Meet researcher, Dr. Cindy Feng
Dr. Cindy Feng is an Associate Professor of Biostatistics in Dalhousie University’s Department of Community Health and Epidemiology, where she has been a faculty member since 2020. Her career is distinguished by a commitment to advancing statistical methods that address complex public health challenges, with a particular focus on mental health, substance use, environmental epidemiology, and health services research. Dr. Feng’s work bridges methodological innovation and practical application, ensuring that new statistical models are not only theoretically sound but also directly responsive to real-world needs.
Educated at Simon Fraser University, where she earned her MSc and PhD in statistics, Dr. Feng was inspired by mentors who emphasized the importance of applying quantitative tools to tangible health problems. Her early career included a formative role as a statistician at the BC Centre for Excellence in HIV/AIDS, collaborating with leading researchers in HIV and substance use. These experiences deepened her understanding of how biostatistics can be integrated with clinical, epidemiological, and social science approaches to inform meaningful public health decisions.
At Dalhousie, Dr. Feng is recognized for her interdisciplinary collaborations, mentoring students, and developing statistical models that account for the complexities of health and environmental data. Her research is driven by a desire to generate insights that support equitable, data-informed decision-making, and she is dedicated to fostering partnerships that bridge statistical theory and public health practice.
Dr. Cindy Feng’s career exemplifies the power of biostatistics to drive public health innovation. Her work is characterized by a deep commitment to methodological rigor, interdisciplinary collaboration, and the pursuit of equity in health outcomes. Through her research, teaching, and mentorship, she continues to shape the field, ensuring that statistical methods remain responsive to the evolving challenges of public health.
Q: What inspired you to pursue a career in biostatistics and public health?
My journey into biostatistics was shaped by the intersection of rigorous statistical theory and the potential for tangible public health impact. During my graduate studies at Simon Fraser University, I was fortunate to have mentors who encouraged applying statistical methods to real-world health problems. Their guidance helped me see how quantitative tools could inform meaningful public health decisions. My work at the BC Centre for Excellence in HIV/AIDS further expanded my perspective, showing me how careful modeling of longitudinal and population-based data could identify social and behavioral drivers of substance use and support more targeted, equitable interventions. These experiences solidified my commitment to biostatistics as a field that bridges methodological rigor with public health impact—a motivation that continues to guide my research today.
Q: How do you balance methodological development and applied research in your projects?
I have always viewed methodological innovation and applied research as mutually reinforcing. Real-world public health questions often motivate new statistical models, especially when existing tools are insufficient to capture the complexity of the data. At the same time, methodological advances are most meaningful when they can be translated into practice. I strive to ensure that each new model I develop is closely grounded in an applied need, so that the work remains relevant and impactful.
Q: What are the key challenges in modeling complex health and environmental data?
Complex health and environmental data often violate the assumptions of traditional statistical models. Spatial correlation, heavy-tailed distributions, and heterogeneity across communities or patient groups require flexible modeling frameworks. Some of the main challenges include accurately capturing dependence structures, avoiding biased estimates due to unobserved confounders, and developing computationally efficient methods that can scale to large datasets. Overcoming these challenges is essential for producing reliable inferences and predictions in public health research.
Q: How do spatial-temporal models help reveal the health impacts of climate change and pollution?
Spatial-temporal models allow us to capture how environmental exposures and health outcomes vary across both space and time, which is essential for studying climate change and pollution. Climate-related exposures, such as extreme heat or wildfire smoke, do not occur uniformly; they cluster geographically, evolve over time, and often have delayed or cumulative health effects. Accounting for spatial and temporal correlation is critical because health and environmental data are rarely independent. Properly modeling spatial-temporal dependence improves inference, yields more reliable uncertainty quantification, and ensures that observed associations reflect true environmental signals rather than artifacts of clustered data. This is particularly important when results are used to inform public health interventions and environmental policy.
Q: What have you learned from interdisciplinary collaboration, and how do you build effective partnerships?
Interdisciplinary collaboration has taught me the value of listening carefully and building shared language across fields. Each discipline brings its own perspectives, priorities, and ways of framing problems. My role as a biostatistician is often to build bridges, translating research questions into statistical models, and translating model results back into insights that advance understanding and practice. Strong partnerships begin with humility and curiosity. I invest time in understanding the public health context, the data-generating process, and the decisions stakeholders need to make. This helps ensure that statistical methods address the right questions and that results are communicated clearly. I also prioritize long-term collaborations where methodological development and applied impact grow together.
Q: What are your hopes for the future of biostatistics in public health?
I hope to see biostatistics continue to play an essential role in addressing emerging public health challenges. As new data sources and technologies become available, there is tremendous opportunity for innovative statistical approaches to support more timely, accurate, and equitable decision-making. My hope is that biostatistical research will continue to strengthen collaboration across disciplines and help translate complex data into meaningful insights that improve population health.