Featured Researcher: Jessilyn Dunn
Jessilyn Dunn, PhD, is an Assistant Professor of Biomedical Engineering at Duke. She focuses on biomedical and health data sciences and computational modeling of biological systems. Dr. Dunn leads the BIG IDEAs Lab, where the team believes a next-generation proactive, preventive healthcare system is possible through a combination of new technology development and shifts in culture and policy surrounding healthcare.
Jessilyn brings vision, drive, and scientific excellence to everything she does. She’s leading one of the most exciting labs in the space and building tools that can shift healthcare at scale. It’s a huge asset to have her energy and expertise at the Center.
Here’s what she’s thinking about and working on.
What excites you most about computational and digital health right now?
It’s the potential to really improve human health—at a population scale. I’m excited about the scalability in terms of cost and the number of people that can be reached.
How did you get interested in computational and digital health?
I always enjoyed the combination of biology, math, and medicine, and asking why is this the status quo? Why does this work or not work? And just wanting to know the answers, and if the answers don’t exist, trying to figure out how to find them.
I became more interested over time, seeing the way that healthcare functions—and doesn’t function, especially for large swaths of the population. That was a real motivator to try to improve the well-being of people generally. It feels like this is an area where there’s space to actually move the needle.
What current research are you involved in?
My lab does digital biomarker development, so we use data that comes from various digital sources. One of the main sources is wearables and mobile devices—smart watches, smartphones, smart rings—and applying data science and AI methods to tie that data back to health outcomes and ideally predict outcomes and find optimal points of intervention.
How do you see AI impacting or advancing your work?
We deal with large volumes of data. A sensor might take 100 measurements a minute, and we collect that over months or years. We want to not only make sense of that data, but also determine how that data can be used to predict future outcomes, or classify people into high- or low-risk groups. So we either leverage existing AI methodology—predictive algorithms or unsupervised learning methods—to do that, or sometimes we develop new ones if the existing methods don’t meet the needs of the work that we’re doing.
What’s your outlook for the future of computational and digital health? Where do you see the field being in 10 years?
I think we’re already seeing an impact today. Smart watches have algorithms that can tell people if they have an irregular heartbeat. Tools are being used in hospital systems that can automatically detect when somebody is at risk of sepsis.
In the next 10 years, I think we’re going to see more reliability of these tools—better tools being built on better data, more representative data. So tools will work better, even in edge cases where somebody doesn’t look like the average person in terms of their health or phenotype.
I also think we’re going to see more diverse types of tools in the coming decade. Right now, my lab is completing a year in review for a conference that I co-organize called CHIL, the Conference of Healthcare, Inference and Learning. We reviewed literature on AI and health from 2024 to June 2025, and it’s really amazing. Tools are being added to electronic health records and to support broadening mental health. There’s some great work with large language models and chatbots to detect conditions that are traditionally challenging to detect, and then offer the ability to intervene at scale. When we have low numbers of licensed professionals in certain areas of healthcare, AI tools can step in and meet a need.
What’s something people don’t know about you?
A lot of people don’t know I am a master scuba diver. I grew up in Florida, and my dad got me into it. I got my license at the youngest age you’re allowed.
