UVM An-Cockrell Lab Center for Biomedical Digital Twins
The UVM An-Cockrell Lab Center for Biomedical Digital Twins is a multidisciplinary research group leveraging advanced computational methods, including machine learning, agent-based modeling, and high-performance computing, to study complex biomedical and physiological phenomena. Their mission is to bridge the gap between basic science and clinical interventions by developing digital twins and computational models that inform precision medicine, therapeutic discovery, and translational research. The lab's work spans detailed organ and disease modeling, AI-driven clinical applications, and collaborative projects such as DARPA-funded initiatives, aiming to revolutionize healthcare through technology and simulation.
UVM An-Cockrell Lab Center for Biomedical Digital Twins
What We Do
A collaborative DARPA-funded project focused on bioelectronics for tissue repair, including research on volumetric muscle loss and the development of smart bandage technologies.
Participation as a funded team in the DARPA Triage Challenge, developing algorithms for rapid, accurate injury identification and physiological decompensation detection from biomedical signal data to improve triage in mass casualty incidents.
AI research and solutions focused on developing algorithms and models for clinical and translational biomedical applications, including the use of machine learning to analyze clinical data, predict disease, and inform therapeutic strategies.
Development of computational digital twins to augment clinical research and enable precision medicine by modeling and simulating biomedical systems using data-driven and mechanistic approaches.
Application Area
Show More (1)Infectious Diseases
Digital Health Technologies
Show More (5)Key People
Lab Director, Co-Founder
Faculty Researcher, Co-Founder
Postdoctoral Fellow
Resident Physician and Researcher
Machine Learning Engineer
Machine Learning Engineer
News & Updates
Selected as a funded team in the DARPA Triage Challenge Data Competition Track, recognizing the lab's leadership in developing advanced algorithms for medical triage.
A publication discussing the development and application of mechanistic medical digital twins in immunology.
A meeting report on the forum discussing immune digital twins and their applications.
Research article on using agent-based modeling to study programmed cell death pathways in cytokine storm.
Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction.
Study on generalizable sepsis prediction using computational models.
Discussion on generating synthetic data for machine learning in biomedical research.