Computational Biomedicine

Papin emphasizes data science in every class he teaches. In the lab, Papin and his team use both laboratory experiments and computer models to predict how cells will change and react in various conditions.

Our collaborative, translational model leads to exceptional student outcomes

Trainees gain depth and breadth through immersion in UVA’s multidisciplinary imaging research ecosystem.

Just a handful of universities have multiple members on the 69-person board of the journal Cell Systems, including Harvard, MIT, Princeton, Stanford, UCSF and UVA.

Data science is defining the field of biomedical engineering

At the University of Virginia, we are training the next generation of data-driven biomedical engineers.

UVA chemical engineer, Matt Lazzara, convenes multidisciplinary, multi-institutional team to design combination therapy for pancreatic cancer

Matthew J. Lazzara, an associate professor of chemical engineering and researchers in his lab are devising a better way to pancreatic cancer.

The curricula of the future will emphasizing cell and molecular biology, linear algebra, statistics, systems modeling, and machine learning. By positioning the future biomedical engineering leaders to harness data science and machine learning, we can slow down antibiotic resistance.

For BME departments, it's a call to action

No other academic discipline is as well positioned to integrate biomedical data science into its research programs and student training programs.

Bioinformatics Applied to Gene Expression

With the help of three new grants, Mete Civelek is employing the tools of biomedical data science to connect the dots between genes and cardiometabolic disease.

The Tenure Track Gender Gap

In this Q&A, Blemker shares her perspectives on why women students may be discouraged from considering faculty careers and what she is doing to change the conversation.

New Modeling Tool Has Implications for Better Understanding of Disease

In an open-source research paper, a UVA Engineering professor and her former Ph.D. student share a new, more accurate method for modeling evolutionary change.