Data Sciences Institute

How can we advance data science integration in policy settings and build programming and training to enable new capacity in advancing data science in the public service?   

To address this challenge, the Data Sciences Institute (DSI) and the Dalla Lana School of Public Health (DLPSH) are launching the Policy Lab, to advance the integration of data sciences in the design and development of public policies, creating a healthier and more just society. 

The Policy Lab will engage in strategic partnerships with ministries, agencies, and various policy-oriented groups to strategize on the most effective ways to build capacity and demand across the public sector for data sciences insights. By collaborating with these groups, the Policy Lab intends to cultivate a vibrant community of data scientists and data science users, leading to increased utilization of data sciences across diverse policy domains. 

One of the key features of the Policy Lab is its hosting of visiting Researchers-in-Residence from the public sector, who will focus on building and advancing data science within the health system. The goal is to advance data science integration in policy settings and build programming and training to enable new capacity in advancing data science in the public service that effectively meets the needs and realities of working with data in this type of setting.

“By collaborating with the Data Sciences Institute and the Dalla Lana School of Public Health, we have a unique opportunity to leverage data-driven insights in designing and implementing evidence-based policies that positively impact the health and well-being of Ontarians,” says Dr. Michael Hillmer, Assistant Deputy Minister of Digital and Analytics Strategy, Ontario Ministry of Health/Ministry of Long-Term Care. 

The initial focus of the Policy Lab will be on public health and health systems, with insights generated from this work serving as a foundation for future projects in data sciences and public policy across various sectors. To foster collaboration and knowledge exchange, the Policy Lab will define compelling data science use cases motivated by real examples from the public sector and engage policymakers and stakeholders from diverse backgrounds to advance critical dialogues on data science for policy. 

Laura Rosella, Associate Director of Education & Training, DSI and Associate Professor, DLSPH, expressed her enthusiasm for the launch of the Policy Lab, stating: “Through the Policy Lab, we have an unprecedented opportunity to shape the future of public policy and transform the way we approach complex societal challenges. We are excited to work with our partners to advance data science integration and empower the public service with the necessary tools and training to use data to support decision-making that improves population health.”

The launch of the Policy Lab marks an important milestone in the convergence of data sciences and public policy. As data-driven decision-making becomes increasingly crucial, the Policy Lab paves the way for transformative policy interventions that prioritize health and equity. 

The Data Sciences Institute (DSI) is pleased to announce its 2023 Doctoral Student Fellowship recipients.  

The DSI Doctoral Student Fellowship supports multi-disciplinary training and collaborative research in data sciences that include faculty from the University of Toronto and external funding partners. Fellows will engage in exciting research projects with a data sciences focus, developing novel methodologies or applying existing approaches innovatively. Each fellow has at least two co-supervisors from complementary disciplinary backgrounds to guide the multidisciplinary aspects of their research project. In addition to their research, Fellows engage in DSI professional development and data skills programming and networking.  

Laura Rosella, DSI Associate Director of Education and Training, shares that “We are delighted to announce the selection of our 9 new fellows for the DSI Doctoral Student Fellowship. These exceptional scholars will be conducting cutting-edge research in data sciences, addressing pressing societal questions and driving positive social change. We look forward to witnessing the impact of their work as they contribute to the DSI community.” 

Each Fellow is tackling diverse problems in a broad range of disciplines. 

Using Architectural Geometric Data for Sustainable and Equitable Built Environment 

Zihan Ling along with her supervisors Professors Alec Jacobson (Computer Science, Faculty of Arts & Science, and Maria Yablonina (John H. Daniels Faculty of Architecture, Landscape, and Design) is making her mark by delving into Architectural Geometric Data. 

Ling’s research is all about using advanced computer techniques called deep learning to solve tricky design problems in architectural geometry. She is particularly interested in finding the best possible shapes for different aspects of design, like the materials used and the energy costs involved. Ling explains, “We hope deep learning techniques combined with novel 3D representations such as neural field will allow us to uncover the unexplored space of architectural geometry.” 

The overarching goal of Ling’s research is to find the best balance between cost and energy efficiency for important parts of buildings like walls, beams and ceilings. “As these substructural elements made up the fabric of our built environment, the ability to optimize for its energy efficiency and material cost will benefit society by reducing construction and energy waste,” says Ling. 

“I believe the DSI Doctoral Student Fellowship will help me to focus on this research project and connect with people who care about our research goals, ” says Ling. “We will also benefit from the community it builds by observing how others leverage data-centric approach for interdisciplinary problems.” 

The Landscape of COVID-19 in Toronto 

Afia Amoako is collaborating closely with Professors David Fisman and and Arjumand Siddiqi, Dalla Lana School of Public Health on her research topic focused on the unequal landscape of COVID-19 in Toronto. 

Describing her research, Amoako explains, “My research incorporates spatial epidemiological methods and mathematical modeling to gain a deeper understanding of the COVID-19 experience in Toronto at a granular scale. These methods enable me to map COVID-19 in a more detailed manner and examine the reasons behind its varied impact across the city. By utilizing various data sources, including case rates, hospitalization rates, vaccination and testing rates, as well as sociodemographic characteristics of Toronto residents, I strive to achieve a comprehensive understanding of the diverse experiences of the COVID-19 pandemic to better understand health inequities.” 

“I am looking forward to the seminars and research days to receive input from the doctoral fellows and faculty that can further enrich my knowledge of data science and enhance my overall research,” says Amoako. I began my PhD during the peak of the lockdown, making these collaborative opportunities even more significant for me.” says Amoako.

Congratulations to all the DSI Doctoral Student Fellows. Learn more about each of them below: 

Afia Amoako – The Unequal Landscape of COVID-19 in Toronto 

Supervisors: David Fisman and Arjumand Siddiqi, University of Toronto, Dalla Lana School of Public Health 

Michael Geuenich – Novel data science methods to understand loss of antigen presentation in pancreatic cancer at single-cell resolution 

Supervisors: Kieran Campbell, Lunenfeld-Tanenbaum Research Institute; Pamela Ohashi, University Health Network, Princess Margaret Cancer Centre 
 
Tara Henechowicz – Applying and comparing polygenic and polytranscriptomic risk score methods to examine the relationship between music training and the motor system 

Supervisors: Michael Thaut, University of Toronto, Faculty of Music; Daphne Tan, University of Toronto, Faculty of Music 
 
Sangwook Kim – Multi-Task Learning for Developing a Robust AI-based Radiation Treatment Planning 

Supervisors: Chris McIntosh, University Health Network, Toronto General Hospital Research Institute; Tom Purdie, University Health Network, Techna Institute 
 
Christie Lau – Longitudinal tracking of cancer drug-tolerant persister populations at single-cell resolution 

Supervisors: Gregory Schwartz, University Health Network, Princess Margaret Cancer Centre; Geoffrey Liu, University Health Network, Princess Margaret Cancer Centre 
 
Wai Hin Henry Leung – Deep Learning for Galactic Astronomy 

Supervisors: Jo Bovy, University of Toronto, Faculty of Arts & Science, David A. Dunlap Department of Astronomy and Astrophysics; Joshua Speagle, University of Toronto, Faculty of Arts & Science, Department of Statistical Sciences 

David Dayi Li – Advanced Spatial Point Process Modeling for Ultra-Diffuse Galaxy Detection 

Supervisors: Gwendolyn Eadie, University of Toronto, Faculty of Arts & Science, David A. Dunlap Department of Astronomy and Astrophysics; Patrick Brown, Unity Health Toronto; Roberto Abraham, University of Toronto, Faculty of Arts & Science, David A. Dunlap Department of Astronomy and Astrophysics 
 
Zihan Ling – Using Architectural Geometric Data for Sustainable and Equitable Built Environment 

Supervisors: Alec Jacobson, University of Toronto, Faculty of Arts & Science, Department of Computer Science; Maria Yablonina, University of Toronto, John H. Daniels Faculty of Architecture, Landscape, and Design 
 
Rongqian Zhang – Mitigating inter-scanner biases in high-dimensional neuroimaging data via spatial Gaussian process 

Supervisors: Jun Young, University of Toronto, Faculty of Arts & Science, Department of Statistical Sciences; Elena Tuzhilina, University of Toronto, Faculty of Arts & Science, Department of Statistical Sciences 

Hotter days in Toronto mean more people flocking to parks for relief, but just how are these green spaces being utilized during extreme heat? University of Toronto researchers, who were awarded the Data Access Grant by the Data Sciences Institute, are analyzing patterns of human activity, park usage and air temperatures to shed light on the impact of extreme heat and climate patterns on the health and well-being of Toronto residents. 

The Data Sciences Institute (DSI) is excited to announce a new partnership with the Ontario Institute for Cancer Research (OICR), a collaborative research institute that conducts and enables high-impact translational cancer research.  

OICR conducts cross-disciplinary cancer research in areas such as genomics, immuno-oncology, informatics, computational biology, genome informatics, implementation science, drug discovery, and molecular pathology while facilitating global research collaboration, securely sharing data, and providing powerful, world-class tools and resources to the research community. 

“Our collaborative approach, both locally and globally, ensures that Ontario remains at the forefront of cancer research and care. With a shared commitment to maximizing the health and economic benefit of our research for the people of Ontario, this partnership with DSI holds tremendous potential to drive breakthroughs in cancer research that can bring real benefits to those affected by cancer,“ said Dr. Laszlo Radvanyi, President and Scientific Director, OICR. 

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, OICR researchers with an appointment at the University of Toronto can apply for research grants, supports and training and lead initiatives at the DSI.  

“We are very excited to have the Ontario Institute for Cancer Research join our growing 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. 

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.