Welcome to Song Lab's website. Our lab pursues the development and application of theories and methodologies from data science to solve scientific problems arising from the domains of medicine and public health.
Data science has always played an important role in the medical and public health sciences. This multi-disciplinary scientific branch has provided analytic tools and software for data collection, modeling and data analysis, all of which have helped advance biomedical research and knowledge to improve human quality of life. Complex big data collected from environmental health and nutritional sciences have presented great challenges for further growth and advancement of statistical methods and theories tailored to the needs of the medical and public health sectors.
Lab members are strongly interested in interdisciplinary research in the areas of statistics, operations research, and machine learning, with our core interest in the statistical foundation of big data analytics. Applications of our work involve processing and analyzing big data from various applied sciences, including nutritional sciences, environmental health sciences, asthma, and nephrology.
The Song Lab currently has one postdoctoral research fellow and eight doctoral students.
This page was last modified on: 31/10/2018
Questions or comments about this website? Contact the maintainer (Mathieu Bray).