AI in the Classroom
B&B faculty Matthew Engelhard, PhD, and David Carlson, PhD, discussed a Duke pilot project on the use of artificial intelligence in classrooms in this article by the Associated Press.
A Smart Sensor for Your Muscles and Tissues
Xiaoyue Ni, PhD, who has a secondary appointment in the Department of Biostatistics and Bioinformatics, led engineers in developing new wearable technology that provides real-time medical and athletic insights.
New Directions in Renewable Energy
David Beratan, PhD, who teaches in the computational biology and bioinformatics program, is one of four Duke chemists exploring promising new directions in renewable energy research.
Can Electronic Health Records Reveal Early Signs of Autism?
Duke researchers, including Ben Goldstein, PhD, are collaborating on a new project that uses machine learning to recognize patterns in electronic health record data associated with children who are later diagnosed with autism.
Tenenbaum Elected to International Academy of Health Sciences Informatics
Division of Translational Biomedical Informatics faculty member Jessica D. Tenenbaum, PhD, FACMI, associate professor of biostatistics and bioinformatics, has been named as a 2025 fellow of the International Academy of Health Sciences (IAHS) Informatics, one of the highest honors in the field.
Meet Bo Hu, PhD
Bo Hu, PhD, professor of biostatistics & bioinformatics, received a PhD in Statistics from the University of Wisconsin-Madison in 2006.
A Rising Star in Computational Digital Health
Learn more about Monica Agrawal, assistant professor of biostatistics and bioinformatics
A New Hub for Fighting Infectious Diseases with Smarter Models
Duke University School of Medicine researchers will launch a new national Center of Excellence for Multiscale Immune Systems Modeling (MISM), funded by a U54 grant from the National Institutes of Health.
Duke Sets National Standards for Safe, Scalable AI in Health Care
Duke University School of Medicine researchers have developed two pioneering frameworks designed to evaluate the performance, safety, and reliability of large-language models in health care.