DLSPH Graduate Awards in Data Science for Public Health and Health Systems
Meet the 2023-2024 Recipients
Beiwen Wu
Project Title: Comprehensive investigation of lipidomics in the risk of aerodigestive tract (ADT) cancers by evidence triangulation
Supervisor: Dr. Rayjean J. Hung
Beiwen Wu is a second year PhD student in Epidemiology at Dalla Lana School of Public Health and a current trainee of the CANSSI Ontario STAGE Program. She graduated from Johns Hopkins Bloomberg School of Public Health in Human Nutrition. Beiwen is interested in investigating the associations between comprehensive lipidome profiles and aerodigestive tract cancer risk using individual-level data from UK Biobank as well as summary-level statistics from publicly available genome-wide association studies and major cancer consortia. She proposes to understand these complex associations by using a multi-design approach to triangulate the evidence, including conventional epidemiologic methods, Mendelian Randomization analysis, and machine learning techniques. From her study, Beiwen hopes to identify and validate novel biomarkers that may have potential applications in the field of clinical-based biomedical genetics.
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: Dr. Jason D. Pole
Giancarlo is a PhD Candidate in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto. His research interests are centered around using administrative data to investigate the social and health outcomes of cancer in children, adolescents, and young adults. His PhD thesis examines the impact of cancer on the employment participation and income of adolescents and young adults. In addition to this, Giancarlo is also actively involved in applying machine learning and data science techniques to identify and predict acute kidney injury in young adults diagnosed with cancer and hospitalized with COVID-19.
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: Dr. Laura Rosella
Lief Pagalan is Ph.D. Candidate in Epidemiology at the University of Toronto’s Dalla Lana School of Public Health. They are a CIHR Frederick Banting & Charles Best Canada Doctoral Scholar and a Graduate Affiliate with the Schwartz Reisman Institute for Technology and Society. Their research focuses on population health and how to leverage new data infrastructures and technologies to predict and prevent health risks, develop population health management solutions, and deliver public health services more efficiently and equitably. As an epidemiologist, their expertise is in environmental health and the built and social environments. They also have a methodological interest in risk prediction, simulation modelling, and integrating causal inference and machine learning methods.
Jasper Zhongyuan Zhang
Project Title: Machine learning algorithm development for complex missing structure imputation on multi-omics data
Supervisor: Dr. Wei Xu
Jasper Zhang is a Ph.D. student in Biostatistics, supervised by Dr. Wei Xu at the Dalla Lana School of Public Health, University of Toronto, and University Health Network (UHN). His research focuses on Cancer Genomics, Survival Analysis, Deep Learning, and Health Economics, with an emphasis on AI-driven data integration algorithms for the analysis of cancer omics data. Jasper’s academic background includes a Master of Science in Biostatistics from the University of Toronto and a Bachelor of Mathematics in Computer Science and Statistics from the University of Waterloo. In his academic community, Jasper has served as the Biostatistics section editor for the University of Toronto Journal of Public Health (UTJPH) since 2022 and as the co-president of the Biostatistics Union of Graduate Students for the 2022-2023 academic year. He is also a student member of the Health Data Working Group at the Dalla Lana School of Public Health.