Data Sciences Institute

Subheadline

Powered by Upskill Canada, the Data Science and Machine Learning Software Foundations Certificates aim to upgrade workers' skills in fast-growing fields

A new training initiative launched by the University of Toronto’s Data Sciences Institute (DSI), an institutional strategic initiative, 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 begin on Jan. 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 degree or college diploma who have three or more years 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 run 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 ISED’s 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, a senior scientist at the Hospital for Sick Children and professor in the departments of statistical sciences and computer science in the Faculty of Arts & Science, and the division of biostatistics in the Dalla Lana School of Public Health.

“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 at Thomson Reuters.

“This collaborative approach helps ensure learners gain the necessary skillsets 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.”

Why is COVID-19 more severe in some people? Researchers use genetics, data science to find out

Christopher.Sorensen — Wed, 25 Oct 2023 14:08:53 +0000

Why is COVID-19 more severe in some people? Researchers use genetics, data science to find out

Christopher.Sorensen Wed, 10/25/2023 - 10:08

Cutline

(Photo by Cole Burston/AFP/Getty Images)

Tyler Irving

Topic

COVID-19

Princess Margaret Cancer Centre

Sinai Health

Sunnybrook Health Sciences Centre

Temerty Faculty of Medicine

Unity Health

Hospital for Sick Children

Mount Sinai Hospital

University Health Network

St. Michael's Hospital

Statistical Sciences

Women's College Hospital

Subheadline

With the help of U of T's Data Sciences Institute, researchers from the university and partner hospitals gathered more than 11,000 full genome sequences from across Canada

Why do some people have a more severe course of COVID-19 disease than others? A genome sequence database created by an international collaboration of researchers, including many from the University of Toronto and partner hospitals, may hold the answers to this question – and many more.

The origins of the Canadian COVID-19 Human Host Genome Sequencing Databank, known as CGEn HostSeq, can be traced to the earliest days of the pandemic.

Lisa Strug, senior scientist at The Hospital for Sick Children (SickKids) and academic director of U of T’s Data Sciences Institute, one of several U of T institutional strategic initiatives, says genetic data was top of mind for her and other researchers in late 2019 and early 2020 as reports of a novel form of coronavirus emerged from China and then other locations across the globe.

Lisa Strug (Photo courtesy The Hospital for Sick Children)

“In my research, I use data science techniques to map the genes responsible for complex traits,” says Strug, who is a professor in U of T’s departments of statistical sciences and computer science in the Faculty of Arts & Science and in the biostatistics division of the Dalla Lana School of Public Health.

“We knew that genes were a factor in the severity of previous SARS infections, so it made sense that COVID-19, which is caused by a closely related virus, would have a genetic component, too.

“Very early on, I started getting messages from several scientists who wanted to set up different studies that would help us find those genes.”

Over the next few months, Strug – who is also the associate director of SickKids’ Centre for Applied Genomics, one of three sites across Canada that form CGEn, Canada’s national platform for genome sequencing infrastructure for research – collaborated with nearly 100 researchers from across U of T and partner hospitals and institutions, as well as other researchers from across Canada to enrol individuals with COVID-19 and sequence their genomes.

Some of the key team members from the Toronto community included:

Stephen Scherer, chief of research at SickKids Research Institute and a University Professor in U of T’s Temerty Faculty of Medicine, as well as director of the U of T McLaughlin Centre
Rayjean Hung, associate director of population health at the Lunenfeld-Tanenbaum Research Institute, Sinai Health, and a professor in U of T’s Dalla Lana School of Public Health
Angela Cheung, clinician-scientist at University Health Network, senior scientist at Toronto General Hospital Research Institute and a professor in U of T’s Temerty Faculty of Medicine
Upton Allen, head of the division of infectious diseases at SickKids and a professor in U of T’s Temerty Faculty of Medicine

The projected was initiated by Scherer and CGEn’s Naveed Aziz, along with Strug, and a $20-million grant was secured from Innovation, Science and Economic Development Canada, administered through Genome Canada.

“We had to go right to the top to get this project funded fast and our labs and teams worked seven days a week on the project right through the pandemic,” Scherer recalls.

Identifying associations between individual genes and complex traits typically requires thousands of genomes – both from those with the trait and those without. Though there was no shortage of cases to choose from, it was critical to gather and sequence DNA – and then organize the data in a way that would be ethical, efficient and useful to researchers now and in the future.

“One of our key mandates at the Data Sciences Institute is developing techniques and programs that ensure that data remains as open, accessible and as re-producible as it can be,” Strug says.

“That vision was brought to bear as we assembled the data infrastructure for this project – for example, ensuring that consent forms were as broad as possible so that this data could be linked with other sources, from electronic medical records to other health databases.

“We wanted to be sure that even after the COVID-19 pandemic was over this could be a national whole genome sequencing resource to ask all kinds of questions about health and our genes. The development of the database and its open nature also enabled Canada to collaborate effectively with similar projects in other countries.”

Partner hospitals and institutions:

The Hospital for Sick Children (SickKids)
Lunenfeld-Tanenbaum Research Institute, Sinai Health
Mount Sinai Hospital, Sinai Health
St Michael’s Hospital, Unity Health Toronto
University Health Network (UHN)
Princess Margaret Cancer Centre, UHN
Ontario Institute for Cancer Research
Women’s College Hospital
Toronto General Hospital, UHN
Baycrest Health Sciences

In the end, the project gathered more than 11,000 full genome sequences from across Canada, representing patients with a wide range of health outcomes. Those data were then combined with even more sequences from patients in other countries under what came to be called the COVID-19 Host Genetics Initiative.

It didn’t take long for patterns to start to emerge. A paper published in Nature in 2021 identified 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19.

Since then, even more data have been added, and subsequent analysis has confirmed the significance of existing loci while also identifying new ones. The most recent update to the project, published in Nature earlier this year, brings the total number of distinct, genome-wide significant loci to 51.

“Identification of these loci can help one predict who might be more prone to a severe course of COVID-19 disease,” says Strug.

“When you identify a trait-associated locus, you can also unravel the mechanism by which this genetic region contributes to COVID-19 disease. This potentially identifies therapeutic targets and approaches that a future drug could be designed around.”

While it will take many more years to fully untangle the effects of the different loci that have been identified, Strug says that the database is already showing its worth in other ways.

“It can be difficult to find datasets with whole genome sequence and approved for linkage with other health information that are this large, and we want people to know that it is open and available for all kinds of research well beyond COVID through a completely independent data access committee,” she says.

“For example, several investigators from across Canada have been approved to use these data and we’ve even provided funding to trainees to encourage them to develop new data science methodologies or ask novel health questions using the CGen HostSeq data.”

“This was a humongous effort, where researchers from across Canada came together during the COVID-19 pandemic to recruit, obtain and sequence DNA from more than 11,000 Canadians in a systematic, co-operative, aligned way to create a made-in-Canada data resource that will hopefully be useful for years to come. I think that was really miraculous.”

U of T's Data Sciences Institute partners with UNICEF to drive innovation in learning

Christopher.Sorensen — Wed, 09 Aug 2023 18:45:29 +0000

U of T's Data Sciences Institute partners with UNICEF to drive innovation in learning

Christopher.Sorensen Wed, 08/09/2023 - 14:45

Cutline

(photo by Biplov Bhuyan/SOPA Images/Lightbox/Getty Images)

Sara Elhawash

Topic

Global Lens

Institute of Health Policy Management and Evaluation

Education

Global

Undergraduate Students

The Data Sciences Institute (DSI) at the University of Toronto is partnering with the United Nations Children’s Fund (UNICEF)’s Frontier Data and Tech team to find new ways to use data to address complex challenges involving children.

Through joint research and training, DSI – a U of T institutional strategic initiative – will work with UNICEF to strengthen the international organization’s ability to use data science to drive innovation in learning.

That includes a partnership focused on using data and artificial intelligence, or AI, to predict student dropout rates and develop better early warning systems that inform decision makers in the development and humanitarian realm.

“This partnership is a significant milestone for our Frontier Data Network, a global community of practice that leverages data science to positively impact the lives of children worldwide,” says Yves Jaques, chief of the Frontier Data and Technology unit at UNICEF.

“Together, we are poised to unlock new insights, drive evidence-based decision-making, and pave the way to a brighter future for children everywhere.”

The effort aligns with DSI’s strategic goal of leveraging the potential of data to promote the greater public good.

As an initial collaboration, Zahra Shakeri, an assistant professor at the Institute of Health Policy, Management and Evaluation (IHPME) at the Dalla Lana School of Public Health, will partner with Manuel Garcia-Herranz, data principal researcher, and Karen Avanesyan, statistics and monitoring education specialist at UNICEF’s Division of Data, Analytics, Planning and Monitoring (DAPM) on a project that aims to revolutionize early warning systems in education through the application of cutting-edge AI technology.

As part of the 2023 Summer Undergraduate Data Science (SUDS) Opportunities Program, the project allows Ziqi Shu, a third-year student studying computer science, cognitive science and mathematics, to gain practical experience by working on fictional case studies focusing on social problems affecting children. By identifying at-risk students and schools with high dropout rates, UNICEF aims to support countries with a strong Education Management Information System (EMIS) and household survey data.

“Our aim is to develop a pilot tool that provides a comprehensive representation of the machine learning-based school dropout prediction landscape, bridging the knowledge gap in this area,” says Shakeri, who is also director of the Health Informatics, Visualization, and Equity (HIVE) Lab at IHPME. “This tool will utilize innovative data analysis and visualization techniques, benefiting researchers, practitioners and other stakeholders in exploring the factors influencing school dropout among children.

“The long-term goal of this project is to harness the power of data science and create an adaptable, publicly accessible system that could support countries in addressing the critical issue of school dropouts. By leveraging AI technology and early warning systems, our aim is to identify and support at-risk students and schools, ultimately safeguarding every child’s right to education.”

The UNICEF-DSI partnership paves the way for further research and training collaborations.

There will be opportunities to connect with the DSI community during its DSI Research Day on Sept. 27, where Garcia-Herranz will deliver the keynote address and Jaques will participate in a panel discussion on developing an effective data science workforce. The discussion will focus on equipping graduates with essential data science skills required in today’s diverse fields and industries. DSI Research Day aims to showcase the work of the DSI community, fostering connections and engagement among academia, industry and government stakeholders.

“By combining our community’s expertise in data science with UNICEF’s commitment to driving results for children globally, we have the opportunity to make a profound impact,” says Lisa Strug, director of the Data Sciences Institute.

“Through our joint efforts, we aim to strengthen UNICEF’s knowledge and capacities in utilizing data science methodologies, fostering innovation in learning and ultimately creating a brighter future for children worldwide.”

Researchers accelerate development of cellular therapies for damaged tissues

siddiq22 — Tue, 25 Jul 2023 16:17:02 +0000

Researchers accelerate development of cellular therapies for damaged tissues

siddiq22 Tue, 07/25/2023 - 12:17

Cutline

From left: PhD candidate Oreoluwa Kolade, Professor Julie Audet and Assistant Professor Sowmya Viswanathan are working to identify the optimal cell culture conditions for different donors without doing full sets of detailed experiments (photo by Safa Jinje)

Safa Jinje

Topic

Breaking Research

Institute of Biomedical Engineering

Temerty Faculty of Medicine

Faculty of Applied Science & Engineering

Graduate Students

machine learning

Regenerative Medicine

Subheadline

Optimizing conditions to culture therapeutic cells can help reduce the costs and labour of experiments

Innovations in the ways that human cells are grown in laboratories could help speed up the development of cellular therapy, a branch of regenerative medicine that targets diseases that are currently incurable.

Julie Audet, a professor in the University of Toronto's Institute of Biomedical Engineering in the Faculty of Applied Science & Engineering, is working to address some of the most significant challenges related to producing therapeutic cells.

Her research on cell and tissue engineering aims to enhance the therapeutic properties of lab-grown human cells to ensure that they are ready for clinical application.

Audet and her team are developing complex computational algorithms to optimize laboratory experiments for academic and industrial researchers. These tools will allow researchers to create the best conditions to culture therapeutic cells.

Cellular therapy acts by transplanting enhanced human cells into the body to replace or repair damaged tissue and cells, in order to treat a variety of diseases and conditions – an approach sometimes referred to as a living drug.

“But before cells can be transplanted into a patient, we want to ensure that the cells are not contaminated with compounds that can trigger an adverse reaction,” Audet says.

“We also need to enhance the therapeutic properties of the cells in a culture process to effect a positive medical outcome.”

There are many factors to consider when designing cell-culture experiments. For example, there are numerous expensive reagents to select and optimize at different doses – these substances are used to test chemical reactions carried out by the cell. There are also significant technical and biological variations to consider – cells from different donors don’t always behave the same way in culture.

“Our algorithms become necessary when experiments are very costly to execute and are extremely labour-intensive,” Audet says.

“They are especially useful when the results are impossible to predict because of the complexity of the biological systems under study. In that case, it would not be feasible or possible for researchers to use conventional approaches to design and execute their experiments.”

With complex computational algorithms based on machine learning, Audet and her team can design experiments that are not only feasible and offer a greater chance of success, but are also less costly, with fewer resources needed to execute the experiment.

An earlier prototype of such algorithms was used by Audet’s lab to make a serum-free T-cell medium to treat blood disorders. The algorithm was also used by Craig Simmons, a professor in the Institute of Biomedical Engineering and the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering, and Neal Callaghan, a PhD graduate from the institute and a former researcher in Simmons' lab, to develop culture media for cardiomyocytes (cardiac muscle cells), a process that is now being commercialized.

“Cardiac tissue engineering is an important application for our tools because when it comes to heart failure and heart disease, there are many conditions that can’t currently be cured,” Audet says.

“Cellular therapy offers a promising approach to treat heart failure and other cardiac ailments.” 

Audet is working with Sowmya Viswanathan, a researcher at the University Health Network's Krembil Research Institute and an assistant professor in the Institute of Biomedical Engineering and the Temerty Faculty of Medicine, on research that addresses the distinct characteristics of cell donors that make cells behave differently in culture.

Viswanathan is developing cellular therapies using mesenchymal stromal cells to combat osteoarthritis, a chronic inflammatory disease.

“We have seen that different mesenchymal stromal cell donors prefer different growth conditions,” Viswanathan says. “This algorithm helps us identify optimal conditions for different donors without doing a full set of detailed experiments.”

The goal of her collaboration with Audet is to also develop categories of conditions that could be matched to diverse groups of donors based on genetic markers. 

Audet and Viswanathan co-supervise Oreoluwa Kolade, a PhD candidate at the Institute of Biomedical Engineering and a fellow at the Data Sciences Institute – one of several U of T institutional strategic initiatives – whose work brings together tissue engineering and data science.

“My research involves the statistical design of experiments, looking at the numerous factors that can impact the cell expansion process, such as oxygen levels, cell density and medium composition,” Kolade says.

“If a researcher wants to see which factors maximize the therapeutic quality of their cells, it isn’t cost-effective to test all possible combinations. So we are trying to design experiments in such a way that researchers can get the highest impact when they see their results.”

This model would allow researchers to narrow down the varying factors and run combinations into a simulation model to see which experiments they would need to do to get the best cells.

“We are currently working on the commercialization of the latest version of one of the algorithms that includes these machine-learning modules to help design experiments,” Audet says.

“We hope this tool will help make cellular therapies both widely available and more accessible by accelerating the development of these therapies while increasing the effectiveness of the enhanced cells.”

U of T researchers developing AI system to tackle harmful social media content

Christopher.Sorensen — Fri, 12 May 2023 19:09:47 +0000

U of T researchers developing AI system to tackle harmful social media content

Christopher.Sorensen Fri, 05/12/2023 - 15:09

Cutline

(photo by Chesnot/Getty Images)

Sara Elhawash

Topic

Schwartz Reisman Institute for Technology and Society

Artificial Intelligence

Faculty of Information

Social Media

U of T Scarborough

Hate speech and misinformation on social media can have a devastating impact, particularly on marginalized communities. But what if we used artificial intelligence to combat such harmful content?

That’s the goal of a team of University of Toronto researchers who were awarded a Catalyst Grant by the Data Sciences Institute (DSI) to develop an AI system to address the marginalization of communities in data-centric systems – including social media platforms such as Twitter.

The team consists of three faculty members. Syed Ishtiaque Ahmed is an assistant professor in the department of computer science in the Faculty of Arts & Science and a fellow of the Schwartz Reisman Institute for Technology and Society. Shohini Bhattasali is an assistant professor in the department of language studies at U of T Scarborough. Shion Guha is an assistant professor cross-appointed between the department of computer science and the Faculty of Information, the director of the Human-Centered Data Science Lab and a faculty affiliate of the Schwartz Reisman Institute for Technology and Society.

Their goal is to make content moderation more inclusive by involving the communities affected by harmful or hateful content on social media. The project is a collaboration with two Canadian non-profit organizations: the Chinese Canadian National Council for Social Justice (CCNC-SJ) and the Islam Unravelled Anti-Racism Initiative.

Historically marginalized groups are most affected by content moderation failings as they have lower representation among human moderators and their data is less available for algorithms, Ahmed explains.

(L-R) Syed Ishtiaque Ahmed, Shohini Bhattasali and Shion Guha (supplied photos)

“While most social media platforms have taken measures to moderate and identify harmful content and limit its spread, human moderators and AI algorithms often fail to identify it correctly and take proper actions," he says.

The team plans to design and evaluate the proposed system to address potential Islamophobic and Sinophobic posts on Twitter. The AI system aims to democratize content moderation by including diverse voices in two primary ways: first, by allowing users to contest a decision, the moderation process becomes more transparent and trustworthy for users who are victims of online harms. Second, by taking user input and retraining machine learning (ML) models, the system ensures that users’ contesting positions reflect on the prescreening ML system.

“Annotating data becomes challenging when the annotators are divided in their opinions. Resolving this issue democratically requires involving different communities, which is currently not common in data science practices," Ahmed notes.

"This project addresses the issue by designing, developing and evaluating a pluralistic framework of justification and contestation in data science while working with two historically marginalized communities in Toronto.”

The AI system will integrate the knowledge and experiences of community members into the process of reducing hateful content directed toward their communities. The team is using a participatory data-curation methodology that helps them learn about the characterization of different kinds of harmful content affecting a community and includes members of the corresponding community in the data-labelling process to ensure data quality.

“We are grateful to DSI for their generous support for this project. The DSI community has also helped us connect with people conducting similar research and learn from them," Ahmed says, adding that his team's research is expected to have far-reaching impacts beyond the two communities it is currently focused on.

Shifting gears: How data science led Madeleine Bonsma-Fisher from studying germ models to bike lanes

Christopher.Sorensen — Tue, 09 May 2023 20:03:34 +0000

Shifting gears: How data science led Madeleine Bonsma-Fisher from studying germ models to bike lanes

Christopher.Sorensen Tue, 05/09/2023 - 16:03

Cutline

Madeleine Bonsma-Fisher, a post-doctoral researcher at U of T's Data Sciences Institute, is studying traffic "stress" in Toronto in order to pinpoint where more cycling infrastructure is needed (photo by Johnny Guatto)

Adina Bresge

Topic

Cycling

Faculty of Applied Science & Engineering

Cities

Physics

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 Data Sciences Institute, an institutional strategic initiative 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 U of T’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 genome editing, 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, published in the journal eLife earlier this year, 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 U of T Coders 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. U of T 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 Bike Ottawa, where she contributed data analysis to report on how the COVID-19 crisis has influenced cycling trends and advocated for a bike-share program.

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.”

Gift from Schmidt Futures to spark a revolution in AI-based STEM research at the University of Toronto

Christopher.Sorensen — Wed, 26 Oct 2022 21:00:39 +0000

Gift from Schmidt Futures to spark a revolution in AI-based STEM research at the University of Toronto

Christopher.Sorensen Wed, 10/26/2022 - 17:00

Advancement Staff

Topic

Acceleration Consortium

Artificial Intelligence

Chemistry

Deep Learning

Hospital for Sick Children

Meric Gertler

Vector Institute

With the goal of accelerating scientific research through the application of artificial intelligence, Schmidt Futures is investing $148-million in nine global universities, including the University of Toronto.

The announcement launches the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures. A large-scale initiative supporting the work of early-career scholars in engineering and the natural sciences, such as mathematics, chemistry or physics, the program fosters their uptake of vital tools in artificial intelligence.

Artificial intelligence is not just a field in its own right but also an important tool for research. It can find patterns to enable research that solves important challenges – across fields from climate change to human health and beyond – more quickly and more efficiently. To accelerate the adoption of AI into scientific methodologies, the Schmidt AI in Science Postdocs initiative aims to spark a significant increase globally in the number of scientists working with cutting-edge AI tools.

“As the home of deep learning, the University of Toronto is proud to partner with Schmidt Futures on this forward-looking program, which will accelerate humanity’s ability to meet some of the most important challenges of our time,” said Meric Gertler, president of U of T. “The Schmidt AI in Science Postdocs program provides tremendous opportunities for the emerging generation of STEM researchers. On behalf of the U of T community, I would like to thank Schmidt Futures for their vision and generosity.”

U of T is the only Canadian university chosen for the program. Its highly diverse community – its existing postdoctoral fellows come from 89 countries – and global links make it an ideal centre to support the Schmidt AI in Science Postdocs global network.

“The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures, will create an immediate acceleration of AI applications across several disciplines. We are proud to partner with these exceptional universities, especially the University of Toronto, on this important initiative,” said Stu Feldman, chief scientist at Schmidt Futures. “The Fellowship will provide these postdoctoral fellows with the advanced tools to increase the scope and speed of their research while discovering new and innovative use cases for AI within their field. U of T’s thoughtfully crafted program design, strong base of alumni in the scientific world, high volume of leading-edge scientific research, and deep history of important AI research give us full confidence in an impactful outcome.”

Creating a cohort of AI-fluent researchers

The Schmidt AI in Science Postdocs program will support nearly 300 postdoctoral fellows each year for six years. U of T hosts 10 in the first year of the program and 20 annually thereafter. The support includes networking and research collaborations between participating universities; a robust series of workshops, conferences and lectures; and training in how to apply AI techniques.

The fellows will not only expand the scope of their own research but will also establish their careers as AI-fluent scientists, ready to expand new research methodologies across a range of fields through their future work.

Apply to become an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow. Visit schmidtfellows.utoronto.ca. Deadline is Nov. 21, 2022.

At U of T, the Schmidt AI in Science Postdocs becomes one of the university’s most prestigious postdoctoral programs. Working closely with the Vector Institute for Artificial Intelligence, two senior faculty members lead the initiative. Alán Aspuru-Guzik is the director of U of T’s Acceleration Consortium, a global network of researchers, industry and government that is leading a convergence of materials science with AI and robotics. Lisa Strug is the director of U of T’s Data Sciences Institute, one of the world’s largest clusters of scientists working on innovative approaches to data that drive actionable research insights.

The Canada Research Chair in Genome Data Science, Strug is a statistical geneticist in the Faculty of Arts & Science who develops novel approaches to identifying the genetic contributors to complex human disease. She is cross-appointed to the Dalla Lana School of Public Health and the Hospital for Sick Children, and is also the director of the Canadian Statistical Sciences Institute, Ontario Region.

As a CIFAR AI Chair at the Vector Institute for Artificial Intelligence and the Canada 150 Research Chair in Theoretical and Quantum Chemistry, Aspuru-Guzik works to accelerate the discovery of new molecules and materials needed for a sustainable future, using novel, disruptive approaches. He is also a Google Industrial Research Chair in Quantum Computing, and is the founder of two startups.

A wide-ranging vision for solving global challenges

Schmidt Futures is a philanthropic initiative founded by Eric and Wendy Schmidt that brings talented people together in networks to prove out their ideas and solve hard problems in science and society.

The CEO of Google from 2001 to 2011, Eric Schmidt has hands-on experience with the transformative power of finding and supporting innovative minds – at scale. Wendy Schmidt, a journalist and competitive sailor, has created multiple non-profits in the areas of global sustainability and human rights. With Schmidt Futures, their focus is on building networks of visionary minds with the talent to solve society’s problems.

U of T is Canada’s leading research university and the home of seminal work in artificial intelligence, from deep learning and neural networks to the interfaces between AI and the natural sciences.

U of T is also home to a vibrant and diverse community of nearly 1,000 postdoctoral fellows. More than half of U of T postdocs work in the physical sciences. Two thirds go on to careers in academia and a third go on to work in the private sector. The Schmidt AI in Science Postdocs will only enrich this remarkable environment for discovery and impact.

“Thank you, Schmidt Futures, for this generous vote of confidence in U of T programming and in the exceptional talents who thrive in our postdoctoral programs,” said Leah Cowen, U of T’s vice-president, research and innovation, and strategic initiatives. “The prestigious Schmidt AI in Science Postdoc program will help catalyze novel solutions to tough challenges. It is the kind of thoughtful support that powers real innovation.”

From mapping stars to helping youth escape poverty, student researchers break new ground

Christopher.Sorensen — Tue, 04 Oct 2022 20:26:54 +0000

From mapping stars to helping youth escape poverty, student researchers break new ground

Christopher.Sorensen Tue, 10/04/2022 - 16:26

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(Photo by Mostafa Alkharouf/Anadolu Agency via Getty Images)

Tina Adamopoulos

Topic

Black Research Network

Groundbreakers

Faculty of Applied Science & Engineering

School of Cities

Astronomy & Astrophysics

Grace Yu remembers marvelling at the night sky as a child. Today, that childhood memory lives on as she studies the stars that make up the Milky Way.

Grace Yu

Working with Ting Li and Joshua Speagle, assistant professors in the University of Toronto’s David A. Dunlap department of astronomy and astrophysics in the Faculty of Arts & Science, Yu is working on a new project, titled “Mapping the Milky Way a Million Light Years.”

The team will characterize and track blue horizontal branch (BHB) stars, which, because of their brightness, can be detected when they are located at the periphery of our own galaxy to measure the density profile of the Milky Way – especially at its periphery.

“The end goal is to detect BHB stars, all the way down to 300 kiloparsecs, roughly a million light-years away, and characterize their density to distance,” Yu says. “Then we will use these BHB to understand more about the Milky Way.”

Yu is one of 36 undergraduate students from across Canada to be selected for the 2022 SUDS Scholars program at U of T’s Data Science Institute’s (DSI). The program pairs students interested in a data science career with DSI researchers to conduct interdisciplinary, hands-on research. It is one of several opportunities that the DSI hosts to foster data science innovation and collaboration.

At present, little is known about the galaxy beyond 100 kiloparsecs, a measure of distance in astronomy, where 1 kiloparsec equals 3.6 light-years, a unit of astronomical distance equivalent to the distance that light travels in one year.

Although BHB stars are ideal tracers for mapping our own Milky Way, there is another type of star, the so-called “blue straggler” (BS) stars, which lie in a similar colour space as the BHB stars. But BS stars are generally closer to Earth and cannot travel as far as BHB stars do, making BHB stars ideal to study the periphery of the Milky Way. A goal of this research project is to develop a tool to separate the distant BHB stars from nearby BS stars.

Using publicly available data from the Dark Energy Survey (DES) – one of the largest astronomical cameras that has surveyed the dark sky – the team found a clear indicator of separate patterns between BHB and BS stars thanks to the unprecedentedly high precision data from DES that measures the brightness and colours of the stars better than 0.5 per cent.

In particular, using a machine learning method called the support-vector machine, a supervised learning model associated with learning algorithms that analyze data for classification, Yu has made great progress in identifying clear, separable boundaries between the stars. Yu is also planning to continue this research under the supervision of Li and Speagle.

With first-hand experience blending statistics, machine learning and astronomy for research thanks to the DSI, Yu hopes to explore how computer science is shaping other fields in the future.

Using poetry to elevate the voices of African, Caribbean and Black women with HIV

Yu’s work is just one of several student-led projects supported by U of T’s Institutional Strategic Initiatives (ISI) portfolio, which facilitates tri-campus interdisciplinary collaborations between researchers at U of T and partner institutions.

Lori Chambers

Lori Chambers, a former post-doctoral researcher at the Factor-Inwentash Faculty of Social Work, is using poetry, prose and spoken word performance to offer safe spaces for women to share their experiences living with HIV.

Launched in 2018, the Because She Cares project amplifies the voices of African, Caribbean and Black (ACB) women with HIV who describe the challenges they encounter working for the Canadian AIDS service and allied organizations. The project aims to develop, promote and design approaches that honour community-based, knowledge-sharing methods – and to decolonize Western ideologies in academia.

Chambers is one of nine researchers supported by the Black Research Network (BRN), an ISI that seeks to promote Black research excellence at the university through mentorship, collaboration, investment and community.

With $7,000 in seed funding through the BRN’s IGNITE grant, Chambers and five spoken word artists travelled to Quebec for the International AIDS Conference, one of the largest AIDS conferences in the world, to perform stories from the Because She Cares project.

Listen to a segment of an interview with Lori Chambers.

Chambers explains that while plays and spoken word performances are commonly used in west and south Africa for knowledge mobilization, it is an emerging space in Canada.

“Using arts-based approaches like spoken word, which is grounded in Black performance, in a Canadian space is still very new,” Chambers says.

"Storytelling is an approach that we all know, so why not bring it to the space of academia? We should develop ways of knowing that are concurrent in the communities we work with.”

Last winter, Chambers launched Because She Cares' first spoken word film series. The collection of 12 short films outlined the challenges that African and Caribbean immigrant women who live with HIV face while working in Canadian AIDS service and allied organizations.

One of the films, HIV Stays at Home, explored the ethics around disclosing HIV status in the workplace, a prominent challenge for people who wish to leave the HIV sector. Another, Living This Kind of Life, navigates working poverty and its effect on family dynamics. Other films addressed setting boundaries between the professional and personal – something that can be especially difficult for women who have been hired to work in the HIV services space because of their lived experience with the condition. Chambers says that professional boundaries have the potential to separate HIV-positive health and social work professionals from their own support systems, often within the HIV community.

With interest from other AIDS organizations, Chambers hopes to share the films as an educational tool and develop strategic partnerships with AIDS organizations in Ontario and across Canada.

Would free, unlimited transit provide youth experiencing homelessness with more opportunities?

Emma Blewett

Noah Kelly, a U of T graduate, and Emma Blewett, a third-year civil engineering student, are researching how free, unlimited transit access can improve the quality of life for Toronto youth who are experiencing homelessness.

TAP (Transit Access Project) for Youth is a student-led transit equity research project that seeks to reduce barriers to transit access. It was founded by five undergraduate students as part of Engineers Without Borders (EWB) at U of T, the largest student chapter of the Canadian non-governmental organization that takes strategic action to tackle global poverty in local communities.

Working with two Toronto-based homeless shelters and one transitional youth home, the team gave free PRESTO cards to 36 participants between the ages of 16 to 24 earlier this year. Each one came pre-loaded with a monthly pass. From there, the TAP team documented participants’ experiences with transit before and after receiving the card. This included interactions with transit authorities and riders, safety and social inclusion.

“Transit equity ensures the right to mobility,” says Kelly, co-founder of TAP. “Public transit access in Toronto is vital in exiting the cycle of homelessness because it enables youth to have job opportunities in the urban landscape, which would otherwise be limited to walkable areas.”

With $2,000 support from the Small Grants Program awarded by the School of Cities – an ISI which brings interdisciplinary urban-focused researchers, students, institutions and the public together to build equitable and sustainable cities – the group was able to hire a social worker to attend the interviews to inform youth of any programs or supports to help with their needs.

Guided by Steven Farber, an associate professor in the department of human geography at U of T Scarborough, and Stephanie Begun, an assistant professor in the Factor-Inwentash Faculty of Social Work, TAP is funded in partnership with Metrolinx, the City of Toronto’s Poverty Reduction Strategy Office and the Toronto Shelter Network, along with other stakeholders.

With a final report set to be published early next year, TAP’s data aims to fill a gap in scholarship and inform City of Toronto and TTC policy frameworks on conversations surrounding transit equity.

The project is a replication of a similar initiative that brought free transit to all people experiencing homelessness in Edmonton, Alta, in 2013. While transit discount programs do exist in Toronto, several hurdles make them insufficient, the group says.

In Toronto, the Fair Pass Transit Discount Program offers a 21 per cent discount on an adult monthly pass, which costs approximately $123 instead of the regular price of $156. To be eligible, applicants must be 20 years old or more and prove enrollment in Ontario Works, the Ontario Disability Support Program, a Child Care Fee Subsidy (through Toronto Children’s Services) or the Rent-Geared-to-Income Subsidy – programs that researchers say aren’t by themselves enough to help youth experiencing homelessness access job opportunities.

“We have to think about setting youth up for success after they enter a shelter or transitional home,” says Blewett, financial lead at TAP for Youth. “What keeps me going in this project is being a part of research that will help young people live their lives as they should be.”

This article is part of a multimedia series about U of T's Institutional Strategic Initiatives program – which seeks to make life-changing advancements in everything from infectious diseases to social justice – and the research community that's driving it.

From happiness to health care, undergraduate summer program inspires future data scientists

Christopher.Sorensen — Thu, 18 Aug 2022 13:13:32 +0000

From happiness to health care, undergraduate summer program inspires future data scientists

Christopher.Sorensen Thu, 08/18/2022 - 09:13

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Victoria College's Anthony McCanny is exploring whether gross domestic product (GDP) is a good measure of economic and societal success as part of a Data Sciences Institute summer research program (photo courtesy of the Data Sciences Institute)

Chris Sasaki

Topic

Temerty Faculty of Medicine

Artificial Intelligence

Earth Sciences