Randles Lab

📢 Calling Duke undergraduate researchers! The Randles Lab is recruiting through Pratt Research Fellows! Interested in computational modeling, artificial intelligence, and cardiovascular health? Join us to explore how computational methods can help us understand blood flow and how it changes over time. Project: Computational methods for long-term hemodynamics assessment Students will analyze blood flow simulation data, explore patterns across activity states, and use statistical and computational tools to study cardiovascular function. We encourage students interested in AI, computational modeling, and data analysis to apply. Experience with Python and statistics is useful. 📅 Application deadline: October 31 🔗 Apply through Pratt Research Fellows: pratt.duke.edu/academics/und… Learn more about our research: randleslab.pratt.duke.edu/re…

Congratulations to Japneet Kaur Mavi on passing her preliminary examination! 🎉 Japneet’s research combines patient-specific blood flow simulations, wearable data, and cardiovascular digital twins to study how blood flow changes over time and how those changes may inform our understanding of cardiovascular disease. Her thoughtful presentation and strong responses to challenging questions demonstrated the depth of her knowledge and the care she brings to her research. We’re proud of her hard work and excited to see her dissertation research take shape! Please join us in celebrating this milestone! 👏 Randles Lab Duke University Duke University Pratt School of Engineering Duke BME #PhD #DigitalTwins #CardiovascularResearch

Want to know what Duke is doing in Computational Health? Check it out at BMES! Members of Duke’s Center for Computational and Digital Health Innovation are sharing their work through 19 presentations at #BMES2026—from spatial omics and wearable health analytics to vascular simulation, biomaterials, and biotechnology education. Join presenters from the Randles, Dunn, Hickey, and Grill labs, along with our Associate Director of Education, Cameron Kim, October 22–24 in Orlando. 📍 Find presenters, times, and locations in the flyer. 💡 Stop by the Duke BME booth to explore the Center’s interactive display! Come meet our researchers and see what we’re building together. #DukeEngineering #ComputationalHealth #DigitalHealth

Heading to BMES 2026? Come learn what we’re working on in the Randles Lab—from blood flow in carotid arteries to flow through porous scaffolds. Shea Middleton will present “Quantifying Morphology-Driven Hemodynamic Changes in the Carotid Bifurcation Through Simulated Forward Head Posture,” exploring how changes in head posture can influence carotid artery geometry and blood flow. Shihab Kabir will present “Identifying Minimum Spanning Sets of Granular Material Distributions to Capture Fluid Flow Behavior,” a poster highlighting an approach to identifying representative flow behavior in porous scaffolds while reducing simulation costs. You can also explore an interactive display from the Duke Center for Computational and Digital Health Innovation (CDDHI) at the Duke BME booth. Stop by to see our work in action and talk with us about computational and digital health research! #BMES2026 #RandlesLab #DukeBME #CDDHI #ComputationalModeling #BiomedicalEngineering

📚 CCDHI CompHealth Corner — What are we reading this week? Can AI recognize when its predictions might be uncertain? A new study published in npj Digital Medicine, co-authored by Evan Calabrese, a member of the Center for Computational and Digital Health Innovation, explores how AI can segment meningiomas on brain MRI while estimating confidence in its predictions. The model achieved high segmentation accuracy, and its uncertainty maps highlighted difficult tumor boundaries and other areas where predictions were less reliable. The researchers also translated this uncertainty into ranges around tumor volume measurements, which could help distinguish meaningful tumor growth or treatment response from measurement variability. The approach was externally validated using MRIs from 353 Duke patients, where it maintained high segmentation performance. This work points toward more transparent AI tools for monitoring brain tumors, with uncertainty estimates that could help clinicians identify predictions needing closer review. Read more and access the paper: comphealth.duke.edu/publicat… Duke University | Duke University Pratt School of Engineering | Duke BME

🇩🇪 Randles Lab is heading to FTC 2026 in Germany! What does it take to make a digital twin reproducible at exascale? Randles Lab’s Ayman Yousef will present “Towards Trustworthy Exascale Computing: Deterministic Algorithms for Reproducible, GPU-Accelerated Digital Twins” at FTC 2026 in Berlin. The work profiles two parallel approaches to determinism in large scale scientific computing. Reproducibility helps researchers debug, validate, and share their codes across computing environments. Join Ayman on October 15, 11:30 AM to 12:45 PM, in Computing Session 1, Rooms 2+3. If you’re attending, come talk with us. Conference details: saiconference.com/FTC #FTC2026 #HPC #GPUComputing #DigitalTwins #Reproducibility

The best way to understand what our labs are working on? See it for yourself. At our first Demo Day for the Center for Computational and Digital Health Innovation, visitors tried interactive demonstrations, explored posters and short presentations, and talked directly with the researchers behind the work. Students also had a chance to learn about research opportunities and connect with labs. A big thank you to Samreen Mahmud and Nusrat Sadia Khan, who served as the event’s main organizers, and to all the participating labs and attendees who made the day such a success. Read Samreen’s reflections on Demo Day: comphealth.duke.edu/demo-day…

What will it take for the next wave of AI advances to change everyday patient care? The Center for Computational and Digital Health Innovation hosted lightning talks and a lively panel at the Duke Summit on AI for Health Innovation on Thursday, October 8 that I was fortunate to moderate. Boyuan Chen shared advances in physical AI and robotics, Jessilyn Dunn discussed how wearables can capture health beyond clinic visits, Cameron McIntyre explored computational models that guide deep brain stimulation, and Dan Ma highlighted how quantitative MRI can reveal tissue properties that conventional images miss. Ajit Menon PhD brought an industry perspective on deploying AI at scale. The discussion explored how healthcare is evolving across these areas and the practical questions that come with that progress. What makes a model trustworthy enough to guide treatment? How do we distinguish meaningful changes from noisy data? And how do we move a successful research tool into routine care? One memorable takeaway was the call to rethink R&D as 'Research and Deployment'. There is exciting work ahead, and getting it into the hands of clinicians and patients matters. We were delighted to participate in the summit for our third year. Thank you to our speakers, the summit organizers, and everyone who joined the conversation for such thoughtful questions and an engaging session! #AIforHealth #DigitalHealth #HealthInnovation

📚 CCDHI CompHealth Corner — What are we reading this week? What does it take to become a better mentor? Mentoring is a skill we can develop and a new study in Biomedical Engineering Education explores how intentional training can help. Co-authored by Cameron Kim, Associate Director of Education at the Center for Computational and Digital Health Innovation, the study examines a Duke course that prepares graduate students to mentor high school researchers through the Duke Research in Engineering Program (DukeREP). The course brings together teaching, leadership, community-building, and wellbeing with hands-on mentorship practice. Graduate student reflections indicated greater confidence and stronger teaching, communication, and leadership skills, helping them create more supportive, student-focused learning environments. The paper got us thinking about the many ways people share their experience and support one another at the Center. At our recent Espresso Chat, Thomas Shepard offered advice on changing roles, taking advantage of opportunities, and showing what you can contribute through hard work and initiative. Cameron Kim also brought students together for a Community of Practice session to share ideas and learn from each other about AI tools. These experiences offer different forms of mentorship, but share a common thread: making time to help others grow. What advice from a mentor has stayed with you? Sharing experience helps others grow. duke.is/mentoringwell #Mentorship #CompHealth

📣 Today! Join our AI in Health Community of Practice from 12–1 PM in Wilkinson 321. Students, clinicians, and educators: come compare AI tools, share what’s working, discuss challenges, and explore resources available through Duke. Bring your questions and ideas to help shape what’s next! Register and contact Cameron early to get lunch!