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.
Industries
N/A
Nr. of Employees
small (1-50)
Products
Mechanistic medical digital twin models (research artifacts)
Mechanistic, model-based digital twin implementations representing immunological and physiological systems for use in precision medicine research and in silico experimentation.
Synthetic dataset generation from critical illness digital twins
Frameworks and generated datasets produced by mechanism-based digital twins for critical illness (e.g., ARDS, sepsis) intended for ML training and validation.
Mechanistic medical digital twin models (research artifacts)
Mechanistic, model-based digital twin implementations representing immunological and physiological systems for use in precision medicine research and in silico experimentation.
Synthetic dataset generation from critical illness digital twins
Frameworks and generated datasets produced by mechanism-based digital twins for critical illness (e.g., ARDS, sepsis) intended for ML training and validation.
Expertise Areas
- Medical digital twins
- Agent-based and multi-scale modeling
- Translational systems biology and computational immunology
- Machine learning for clinical and translational applications
Key Technologies
- Agent-based modeling
- Multi-scale computational modeling
- High-performance / distributed computing
- Machine learning (supervised/unsupervised)
Key People
Lab Director, Co-Founder
Faculty Researcher, Co-Founder
Postdoctoral Fellow
Resident Physician and Researcher
Machine Learning Engineer
Machine Learning Engineer
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.
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.