Computational Medicine

Computational Medicine aims to improve health care by developing computational models of disease, personalizing these models using data from patients, and applying these models to improve the diagnosis and treatment of disease. Patient models are being used to predict and discover novel sensitive and specific risk biomarkers, predict disease progression, design optimal treatments, and discover novel drug targets. Applications include cardiovascular and neurological diseases and cancer.

Computational Molecular Medicine

A deep understanding of molecular networks requires learning the likely and unlikely concentrations of biomolecules and how they vary in time—not just individually, but collectively as a multivariate probability distribution. Achieving this goal will enable more informed clinical decisions.

Computational Physiological Medicine

Understanding disease and its treatment requires models spanning multiple levels of biological organization, integrating from molecules and networks to cells, tissues, organs, and organ systems. Computational Physiological Medicine is developing models of disease that combine information across these levels to be applied to patient care.

Computational Anatomical Medicine

Computational Anatomy is a mathematical theory of how to model anatomic structure and its variations in health and disease. Applications include identifying differences of brain shape and connectivity in neuropsychiatric disease and neurodevelopmental disorders, and classifying shape and motion changes of the heart that characterize heart disease.

Computational Healthcare

The field of Computational Healthcare exists at the interface of biomedical signal processing, computational modeling, machine learning, and health informatics. It enables the practice of personalized medicine via electronic health records, physiological time series, and genomics.

Core Faculty

Research Faculty