A key focus of our research is the application of novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. Our research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.


We pursue research in the application of data science to healthcare across the from individual patient trajectories, to entire populations, to health systems. We work on developing cutting-edge methodologies to derive insights from multi-modal digital data sources. OUr team includes extertise in data mining, machine learning, deep learning, biostatistics, economics, and medicine. Recent examples of our work are displayed below.

Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment.


Health Policy Informatics

Healthcare reform has led to US initiatives designed to improve the quality, efficiency, safety and effectiveness of healthcare care delivery. A major focus of our group is to generate Insight into the quality of healthcare delivery, using a broad range of clinical data, including electronic medical records (EMR), administrative data and healthcare registries. Recent examples of our work are displayed below

Payment Reform

Quality Measurement

Comparative Effectiveness

Patient-Centered Outcomes Research

As health systems move to value-based care, patient centeredness has become an important attribute of care. Patient-centered outcomes assess the net effects of disease treatment, such as general health status and disease-related quality of life, rather than physiological endpoints, such as laboratory values and disease-specific survival. These outcomes are not systematically capture in a format that enables efficient evaluation at a patient-level. Our group employs innovative designs and methods to advance patient-centered outcomes research at a population-level. 

Population Health

Population health has been defined as "the healthoutcomes of a group of individuals, including the distribution of such outcomes within the group". It is an approach  that aims to improve the health of an entire human population. This is a well-studied topic in our lab. We have performed a series of population-level studies to assess patterns of care, variations of patient outcomes, and healthcare utilization and accessibility. We aim to study not only the general population, but also important under-represented populations that are often excluded from controlled clinical trials, including the effects of system-level policies on the population, such as the Affordable Care Act (ACA) and USPTF guideline changes.