Work in the Maunank Shah Lab focuses on infectious disease modeling and health economics, and seeks to investigate new strategies for diagnosing HIV and tuberculosis (TB) in areas with limited resources, both domestically and abroad. Our primary focus is TB diagnostics, with studies examining the diagnostic test accuracy, cost-effectiveness and programmatic impact of emerging diagnostics. We have developed mobile health initiatives to incorporate video-based therapy for TB treatment, and we have a longstanding interest in interventions that help to reduce or prevent HIV transmission.

The key research interests in the Robert Bollinger Lab include identifying biological and behavioral risk factors for HIV transmission as well as characterizing the clinical progression and treatment of HIV and related infectious diseases. We also have a long-standing interest in optimizing health care capacity and delivery in settings with limited resources. Our work includes implementing science research projects to explore the effectiveness of initiatives such as task-shifting, clinical education, distance learning and mobile health programs as a way to improve health care in these locations.

The Alain Labrique Lab conducts research on infectious diseases and public health. Our team studies the various factors that lead to maternal and neonatal mortality, particularly in underserved populations in South Asia, using the tools of infectious disease epidemiology, molecular biology and biostatistics. We work to better understand factors such as the interface of micronutrient deficiency and maternal/infant mortality and the prevention of nosocomial infections through mechanistic or nutritional interventions. We also have a longstanding interest in technologies that may enable early detection of disease.

The Suchi Saria Lab, part of the Institute for Computational Medicine, explores topics within the fields of machine learning and computational statistics, with a focus on computational solutions for problems in health informatics. Our team investigates the applications of machine learning and computational statistics to domains where one has to draw inferences from observing a complex, real-world system evolve over time. We use Bayesian and probabilistic graphical modeling approaches to address the challenges that emerge with modeling and prediction in real-world temporal systems.

Dr. Martin's research is focused on rapid generation of new knowledge through clinical studies that can be brought back to the bedside to directly inform the care of patients with advanced lipid disorders and those in need of state-of-the-art comprehensive CV prevention. Members of his lab commit to complete ownership of their project, unwavering pursuit of excellence, and thrive on multidisciplinary teamwork. Active projects include the Very Large Database of Lipids, CASCADE FH Registry, MiCORE (Myocardial infarction, COmbined device, Recovery Enhancement), Google Health Search Trial, and mActive-Smoke. For more information, please visit the Ciccarone Center.

Work in the Kristin Riekert Lab focuses on methods for improving health care quality and delivery, particularly among underserved and disadvantaged populations. Our research covers a range of important topics, including health beliefs, treatment adherence, doctor-patient communication, self-management interventions, mobile health initiatives, health disparities and patient-reported outcome methodology. We also work with the National Institutes of Health on multiple intervention trials focused on improving adherence and health outcomes in asthma, chronic kidney disease, cystic fibrosis (CF), sickle cell disease and secondhand smoke reduction.