science is the answer to everything, but I am finding that there is so much you can do,” Bonsma-Fisher says. “It gave me a lot of freedom to really just do whatever I wanted.”

Her current research focuses on what might seem like a simple question: At any point in Toronto, can you cycle to essential destinations – grocery stores, health care and schools – within 30 minutes, using only bike lanes and traffic-calmed roads?

The answer, she says, is far from straightforward. It requires sophisticated data analysis to make a map of the entire city and rate each road according to traffic stress, which accounts for factors such as traffic volume, speed limits and physical separation.

The next step, Bonsma-Fisher says, is to pinpoint places where infrastructure could improve access to cycling as a comfortable and convenient mode of transportation, such as dedicated bike lanes and physical separation from car traffic.

As she searches for active transportation solutions, Bonsma-Fisher is working with two advisers at the Data Sciences Institute: Shoshanna Saxe, an associate professor in the department of civil and mineral engineering, and Timothy Chan, a professor of mechanical and industrial engineering – both in the Faculty of Applied Science & Engineering.

“What’s cool about the Data Sciences Institute is that the vision is to bring people together with different experience and allow people to make that jump to a different field.”

The winding road of Bonsma-Fisher’s research career – and the data focus that underpins it – began when she arrived at ����ֱ��’s School of Graduate Studies in 2014 with a physics degree and an interest in using the field’s principles to solve biological problems.

Her supervisor, Sidhartha Goyal, an associate professor in the department of physics in the Faculty of Arts & Science, suggested she look into CRISPR – a term she hadn’t heard before, but one that would become the subject of both her master’s and doctoral studies.

You may have heard of CRISPR in the context of , but the technology is derived from a bacterial defence mechanism that is analogous to adaptive immunity in humans. Many bacteria have an immune system called CRISPR that allows them to store memories of viruses in their own DNA – like a genetic gallery of viral “mug shots,” Bonsma-Fisher explains.

As part of her PhD research, Bonsma-Fisher built a simple mathematical model to explore how computer-simulated interactions between populations of bacteria and viruses shape CRISPR immune memories.

The paper, , provides fresh insight into the evolutionary “arms race” between viruses and bacteria – with viruses mutating to evade immune recognition, while CRISPR builds bacteria’s DNA database of previous attackers. The simplicity of the model helped narrow down the most prominent processes in a complicated system, Bonsma-Fisher says.

Down the road, Bonsma-Fisher says the model could contribute to our understanding of immunity in more complex organisms, including humans.

“Some of the conclusions we think are going to apply to any type of immune system-virus interaction.”

While she was chipping away at her microbial models, Bonsma-Fisher made another discovery: data analysis skills were in short supply – and high demand – among her fellow graduate students. So, she co-founded the group to give researchers across all disciplines a chance to learn the basics of programming and teach each other new techniques through hands-on, member-led tutorials.

“A lot of people would try to learn by themselves,” she says, “and there would be a lot of struggle and tears. ����ֱ�� coders was a place for people to support each other through all of that.”

Bonsma-Fisher is interviewed by CBC about cycling infrastructure in Ottawa.

Bonsma-Fisher’s turn toward sustainability-oriented research around cycling came naturally.

Like many university students, Bonsma-Fisher relied on her bike to commute to campus and was all too familiar with the challenges of being a cyclist in a car-focused Canadian city.

Upon moving to Ottawa, Bonsma-Fisher joined the board of advocacy group , where she contributed data analysis to report on how the COVID-19 crisis has influenced cycling trends .

The more she learned about transportation infrastructure, the faster the wheels in her head began to turn. What if she could combine her passions – cycling and data analysis – to make the streets safer and cities more sustainable?

“It felt like there were these two parts of me,” she says. “I [used data analysis] to bring together a lot of things I care about: environmental sustainability and having a more human-scale place to live.”

Saxe, who is Canada Research Chair in Sustainable Infrastructure, says Bonsma-Fisher’s personal investment in the subject is foundational to her work. “I find people do better research when they are intrinsically motivated by the topic,” she says.

Bonsma-Fisher notes that quantitative data alone can’t solve every problem, particularly when it comes to questions of equity and people’s lived experiences. Nevertheless, she says surveys suggest that most adults would be willing to bike if they were physically protected from cars – and data can help point policymakers to the places where infrastructure is needed most.

“I know from my experience what I want to bike on and what it feels to be on a road that feels unsafe,” she says. “If the city wants to get people biking – and they do – they need to make it safe.”