Rapid biotechnological advances have turned the biomedical sciences into a data science. Today, large-scale high-dimensional data is generated routinely by new imaging modalities, DNA sequencing technologies, and many other molecular profiling techniques. These profiles promise to reveal the molecular basis of diseases and to guide the design of novel therapeutic interventions. In addition to molecular and clinical data, mobile health data obtained from internet-based pervasive monitoring can also provide useful information. However, integrating and analyzing complex clinical, molecular, and mobile health data is extremely challenging, and new statistical models and computational inference methods are needed. Our workshop will (i) explore recent advances and open problems in statistical modelling, inference, and integration of molecular profiling, electronic health record, and mobile health data; (ii) identify opportunities and challenges for translation of data science approaches to health and disease, such as the construction of data-driven medical decision support systems; and (iii) facilitate meaningful interactions between engineering, biomedical, and quantitative researchers.