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Data Sciences Institute

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 schmidtfellows.utoronto.ca.

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 

by Sara Elhawash

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. 

Health Research Made Easy with User-Friendly Rank-Heat Plot Web Interface

by Sara Elhawash

Health researchers often face challenges in data interpretation, especially when using network meta-analysis (NMA), which compares multiple treatments by combining various types of evidence from randomized trials. This complexity arises due to the numerous outcomes and interventions involved. To address this issue, the Data Sciences Institute (DSI)’s research software development support team collaborated with Dr. Areti-Angeliki Veroniki, a scientist at the Li Ka Shing Knowledge Institute at St. Michael’s Hospital, a site of Unity Health Toronto, to create a user-friendly web interface, the Rank-Heat Plot R Shiny tool. This tool allows health researchers to upload spreadsheets containing results of various medical treatments and compare outcomes through an easy-to-understand visualization tool. 

DSI’s senior software developer Conor Klamann explains that the Rank-Heat Plot tool uses the “R Shiny framework to provide a user-friendly web interface, enabling users unfamiliar with R to analyze data and download results easily.”  

“Working with the Data Sciences Institute has been transformative for our project. Their support enabled us to create the interface for the Rank-Heat Plot R Shiny tool, which has significantly simplified the way health researchers interpret complex network meta-analysis results. This user-friendly tool empowers researchers to make informed decisions and advance their understanding of various medical treatments, ultimately contributing to better patient care and outcomes,” says Dr. Veroniki. 

DSI’s software development program offers faculty and scientists access to skilled developers who refine existing software, develop new tools and disseminate research software. “The Rank-Heat Plot project is hosted on a server provided free of charge by the Digital Research Alliance of Canada, making it a cost-effective option for researchers publishing small or moderately sized tools,” shares Conor. 

Using the Rank-Heat Plot Tool 

Users can upload data from multiple studies in a single excel file, select model specifications, and run the analysis. The tool then generates a rank heat plot, which can be customized and downloaded in high-quality PNG format. In protection of user privacy, no data is collected during this process. 

Dr. Veroniki emphasizes the tool’s ability to quickly identify the most effective and safest interventions for various outcomes, as well as highlighting interventions that haven’t been studied for specific outcomes. She says, “The tool allows the conduction of multiple analyses and presentation of results in a very short timeframe, which can also be useful for users with limited or absence of knowledge in coding with R. The rank-heat plot can also be used for any discipline or disease, without any restrictions.” 

Impact on Clinicians, Guideline Developers and Policymakers 

Clinicians, guideline developers and policy makers can use the RankHeat Plot to make informed decisions about drug coverage, inform recommendations and discuss optimal agents across different outcomes with patients. The RankHeat Plot is expected to greatly benefit health researchers and improve their decision-making process. 

Working alongside Dr. Veroniki is Professor Andrea Tricco from the Dalla Lana School of Public Health and a Scientist at St. Michael’s Hospital, a site of Unity Health Toronto, she emphasizes, “The rank heat plot allows all decision-makers to quickly identify which interventions are the safest and most effective across a range of outcomes. It is an essential component of our research and allows our results to be easily transferred to decision-makers.” 

Dr. Veroniki and her team will be working with DSI on the upcoming version of the tool, stating, “We plan on developing the option to perform a Bayesian approach to be included and the ranking statistic results will be based on pre-specified clinically important effects.” She further explains, “This will facilitate interpretation of NMA results based on the smallest change in each outcome assessed, which is considered worthwhile and important by a patient and would mandate a change in the patient’s management.”  

The Rank-Heat Plot has already been used in multiple fields, including falls prevention in older adults, dementia, cardiovascular risk reduction, COVID-19 vaccines, pediatrics, oncology and more. “The R Shiny tool’s accuracy, reliability, and user-friendly interface make it an invaluable asset to health researchers, improving their decision-making process and the quality of care they provide,” says Dr. Veroniki. 

Celebrating the first Graduates of the Data Sciences Institute’s Professional Data Science Certificate Program

by Sara Elhawash

In an ever-evolving data driven world, data science has become a cornerstone of innovation, decision-making and problem solving across industries. As we increasingly rely on data to steer our businesses, governments and societies, skills in data science are in high demand. 

The Data Sciences Institute (DSI) at the University of Toronto is addressing this demand by offering training in Data Science through the Data Science Certificate program. This spring, DSI celebrates the graduation of the first cohort of students that are completing the certificate. 

First-year graduates will soon receive their certificates, ready to apply their job-ready skills. Yongran Yan, Research Technician, University Health Network, shares her transformative experiences with the program and her excitement for the future. “Without prior knowledge about data science, the knowledge and skills that I’ve learnt from this program are invaluable. In addition to learning knowledge from our instructors, the guest speakers coming from different fields in the industry have provided me with insights on the potential applications of data science. As a cancer researcher, I am also excited to see how my data science skills will help me explore more aspects of my research topic.”

Professor Rohan Alexander, Faculty of Information and Department of Statistical Sciences, Faculty of Arts & Science, serves as the academic lead of the certificate. Reflecting on its growth, he says, “The DSI Data Science Certificate is a truly exceptional program that combines core courses to establish a solid foundation in data science. Designed for individuals with no prior expertise in data science, this program empowers students to thrive in data-driven fields. As we celebrate the program and our upcoming graduates, we are confident that they will be fully prepared to apply their newly acquired skills and leverage their professional networks to make a significant impact in the industry.”

The certificate provides flexibility, allowing learners to choose a single course to improve their skills in a specific area or earn a full certificate by taking six of the eight courses available. The curriculum ensures that learners master core competencies in foundational data science, including SQL, R, and Python, while gaining hands-on experience through real-world case studies. 

In addition, the certificate presents the opportunity to learn from private-sector experts during case studies. This year, we had various experts, including Ajit Desai, Principal Data Scientist at the Bank of Canada; Richard Wintle, Assistant Director at The Centre for Applied Genomics, SickKids Hospital; Zia Babar, Director, Cloud Engineering at PwC Canada. The case study component offers learners valuable insights into the professional world of data science analytics. 

Looking ahead, in response to the interest from past participants, DSI will be offering three programming courses in May to July: Introduction to Unix Shell, Git, and GitHub, Introduction to R, and Introduction to Python. All courses require no data science experience, making them accessible to a wide range of students.  

What previous participants had to say

Here’s what some had to say: 

“I highly recommended this course to beginner and intermediate users. The instructor starts from the beginning through the complex data interpretation process,” says one participant.

“I really liked the live coding format and being able to follow along with the instructor, which I think is the best way to learn coding. I really appreciated how well organized and well presented the material was and how supportive the instructor and TA were of students, always taking the time to stay and answer questions after every class,” says another participant.

Polygenic Risk Score Grant Winners Announced: Advancing Genomic Medicine Through Innovative Research

by Sara Elhawash

The Data Sciences Institute (DSI) is pleased to announce the recipients of the DSI-McLaughlin Centre Polygenic Risk Score Grant competition. This grant, created in partnership with the University of Toronto’s McLaughlin Centre and the Dalla Lana School of Public Health, aims to support emerging research and build capacity in the field of polygenic risk score studies. Polygenic risk scores enable researchers to use multiple genetic factors to estimate an individual’s genetic risk for complex diseases, providing important information for predicting, preventing and treating diseases. 

Professor France Gagnon, Chair of the adjudication committee and Associate Dean Research at the Dalla Lana School of Public Health, expressed enthusiasm for the wide range of proposals received from researchers across the University and partner institutions. These proposals demonstrate the potential for innovative methodologies in polygenic risk scores to impact a wide range of fields. “We are thrilled to support this cutting-edge research and look forward to seeing its impact on the field of precision population health and medicine,” said Gagnon. 

Two of the grant recipients are Professors Frank Wendt and Esteban Parra, Department of Anthropology at the University of Toronto Mississauga. They are taking a new approach to the study of major depressive disorder (MDD) and hippocampus volume. Their research aims to improve the accuracy of polygenic risk score predictions for this disorder and expand our understanding of its biology. Wendt and Parra said, “By taking a tandem repeat aware approach to risk scores, we hope to uncover new insights into the biology of major depressive disorder, improve prediction accuracy, and develop scores that better translate across population groups. We are thrilled to contribute to this important area of research that takes an interdisciplinary approach to pressing matters in genomic medicine.” 

Grant recipients Lei Sun and Ziang Zhang from the Department of Statistical Sciences, Faculty of Arts & Science, are collaborating with Dr. Andrew Paterson from The Hospital for Sick Children on a project to develop polygenic risk scores for binary traits, which are traits that can only take on two possible outcomes, such as the presence or absence of a particular disease. Their research aims to investigate how the estimated effects of different genetic factors can be biased and propose a new way to adjust for this bias to improve the accuracy of the polygenic risk scores. “Because of DSI’s emphasis on interdisciplinary research, all team members with complementary expertise worked closely to define and develop a research project with statistical rigor and practical impact. This grant also provides graduate students in Statistical Sciences a unique opportunity to lead a grant application, which is rare in our discipline,” said Sun. These projects have the potential to improve our understanding of complex diseases and advance the fields of precision medicine and population health. 

Congratulations to all the DSI – McLaughlin Centre Polygenic Risk Score Grant collaborative research teams! 

A Multimodal AI Solution for Improved Outcome Prediction using Polygenic Scores and EHR  

  • Zahra Shakeri (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, U of T); Kuan Liu (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, U of T) 

Addressing non-collapsibility in logistic regression when constructing polygenic risk scores for binary traits 

  • Lei Sun (Department of Statistical Sciences, Faculty of Arts & Science, U of T), Andrew Paterson (Genetics and Genome Biology, The Hospital for Sick Children), and Ziang Zhang (Department of Statistical Sciences, Faculty of Arts & Science, U of T) 

Inclusive Trans-ancestry Polygenic Genetic Risk Scores (iPRS) via Robust Transfer Learning 

  • Jessica Gronsbell (Department of Statistical Sciences, Faculty of Arts & Science, U of T); Jianhui Gao (Department of Statistical Sciences, Faculty of Arts & Science, U of T)

Tandem repeat aware risk scores linking major depression and hippocampus volume 

  • Frank Wendt (Department of Anthropology, University of Toronto Mississauga); Esteban Parra (Department of Anthropology, University of Toronto Mississauga)