Data Sciences Institute

Data Sciences Institute Catalyst Grant Fuels U of T Researchers’ Community-Powered AI to Tackle Harmful Content on Social Media

Hate speech and misinformation on social media can have a devastating impact, particularly on marginalized communities. But what if we use AI 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 to develop an AI system to address the marginalization of communities in data-centric systems – including social media platforms like Twitter. 

The collaborative research team, which consists of Professors Syed Ishtiaque Ahmed, Department of Computer Science (Faculty of Arts & Science), Shohini Bhattasali, Department of Language Studies (University of Toronto Scarborough) and Shion Guha (Faculty of Information), intends to make content moderation more inclusive by involving the communities affected by harmful or hateful content on social media. The project collaborates with two Canadian non-profit organizations: the Chinese Canadian National Council for Social Justice (CCNC-SJ) and the Islam Unravelled Anti-Racism Initiative. 

Professor Ahmed shares that 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. He says, “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.” 

The team plans to design, develop, deploy, 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. 

Ahmed explains, “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. 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 wisdom, 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. They learn about the characterization of different kinds of harmful content affecting a community and include members of the corresponding community in the data labeling 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. Thanks to the wonderful DSI community, whose mission includes  innovating and adopting various data-centric approaches to social justice,” says Ahmed. 

The research project is a promising initiative to address the issue of harmful content on social media and is expected to have far-reaching impacts beyond the two communities it is currently focusing on. 

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

by Adina Bresge

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 postdoctoral research at the 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.”

DSI Summer Undergraduate Data Science Program Empowers Students to Apply Data Science Skills Across Disciplines

This summer, a group of 37 undergraduate students from across Canada will participate in the Data Sciences Institute (DSI) Summer Undergraduate Data Science (SUDS) research opportunity. SUDS offers an enriching summer experience to apply data science methods and tools in various application areas, including humanities, life science, engineering, public health and more. SUDS Scholars are supervised by DSI member researchers across U of T and external funding partners. In addition to their research projects, SUDS Scholars are provided with a full set of data science skills, networking, research and professional development opportunities. 

The SUDS Scholars kicked off their summer programming on May 1 by participating in their welcome and orientation where they met their cohort and shared their research interests. This week, they will also be gaining new skills and knowledge via the Data Science Bootcamp for introductions to Unix Shell, R, Python, and Machine Learning.   

SUDS Scholar Yuanhan Peng, expressed enthusiasm about the program, noting she is “currently strongly interested in education data science and I want to work as a data scientist in the education technology industry. I think that being a SUDS Scholar will provide me with the opportunity to participate in related research and gain more experience.”  

SUDS Scholar, Akil Huang, will be working with Professor Spike Lee, Rotman School of Management on the project, Automated text analysis of liberal and conservative news, which explores the nature of linguistic differences among news outlets. Huang is looking forward to the summer: “As a finance and economics specialist coming from an interdisciplinary background, SUDS will give me the tools, learning resources, and mentorship to delve deeper into my area of focus within academia. I’m really looking forward to the bootcamp in the beginning of the program.” 

Over the next twelve weeks, the Scholars will engage on their data science research projects and attend the DSI cohort programming. The programming includes the Data Science@Work Series where members from the private sector and government organizations discuss data science in the workplace.  

Professor Kuan Liu, Dalla Lana School of Public Health and SUDS supervisor shares, “The DSI SUDS program provides unique hands-on research and learning experiences in data science that help students gain exposure to cutting-edge research topics spanning a wide range of disciplines and develop critical scientific communication, computation, and problem-solving skills. It was a great pleasure working with the SUDS scholar this past summer and I am excited to take part in the program again this year.” 

The program will conclude in August with the DSI Research Day, where the SUDS Scholar cohort will showcase their research.

Professor Laura Rosella, DSI Associate Director of Education and Training, shares that “As we welcome the new cohort of students, we are excited to see their passion and curiosity. Besides engaging in research projects, our SUDS Scholars benefit from acquiring data science expertise and professional growth opportunities. We are enthusiastic about the prospect of inspiring these students and, hopefully, launching their careers in data science. They are an outstanding group indeed.”  

The SUDS research opportunity is an excellent way for students to get involved in high-quality and enriching data science learning and to experience the application of data science methods and tools in various fields. It provides an opportunity for students to build their careers in data science. 

From mitigating weather disasters to mapping genetic diversity: U of T’s Schmidt AI in Science Postdoc announces first cohort

By Erin Warner

The University of Toronto’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures, is pleased to announce its first cohort of 10 fellows. U of T is one of nine universities around the world, and the only one in Canada, to be selected for this $148-million program to support the use of artificial intelligence (AI) in research.

From climate change to infectious disease, AI can help us solve the biggest challenges of our time by accelerating the pace of scientific research and development. U of T’s Eric and Wendy Schmidt AI in Science Postdocs program boosts the work of early-career scholars in engineering, mathematics and natural science by giving them vital tools in AI.

The fellowship includes networking and research collaborations between participating universities; a robust series of workshops, conferences and lectures; and training in how to apply AI techniques. To maximize accessibility and impact, fellows do not need prior experience with AI but will leave the program as AI-fluent scientists, ready to expand new research methodologies across a range of fields through their future work.

‘It is an exciting time to be part of the AI revolution that is fundamentally changing the way we do science’

“My warmest congratulations to the first cohort of Schmidt AI in Science post-doctoral fellows. It is an exciting time to be part of the AI revolution that is fundamentally changing the way we do science,” says Timothy Chan, U of T’s associate vice-president and vice-provost, strategic initiatives. “I wish you great success in your training and research at U of T.”

“U of T’s Schmidt AI in Science Postdoc program will equip fellows with AI tools and training that will transform and accelerate their research, ultimately helping catalyze novel solutions to many of the daunting challenges we face,” says Alán Aspuru-Guzik, director of U of T’s Acceleration Consortium and co-lead of the Schmidt AI in Science Postdoc program.

“Thank you to Schmidt Futures for developing a program that is interdisciplinary and accessible––an opportunity that will allow young scientists to take risks with new techniques to drive real innovation,” says Lisa Strug, director of U of T’s Data Sciences Institute and co-lead of the Schmidt AI in Science Postdoc program.

To review the past call for Schmidt AI in Science Postdocs and stay in the loop about future calls for proposals, please visit

U of T’s Eric and Wendy Schmidt AI in Science Postdocs

Meet the inaugural cohort of U of T’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship and learn what grand challenges they aim to solve using AI:

Daniel Gilman: the mysterious properties of dark matter

Research goal: To identify and understand the properties of dark matter, one of the most confounding mysteries in cosmology.

Soumita Ghosh: early detection of non-alcoholic fatty liver disease

Research goal: To discover consistent biomarkers, leading to non-invasive tests for large-scale screening, early detection and individually customized interventions for Non-Alcoholic Fatty Liver Disease, the most common chronic liver disease in Canada.

Md Abdul Halim: mitigating methane emissions for smart cities

Research goal: To quantify and monitor point-source methane emissions, which traps 25 times more heat than carbon dioxide, from urban landscapes and develop effective mitigation strategies for climate smart cities.

Jessica Leivesley: revolutionizing fish population management

Research goal: To revolutionize the monitoring and management of commercially important fish populations through non-invasive and non-lethal methods.

Tianyuan Lu: genetic disease prevention for underrepresented ancestries

Research goal: To improve the prevention of complex diseases by better understanding an individual’s genetic predispositions, especially for individuals of non-European ancestries who are vastly underrepresented in the data.

Soukayna Mouatadid: accurate forecasting for weather event management

Research goal: To improve the accuracy of sub-seasonal forecasting to better respond to weather events, including decisions related to water allocation, wildfire management, and drought and flood mitigation.

Gerard O’Leary: devices to treat neurological disorders

Research goal: To better understand mechanisms of neurological disorders and to accelerate the deployment of neuroelectronic medical devices to treat them, devices which have already shown great promise in reducing symptoms of brain-related disorders, such as tremors and seizures.

Felix Strieth-Kalthoff: sustainable molecules for medicine, agriculture and materials

Research goal: To make molecules sustainably and efficiently for different (chemical) industries, ranging from modern medicine and drug development to agrochemistry and performance materials.

Daoye Zhu: sustainable natural-urban ecosystems

Research goal: To improve our understanding of a wide range of biophysical, ecosystem and socio-economic changes in order to create sustainable natural-urban ecosystems.

Fatema Tuz Zohora: anti-cancer drug resistance

Research goal: To improve anti-cancer drug resistance in humans, which is responsible for up to 90 per cent of cancer related deaths, despite vast improvements to date.

DSI welcomes Holland Bloorview Kids Rehabilitation Hospital as a new partner 

The Data Sciences Institute (DSI) is excited to announce a new partnership with the Holland Bloorview Kids Rehabilitation Hospital, Canada’s largest children’s rehabilitation hospital. The Bloorview Research Institute, housed in the hospital, is dedicated to research that co-creates meaningful and healthy futures for children and youth with developmental conditions and disabilities and their families.  

The Institute brings together researchers and research trainees from the fields of medicine, psychology, occupational therapy and physical therapy, speech language pathology, engineering, computational sciences, sociology, urban planning  and more, to generate research  aimed at understanding developmental diversity — studying the brains, bodies and pathways of the lived experiences of children and youth with disabilities, co-creating and evaluating interventions that can promote health and wellbeing, removing barriers to meaningful  inclusion and participation, and  integrating research and teaching with frontline care. 

“Our research institute is reimagining and redesigning healthcare to enhance access, address inequities, and innovate more readily. To do that, we are leveraging data insights to identify areas for improvement, test new models of care and ultimately improve care for everyone. As a research leader in the field of data science, this collaboration with DSI will enable our teams to further their work, pursue new opportunities, and expand our partnership network,” said Dr. Evdokia Anagnostou, vice-president, research, Holland Bloorview. 

DSI collaborates with organizations eager to support world-class researchers, educators, and trainees advancing data sciences. We facilitate inclusive research connections, supporting foundational research in data science, as well as supporting the training of a diverse group of highly qualified personnel for their success in interdisciplinary environments.  

As one of the DSI external funding partners, Holland Bloorview researchers can apply for research grants, supports and training and lead initiatives at the DSI.  

“We are very excited to have Holland Bloorview researchers join the DSI community. Our goal is to create a hub to elevate data science research, training, and partnerships. By connecting and supporting data science researchers, the DSI advances research and nurtures the next generation of data- and computationally focused researchers.” says Lisa Strug, Director, Data Sciences Institute.