Data Sciences Institute (DSI)

by Sara Elhawash

In today’s data-driven world, organizations face the challenge of effectively utilizing data to advance their work. Through new research funding awarded to the Data Sciences Institute (DSI) by Mitacs, DSI will connect industry with the next generation of data science leaders for Data-driven Decisions & Discovery: Innovation for Transformative Impact. The umbrella funding for 30 research internships is a reflection of the commitment by both DSI and Mitacs to equip industry and organizations, researchers and students with opportunities and skills needed to harness the power of data in real world applications.  

Mitacs, a national non-profit research organization that fosters growth and innovation, will enable academic and industry research collaborations through research internship opportunities for students in DSI’s Summer Undergraduate Data Science (SUDS) program. Mitacs funds matches industry contributions to provide stipends for students who will also participate in the SUDS data science bootcamp and professional development programming. This is an opportunity for organizations to access data science students for research internships at a rate subsidized by Mitacs.   

“DSI values the collaboration with industry and organizations,” says Professor Laura Rosella, DSI Associate Director of Education and Training. “These partnerships enrich the academic experience for students and provide our partners with access to cutting-edge research and emerging talent.”  

The four-month SUDS program provides students with data science training throughout the summer. Students engaged in industry projects benefit from participation in SUDS programming, focusing on career growth and professional development. Scholars actively take part in sessions that cover topics from scientific abstract writing to effective networking, as well as presenting their work at the SUDS Showcase in August.  

“The Institute’s collaborative environment empowers partners to make informed decisions and implement data science solutions in their operations and presents them with the opportunity to tap into a pool of skilled interns who contribute fresh perspectives, innovative ideas, and immediate value to ongoing projects,” says Sumaiya Hossain, DSI’s Partnership & Business Development Officer. “We support organizations for the Mitacs application process, and our MITACS umbrella award allows for a quick timeline from application to funding notifications.” 

As DSI continues to build bridges between academia and industry, the Institute is shaping the future of data science and contributing to the broader goal of advancing data science for societal benefit. “Our goal,” says Sumaiya “is to ensure that data science work has a real-world impact. By connecting with external partners, we facilitate a two-way exchange of knowledge and expertise.”  

Together, industry and scholars can turn data into decisions, ideas into innovations, and dreams into reality. DSI offers an exhilarating vision of a brighter, data-driven future, where collaboration, innovation and talent development converge. 

Learn more about the DSI Mitacs Accelerate funding here. 

by Sara Elhawash

In the nuanced landscape of patient care, unlocking valuable insights is dependent on navigating the vast realms of data. However, what if the data is neither easily accessible nor user-friendly? That is where the expertise of the Data Sciences Institute’s (DSI) software development support team becomes essential. The DSI team developed a new user-friendly web portal to seamlessly and securely distribute individualized healthcare quality reports for the General Medicine Quality Improvement Network (GeMQIN), a program of Ontario Health. These reports are developed by the GEMINI team, based at St. Michael’s Hospital (a site of Unity Health Toronto).

Recognized for its deployment of a data and analytics platform, GEMINI harnesses information from hospital computer records, playing a vital role in generating insights to improve healthcare delivery. Holding data from over 30 Ontario hospitals, the project is Canada’s largest hospital data sharing network for research and analytics.

DSI’s software development program provides faculty and scientists access to skilled developers who refine existing software tools to enhance usability, robustness and functionality.

“The DSI software support provided web development capacity and skillset that greatly expedited our timeline in achieving this major project deliverable. We are excited to launch this new GEMINI portal in the coming months for GeMQIN and look forward to a much more streamlined process of delivering healthcare quality reports,” says Denise Mak, Director of Data Science & Innovation, GEMINI.

New professional certificates in Data Science and Machine Learning Software Foundations, launched by U of T’s Data Sciences Institute and powered by Upskill Canada, will prepare workers for success in these fast-growing fields. Photo: skynesher via Canva, Getty Images.

by Tyler Irving

A new training initiative launched by the University of Toronto’s Data Sciences Institute (DSI) is helping Canada meet its growing need for talent in data science and machine learning

Applications for the DSI Data Science and Machine Learning Software Foundations Certificates opened in October to strong demand. DSI is now gearing up for a second session, scheduled to commence on January 15.

By 2026, digital literacy is projected to be essential for 90 per cent of jobs in Canada

The certificates offer affordable, flexible and rigorous upskilling opportunities, designed for learners with a university, college degree or diploma who have three years or more of work experience. 

Prospective DSI Certificate participants can be employed or actively seeking employment and do not need experience or education in the field of data science. These certificates are accessible to individuals from all backgrounds, and do not require prior affiliation with the University.

The certificates are powered by Upskill Canada, a national initiative powered by Palette Skills and funded by Innovation, Science and Economic Development Canada (ISED). Upskill Canada is designed to meet the talent needs of high-growth sectors while building a more inclusive economy.

Supported by funding from Innovation, Science and Economic Development Canada’s (ISED) Upskilling for Industry Initiative, more than 15,000 Canadian workers will benefit from an innovative approach to skills training. Central to the Upskill Canada initiative is the role of community training providers, who work closely with local and national employers to identify precise suites of skills being sought by industry. Equipping workers with these skills will create new career pathways for Canadians and better position Canadian companies to compete both domestically and internationally.

“What we’re hearing from our partners in industry is that targeted training in key areas can greatly increase the available talent pool in this fast-moving sector,” says Lisa Strug, Academic Director of the 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 U of T. Strug is also a Senior Scientist at The Hospital for Sick Children.

“We’re pleased to be able to leverage U of T’s leadership in machine learning and data sciences to provide new opportunities for workers in the digital economy.”

“Through the industry advisory group, prospective employers like Thomson Reuters are actively engaging with the Data Sciences Institute as they develop learning opportunities that address the evolving data science and machine learning demands across small, medium, and large-sized enterprises,” says Carter Cousineau, Vice President, Data and Model (AI/ML) Governance and Ethics, Thomson Reuters.

“This collaborative approach helps ensure learners gain the necessary skill sets to pursue new roles, or identify opportunities for advancement, in this swiftly changing landscape.”

Both certificates offer foundational concepts in data science and machine learning knowledge and provide opportunities for practical application through employer case studies. Each certificate also includes sessions dedicated to career advancement, from support for resume writing to networking and interview skills development.

The technical and job readiness programming will be delivered as online modules with in-person and hybrid opportunities for professional networking. Certificate recipients will be well positioned for roles such as data analysts, data managers or applied machine learning analysts.

The courses and job readiness sessions are offered part-time, allowing learners time to balance existing commitments and still accomplish their career goals. Over the course of the next two years, five cohorts of learners are expected to complete the 16-week certificates.  Initially, the training will be offered to learners at a substantially reduced rate of $425 (+HST) per certificate, thanks to the support of Upskill Canada. The DSI has also committed accessibility funding for those with financial need.

“We’re so proud to formally launch Upskill Canada with our inaugural class of workers and training service providers,” says Rhonda Barnet, CEO of Palette Skills, which was chosen by ISED to run the Upskill Canada initiative.

“This is a big first step – but it’s only the beginning. We’re looking forward to working with our supporters in government and industry to upskill many more Canadians so they can transition into high-demand roles in the modern workforce – and help fast-growing companies achieve their full potential.”

by Sara Elhawash

When purchasing foods, many consumers give food labels cursory scans, taking in information such as calorie levels or sodium content. Why is streamlining this process crucial from a public and policy perspective? 

Creating and maintaining the databases needed by researchers and others to establish food policies and monitor the food supply is a significant task. This involves classifying and analyzing hundreds of thousands of foods, a process that is typically done manually and infrequently. 

Guanlan Hu, Postdoctoral Fellow in the Department of Nutritional Sciences (Temerty Faculty of Medicine, U of T), is on a mission to simplify this complex process. Her research explores the use of pre-trained language models and supervised machine learning to analyze unstructured food label text, thereby streamlining food categorization and other important classification tasks. Among her primary goals is to revolutionize the understanding and categorization of ultra-processed foods (UPFs), particularly for the benefit of the public and policy makers. Her aim is to improve public health and streamline the analysis of food, underscoring the broader impact and significance of her research. 

Supervised by Professor Emerita Mary R. L’Abbé (Temerty Faculty of Medicine, U of T), and co-authored by Postdoctoral Fellow Mavra Ahmed and PhD student Nadia Flexner, Hu’s presentation at the DSI Research Day signals a shift in the landscape of food classification and health policy.  

“Using cutting-edge language models and machine learning, we’ve automated food categorization, nutrition quality scoring and food processing level classification,” says Hu. “This streamlines food analysis and holds promise for swift, scalable monitoring of the global food supply, particularly in identifying ultra-processed foods.” 

Leveraging pre-trained language models and the XGBoost multi-class classification algorithm, Hu’s methodology achieved an impressive accuracy score of 0.98 in predicting both major and sub-category classification of foods, outperforming traditional bag-of-words methods and presenting a powerful tool for efficiently determining food categories and food processing levels.  

“The research holds the potential to expedite the monitoring and regulation of ultra-processed foods in the global food supply, offering a transformative impact on public health and regulatory practices,” says Professor L’Abbé. 

This research is part of a DSI Catalyst Grant project, Using deep learning and image recognition to develop AI technology to measure child-directed marketing on food and beverage packaging and investigate the relationship between marketing, nutritional quality and price, awarded to L’Abbé and Professors David Soberman (Joseph L. Rotman School of Management), Laura Rosella (Dalla Lana School of Public Health), and Steve Mann (Edward S. Rogers Sr. Department of Electrical & Computer Engineering, Faculty of Applied Science & Engineering). The Collaborative Research Team includes trainees such as Hu. 

By refining food analysis and offering a better method for policymakers to monitor and regulate UPFs, Hu especially hopes to improve public health and dietary understanding in countries where highly processed foods contribute significantly to daily energy intake, such as Canada, the United States and Argentina, where Hu has applied her work. 

Her just-completed research, though, is simply a first step. “Much like the continual evolution of technology,” says Hu, “our work demands continuous development and evolution in this pioneering field.” 

In the meantime, Hu’s work underscores the potential of machine learning and natural language processing in nutrition sciences and the interdisciplinary nature of such breakthroughs, reflecting the importance Data Sciences Institute grants in fostering collaborative research. 

As a collaborative community, the DSI promotes innovation and facilitates the exchange of ideas, connecting diverse groups of researchers and trainees spanning various disciplines. One of the many ways that trainees can get involved is through the DSI’s Postdoctoral Fellowship, designed to support multi and interdisciplinary training and collaborative research in data sciences. 

By Kate Baggott 

“Causal inference is hard.”  

That’s not a conclusion. It’s an observation Rahul G. Krishnan was brave enough to make at the Forging a Path: Causal Inference and Data Science for Improved Policy Workshop on November 10th to over 100 faculty, students and participants from organizations.  

The difficulty of causal inference is not a matter of methodological rigour or reporting. The difficulty comes from the interdisciplinary nature of the process. The community doing causal inference is not one community, Krishnan reminded those present. Rather, causal inference is a process that engages different communities; biostatisticians, economists, epidemiologists, computer scientists, and data scientists, among others; engage in to make decisions and form policies.  

“Among these communities, different language is used to describe the same phenomenon,” Krishnan said.”  

The workshop was created to bring together practitioners of multiple disciplines who are employing a variety of methodologies. The Data Science Institute funds the Causal Inference Emerging Data Science Program and held the workshop in collaboration with the Forward Society (FOS) Lab. The program was initiated by University of Toronto‘s Linbo Wang (Department of Statistical Sciences, University of Toronto Scarborough), Gustavo J. Bobonis (Department of Economics, Faculty of Arts & Science), Ismael Mourifié (Department of Economics, Faculty of Arts & Science), and Raji Jayaraman (Department of Economics, Faculty of Arts & Science). The workshop was the first of three workshops and a seminar series over the new two-years of the emerging data science program. 

The challenge put to participants was not to create a common language, but to create a shared understanding for how to manage the reams of data collected on human activity and explain it to help policymakers improve their decision-making in all areas from public health to education, and from social security to law and justice.  

Throughout the presentations from practitioners, there was an emphasis on description, shared definitions, and clear communication when working with decision-makers. 

Econometrician and empirical microeconomist Alberto Abadie (MIT Economics) talked about estimating the value of evidence-based decision-making (EBDM) itself in his keynote presentation.  

“Despite the ubiquity of EBDM, we are unaware of empirical tools that organizations can use to assess the value of their EBDM practices,” he reminded attendees of the workshop. “Part of the challenge in evaluating the value of EBDM is that it requires a description of what organizations will do with and without various amounts of evidence that they can choose to generate at some cost.” 

Professor Elizabeth Halloran (Fred Hutchinson Cancer Center)  is a world leader in using mathematical and statistical methods to study infectious diseases and a pioneer in the design and analysis of vaccine studies.  

“Important examples of global public health policies where causal inference with interference can make a difference include vaccines and vaccination programs,” she reminded participants.  

Causal estimates demonstrating indirect effects of intervention programs, she said, can make policies in all fields more cost-effective. 

The workshop concluded with a student-led roundtable discussion where Vahid Balazadeh, Sonia Markes, Stephen Tino, Dario Toman, and Atom Vayalinkal outlined next steps in the efforts to bring together causal inference and data sciences communities.