DLSPH Graduate Awards in Data Science for Public Health and Health Systems
The Interdisciplinary Research Cluster on Data Science for Population Health and Health Systems is one of the five priority research areas at the Dalla Lana School of Public Health (DLSPH), which responds to the importance of advancing data sciences, artificial intelligence and emerging technologies in informatics and analytics to improve population health and health systems performance. The Data Science cluster has the following key areas of focus: fosters important conversations and critical thinking, supports novel interdisciplinary research, and advances education and training.
The DLSPH Data Science Interdisciplinary Research Cluster offered $10,000 stipends to support DLSPH graduate trainees conducting novel research and/or developing new methods in data science and artificial intelligence to improve population health and health system performance.
If you are interested in applying, click here to learn more about the award.
Beiwen Wu
Project Title: Comprehensive investigation of lipidomics in the risk of aerodigestive tract (ADT) cancers by evidence triangulation
Supervisor(s): Dr. Rayjean J. Hung
Giancarlo DiGiuseppe
Project Title: Using machine learning methods to predict acute kidney injury in adolescents and young adults affected by cancer and hospitalized with COVID-19
Supervisor(s): Dr. Jason D. Pole
Lief Pagalan
Project Title: Using Machine Learning and Environmental Data to Predict and Prevent Premature Mortality in Canadian Cities: Developing Decision-Support Tools to Inform Health System Planning
Supervisor(s): Dr. Laura Rosella
Jasper Zhongyuan Zhang
Project Title: Machine learning algorithm development for complex missing structure imputation on multi-omics data
Supervisor(s): Dr. Wei Xu
Joseph Donia
Project Title: Critical questions for data-intensive health innovation: A design ethics case study
Supervisor(s): Dr. Jay Shaw & Dr. Jennifer Gibson
Jo-Ann Osei-Twum
Project Title: Comparing data science approaches to population health management and the associated health equity implications
Supervisor(s): Dr. Laura Rosella
Rose Garrett
Project Title: The role of sensitivity analysis in handling untestable assumptions in analyses of electronic health record data: an example in pediatric healthcare
Supervisor(s): Dr. Eleanor Pullenayegum (in collaboration with Dr. Brian Feldman)
Vinyas Harish
Project Title: Assessing the stability of machine learning models to dataset shift during the COVID-19 pandemic
Supervisor(s): Dr. Laura Rosella & Dr. Kamran Khan