Cormac Rea

By: Cormac Rea

AI is no longer just a buzzword – from family gatherings to office water cooler chat, the power of AI is driving endless discussion and debate.  

The pace of AI advancement is outstripping workforce readiness, creating a critical need for professionals who can translate cutting-edge models into applied, scalable solutions.  Employers are seeking talent who can move beyond experimentation to deploy AI responsibly and effectively.  

In response to this industry demand, the Data Sciences Institute (DSI) is expanding on its data science and AI training, launching a Deploying AI microcredential to empower professionals with the skills to use AI models – especially Large Language Models (LLM) – to close the gap between innovation and implementation. This short, targeted learning experience provides the necessary frameworks, tools, and applied skills to help professionals navigate the ethical, operational and organizational challenges of AI integration. 

“As organizations race to integrate generative AI into their operations, the talent gap is growing just as fast,” said Prof. Rohan Alexander, Certificate Director, Technical Skills and Curriculum (Faculty of Information and Department of Statistics, Faculty of Arts & Science).  

“Employers need professionals that can do more than experiment – they need people who can understand, build, deploy, and scale AI solutions in real-world environments.” 

Building on the success of the DSI Data Science and Machine Learning Software Foundations Certificates [insert link to Palette Certs page], this microcredential is a natural next step for professionals looking to deepen their AI capabilities. Although this microcredential is open to anyone interested, learners who have completed the DSI Certificates can register for the microcredential at a subsidized price with the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada.   

The three-week, Deploying AI microcredential focuses on the technical know-how and practical strategies needed to take AI from prototype to production. Participants will gain in-demand expertise in model evaluation, prompt engineering, and navigating deployment frameworks, equipping learners with practical skills to operationalize AI models in production environments.  

Emphasizing real-world applications and toolsets, learners are empowered to immediately contribute to AI integration initiatives.. Whether aiming to innovate in industry, accelerate research, or modernize government systems, learners gain the confidence to deploy generative AI tools at scale.  

Learners will also hear directly from an industry leader applying AI in practice and University of Toronto faculty will provide cutting-edge insight into the landscape of generative AI research and its applications. 

“Whether you’re in tech, finance, healthcare, or government, the ability to understand and apply LLMs in real-world settings is quickly becoming essential,” said Lisa Strug, Director, Data Sciences Institute and Professor in the Departments of Statistical Sciences and Computer Science (Faculty of Arts & Science) and the Division of Biostatistics (Dalla Lana School of Public Health) at the University of Toronto.  

“As a hub for professional data science and AI training, we’ve created Deploying AI to help busy professionals and employers build hands-on expertise in operationalizing AI models.” 

Deploying AI microcredential launches in October 2025. This microcredential will be the first in a new series of DSI microcredentials, with Analytical Toolbox for Genetics to launch in 2026. 

Get notified when registrations for Deploying AI open. 

By: Cormac Rea

Photo: Justin Lenis Photography

Leading scholars, industry professionals and VR enthusiasts again convened at the second annual Questioning Reality: Explorations of Virtual Reality (VR) and our Social Future conference – a three-day conference to explore the future of virtual reality (VR) and its impact on social interactions in mediated environments, encompassing VR, augmented reality (AR), extended reality (XR), mixed realities (MR) and the next generation of AI driven immersive environments  

Hosted by the Data Sciences Institute (DSI) — the University of Toronto multidisciplinary hub for data science innovation and collaboration — the conference was co-led by the DSI’s Bree McEwan, a professor in the Institute for Communication, Culture, and Information Technology (ICCIT) at the University of Toronto Mississauga and Sun Joo (Grace) Ahn, director of the Center for Advanced Computer-Human Ecosystems and professor at the University of Georgia. 

“We look forward to welcoming new ideas, new synergies and discussion at this edition of Questioning Reality. With the AI boom, things that were not possible – even months ago – are now possible. We want to lean into this space and start this discussion of how generative AI can shape communication and interactions in immersive spaces,” said Ahn. 

“The connection between VR and data science is intertwined to the extent that – when we get into investigating VR – everything is data,” noted McEwan.  

“Our mission is to connect the people doing the work behind data science – engineers, computer science, data science etc. – with the people who are developing and exploring areas related to VR and its impact.”

By: Cormac Rea

Get to know Professor Meredith Franklin, who joined the Data Sciences Institute (DSI) as the Associate Director, Joint Initiatives this year. 

Franklin is an Associate Professor jointly appointed in the Department of Statistical Sciences and School of the Environment, Faculty of Arts & Science at the University of Toronto. She is also the Master of Science in Applied Computing (MScAC), Data Science concentration lead. 

In the Associate Director, Joint Initiatives role at the DSI, Franklin will be responsible for developing joint programming opportunities with other university units. She will draw on her substantial experience developing educational data science programs that have leveraged offerings across departments, faculties and external partners, creating opportunities for students to learn skills that are applicable in the classroom and on-the-job. 

Franklin’s own interdisciplinary research centres on using data science to better understand how the physical environment affects public health. She has been a leader in developing spatiotemporal methods that leverage large ground- and space-based datasets to characterize human exposures to environmental factors including air pollution, wildfires, oil and gas flaring, noise, artificial light at night, and greenspace.   

How did you first become aware of the DSI and what led to your role as Associate Director, Joint Initiatives? 

I became familiar with the Data Sciences Institute (DSI) shortly after arriving at the University of Toronto, in part due to its strong ties with the Department of Statistical Sciences. From the beginning, I was impressed by the breadth of opportunities the DSI provides for students and postdoctoral researchers.  

When Lisa Strug asked me to join the DSI, I didn’t hesitate for a moment. I feel that my research and teaching closely align with the institute’s mission. I am deeply committed to ensuring that data science maintains a strong, visible presence at the University of Toronto. The DSI serves as a flagship institute in this space, and its reputation across campus speaks volumes. I am genuinely excited and proud to be part of it. 

Please speak to the research you do with your cross-appointment in the School of the Environment and Department of Statistical Sciences? 

My research is deeply grounded in data science, with a strong emphasis on machine learning and AI tools.  

I primarily work in environmental exposure assessment, where I integrate data from a range of sources including ground measurements, space-based satellite instruments, and climate models to estimate human exposures to environmental hazards. These exposure estimates are then used in environmental health and epidemiological study to better understand how environmental factors affect health outcomes. 

A central focus of my work is on air quality, specifically assessing pollutants such as particulate matter, ozone, and nitrogen dioxide. I develop high-resolution spatiotemporal exposure models at regional to global scales, which are critical for supporting large-scale epidemiological investigations into the health impacts of air pollution. 

What role does AI play in your data science protocols for research? 

Data science and AI play a central role in my research. Much of my work has pioneered the use of satellite images for environmental applications, which requires processing vast amounts of data with sophisticated tools to extract meaningful insights. Several years ago we began using neural networks to generate exposure estimates from satellite images, and since then we have expanded our approaches to incorporate state-of-the-art AI techniques including transfer learning and generative models. While these methods are often associated with large language models, we have been adapting them for environmental data applications.    

Transfer learning, in particular, has been instrumental in managing the challenge of working with large volumes of satellite imagery when only limited ground-truth measurements are available. By training models on available data and applying them to broader domains, we are able to generate robust predictions beyond the original training set. Generative AI has similarly enhanced our work, enabling us to produce high-resolution exposure maps from lower-resolution satellite data. Together, these techniques allow us to generate realistic, spatially and temporally detailed environmental exposure estimates. 

We are also incorporating physics into AI through physics-informed neural networks, a novel and increasingly important approach in environmental modeling. By embedding physical processes, such as advection and diffusion, as partial differential equations within the network architecture, we can ensure that the predicted evolution of air pollutant concentrations over space and time remains physically realistic and scientifically credible. 

Ultimately, our goal is to build AI systems that do more than just fit the data. We want them to respect the underlying constraints and structure of the physical world to produce estimates that are both accurate and credible within the field of environmental exposure science. 

I believe that the responsible application of AI requires a strong foundation in data science and statistical principles. Understanding underlying data structures, model assumptions, and statistical reasoning is essential for applying advanced AI tools effectively. In my view, these foundational elements must be fully integrated into any scientific use of AI, particularly in fields like environmental modeling, where rigor, transparency, and interpretability are critical. 

Would you tell us a little about your experience building data science educational programs? 

I came to the University of Toronto just three years ago, after spending nearly 12 years at the University of Southern California (USC). At USC, I served as a faculty member in Biostatistics and, around 2018–2019, led the development of a new Public Health Data Science program. Setting up the program required extensive collaboration across departments at USC. 

Our aim was to focus on applied data science within the specific domain of public health where there was a clear and growing need. While USC already had a data science program based in the computer science department, our aim was to develop a professional master’s program targeted toward students who had quantitative backgrounds in domains outside of statistics and computer sciences.  

Bridging different disciplines and organizational structures was a key part of launching a successful program. We developed new courses, leveraged existing ones, and partnered with computer science to offer students a program that merged technical and theoretical rigor with the applied needs of data science students. 

The first cohort began in 2020—a challenging time to launch a new program in the midst of the COVID-19 pandemic, but we managed successfully. Through this experience, I gained valuable expertise in building interdisciplinary programs from the ground up. I look forward to bringing that experience to the DSI, helping to develop new data science programming and training initiatives by collaborating with multiple units to create opportunities that meet the evolving needs of students. 

Please comment on the role that data science plays in your current work with respect to training that you develop or teach? 

Currently I teach a data science course for undergraduate students in the Joint Statistics and Computer Science Program. In developing this course, I built upon the introductory graduate-level course I offered as part of the USC data science program, adapting it to suit the needs and skill levels of undergraduates. 

In developing data science training programs, my focus is not only on theory but also on preparing students with practical skills they need to succeed in the workforce. I strive to include tools and techniques that are often not covered in traditional coursework, such as accessing and working with real-world data, querying APIs, web scraping and parallel processing tools needed for managing large and complex datasets. These are critical skills for both scientific research and industry careers. 

It’s important to me to stay closely connected to industry trends and ensure the tools and methods I teach remain current and relevant. I update my course materials every year to reflect advances in the field and to respond to the evolving needs of students aiming for careers in data science. My goal is to equip students with both strong foundational knowledge and the hands-on skills that will make them competitive in the job market.

By: Cormac Rea

As Electric Vehicles (EVs) have become a more familiar sight on our streets and highways, one may wonder – has there been a corresponding effect on public health from reduced traffic pollution?

In a paper recently published in the journal, Environmental Research, a Data Sciences Institute (DSI)– funded research team showed that EV sales in the US have had a positive and measurable impact on childhood asthma cases.

“There are many policies in the US that are specifically focus on reducing the burden of asthma, but none of these policies directly address the asthma cases stemming from traffic-related air pollution,” said DSI Doctoral Student Fellow, Harshit Gujral, lead-author of the paper entitled, Emerging evidence for the impact of Electric Vehicle sales on childhood asthma: Can ZEV mandates help?

“Previous research shows that around 18-42 per cent, which is a huge number, of all cases of childhood asthma are attributed to traffic-related air pollution,” he added. “So clearly there is this gap between what is covered in the major policies and actual effectiveness in reducing asthma.”

Along with co-authors and DSI proposal supervisors – Steve Easterbrook (Department of Computer Science, Faculty of Arts and Science, University of Toronto), Meredith Franklin (Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto) and  Paul Kushner (Department of Physics, Faculty of Arts and Science, University of Toronto) – Gujral and team were able to employ a cross-department approach that leveraged expertise across various areas and departments.

“It was clear in this case how important it was to bring together different skills and work collaboratively,” said Franklin.

“A key component was wrangling multiple nationwide data sets from various sources and making them work together in one cohesive analysis.”

Gujral also highlighted the DSI funding as creating a pathway for researchers to focus on their work over a sustained period without distraction.

“The DSI provided three-year funding, which meant that I have not had to apply for the funding every year and could just focus on my research and outcomes,” he said.

“The DSI funding created the bandwidth to do exactly that.”

Using childhood asthma as a proxy due to its widespread impact on the population, the research team relied on publicly available datasets from the U.S. Centers for Disease Control and Prevention from 2013-2019, as well as independently obtained EV sales data

“Employing linear mixed models from data science, we were able to find the associations between the sales of EVs and the cases of asthma due to traffic-related air pollution,” explained Gujral.

The research team found that for every 1,000 new gas-powered vehicles sold, there was one new case of childhood asthma. The team also found that replacing approximately 21 per cent of these sales with electric vehicles appeared to be sufficient to halt rising asthma rates caused by new vehicle sales. However, this number varied depending on the state and various factors — such as population density and the number of existing gas-powered vehicles on the road.

For instance, in some states, replacing just seven per cent of gas car sales with electric vehicles might be enough to halt rising asthma rates caused by new vehicle sales. But in other states, 42 per cent of new car sales had to be electric vehicles in order to have any impact.

“A fundamental finding of this research is that the health impacts of EVs will only manifest when EVs replace existing non-EV vehicles,” said Gujral. “If one simply adds more EVs on the road, it might not result in same health benefits.”

The research team’s findings indicate there’s already a measurable public health benefit being seen in the U.S. from the increase of electric vehicles on the road.

“A 36-77 per cent fleet share of electric vehicles should minimize the asthma burden due to reducing the amount of nitrogen dioxide emitted from gas-powered automobiles, but this doesn’t eliminate all the pollutants that are produced by EVs,” said Gujral.

“Next we want to go to the level of ZIP code to understand this problem a bit more and, at the same time, look further at the socioeconomic implication as low-income communities are the ones who are the most disproportionately impacted by traffic-related air pollution.”