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