- About Us
- Research Projects
- Access to Primary Care for Older Adults
- Infectious Math
- Understanding Pandemic Related Moral Distress
- Equity-Based Pandemic Preparedness
- Optimizing Virtual Health
- Pandemics and Borders
- Social Media Use for Pandemic Preparedness and Response
- Women and Precarious Work
- Work conditions of Black workers in healthcare
- News and Events
- Resources
- Contact Us
Siying (Sydney) Ma
Siying Ma is a Ph.D. student in Statistics at ¶¡ÏãÔ°AV, specializing in mathematical and statistical modelling, machine learning, and disease analytics. Her research at PIPPS focuses on developing novel statistical models for disease analytics and population estimation, with an emphasis on enhancing public health surveillance and data-driven decision-making.
Siying completed her Master of Science in Statistics at the University of Victoria, where she developed disease analytic models based on ecological frameworks to estimate underreported COVID-19 cases. She also holds a Bachelor of Science in Statistics from East China Normal University, where her undergraduate research focused on eQTL analysis with auxiliary information.