Computational Medicine aims to advance healthcare 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. We are using these patient models to discover novel risk biomarkers, predict disease progression, design optimal treatments, and identify new drug targets for applications such as cancer, cardiovascular disease, and neurological disorders.
Education in Computational Medicine
Our curriculum bridges biology with mathematics, engineering, and computational science. Students develop new solutions in personalized medicine by building computational models of the molecular biology, physiology, and anatomy of human health and disease.
Research in Computational Medicine
Our students and faculty are pioneering the development and application of patient-specific, quantitative models that can be used in the clinic to understand, diagnose, and treat disease. Key research areas include:
Computational Molecular Medicine
We are building a deep understanding of molecular networks by learning the likely and unlikely concentrations of biomolecules and how they vary in time to enable more informed clinical decisions.
Computational Physiological Medicine
We are developing models of disease that combine information across multiple levels of biological organization—from molecules and cells to tissues and organ systems—and applying these models to patient care.
Computational Anatomical Medicine
We are applying mathematical theory to model anatomic structures and their variations in health and disease. Examples include identifying brain shape and connectivity differences in neuropsychiatric disease and neurodevelopmental disorders, and classifying the heart shape and motion changes that characterize cardiac disease.
We are integrating biomedical signal processing, computational modeling, machine learning, and health informatics to develop new approaches in personalized medicine via electronic health records, physiological time series data, and genomics.