Duke researchers are using advances in computing to expand our knowledge of biology and human health and build artificial intelligence systems people can understand and trust.
"AI has been a massive multiplier in terms of answering existing questions, but the path which is super interesting to me is around the new questions we'll be able to ask," said Rohit Singh , a computational biologist at Duke University School of Medicine who uses AI to find patterns across millions of cells. Decades of progress in computing have resulted in more sophisticated machine learning algorithms and better computing software and hardware. Those advances include graphics processing units (GPUs) that enable researchers like Singh totrain large AI models and rapidly analyze large-scale datasets. This has expanded what is possible for Duke researchers, who can now complete computational tasks that would have taken weeks in just a few days.
AI fuels Duke research with the potential to improve human lives, from modeling patient hearts with supercomputers and mining health data to predict ADHD risk in children to predicting how molecules will bind to proteins to develop more effective drugs for a range of diseases.
Duke researchers who leverage AI have access to the Duke Computer Cluster , a high-performance computing resource for large-scale scientific applications. The university is also expanding its AI infrastructure with a small GPU center expected to open in 2027 , which will be designed to minimize power and water consumption and carbon emissions through energy-efficient practices. Singh said that access to more GPUs will help accelerate his computational biology research.
"The investments Duke has been making have gone a long way in allowing us to address some of these questions, and from a competitive perspective, be one of the top places in the world that's doing this kind of work," he said.
Accelerating Biological Discovery
Singh uses advanced computing to improve understanding of cell biology, analyzing millions of gene expression profiles and biological sequences with foundation models, which are machine learning models trained on vast datasets. Machine learning allows computer systems to learn patterns from data and make predictions, helping researchers work with biological data at a large scale while reducing the need for time-consuming manual annotation.
"In many ways I almost think of foundation models as microscopes," Singh said. "They help us see biology with a new perspective. Each of them gives you a new aspect of life that you can study."
Training models on "hundreds of millions of data points" requires a "massive computational effort to ingest it all and build a model that can understand it," Singh said. Access to GPUs accelerates this process and makes it easier to build additional models adapted to specific types of research.
Insights from Singh's research can be applied to a wide range of projects, from developing gene therapies to identifying new drugs to treat a range of diseases. The machine learning models he trains can create abstract representations of proteins, genes and cells, allowing him to better understand the ways mutations or diseases may alter their functions.
"If I can learn a good abstract representation of, for example, both healthy cells and cancer cells, then I can try to determine the difference between a cancer cell and try to figure out how I can zero out that difference," Singh said. "Is there a drug that zeros out that difference?"
Building Trustworthy AI
For Duke computer scientist Cynthia Rudin , the impact of advanced computing goes beyond faster hardware and encompasses new algorithms and machine learning methods that have changed the way AI research is done.
"The field and what we can do keeps surprising me," Rudin said. "In my lab, we've designed some algorithms that I didn't think were possible at all."
