In the last decade, there has been major advances in the production and collection of data, from medical research to patient wellness regimes. These vast new troves of data from electronic health records (EHR), genetic databases, connected devices and wearables offer a unique opportunity to make health care more predictive and preventive. Transforming data into knowledge requires deep understanding the features of data, integrating disparate data sources, and having strong analytical strategies.
We aim to tackle several key challenges in the modern data rich era, including heterogeneity, complexity, suboptimal quality, reproducibility, and high-dimensionality of biomedical data. We believe that fundamental principles and wisdoms of statistics can be revived in tackling these problems, such as likelihood principle, Bayesian analysis, robust inference, dimension reduction, penalized methods, as well as a combination of statistical modeling and algorithm design.