Assistant Professor
Research and Clinical Interests
Clinical applications of AI/ML, precision medicine, advanced analytics for physiological signal processing, real-time data analytics, risk assessment, complication detection, perioperative monitoring, cerebral autoregulation, vascular stiffness, generative AI, MLOps
Dr. Godwin's research interests lie at the intersection of artificial intelligence (AI), clinical data science, and perioperative medicine. He is deeply committed to advancing AI-informed precision and personalized treatment, with the goal of enhancing patient outcomes and clinical workflows in anesthesiology and perioperative care.
He is part of the data science team which focuses on the development and deployment of robust machine learning (ML) and AI models that leverage large-scale, real-time physiological data. Godwin designs advanced analytics methods for processing and interpreting complex physiological signals—such as heart rate variability, vascular stiffness, and cerebral autoregulation metrics—to drive individualized decision-making during and after surgery.
A key component of his team’s research is the end-to-end translation of novel computational techniques into practical, clinician-facing tools. This includes building and validating models for perioperative risk assessment, early detection of complications, and dynamic patient monitoring, as well as integrating generative AI solutions to reduce administrative burden and support clinical research.
Godwin is especially excited about MLOps and open-source collaboration for scaling and validating AI/ML models in real-world healthcare environments, ensuring reproducibility, transparency, and continuous improvement. My ultimate aim is to use data-driven technologies to personalize care, improve efficiency, and contribute to safer, more effective anesthesia and perioperative management.
Education
Ph.D, Physics
Wake Forest University, Winston Salem, NC
Dissertation Title: Binding NEMO: Adventures in Molecular Dynamics
Certificate in Structural and Computational Biophysics
M.S., Applied Physics
Northern Arizona University, Flagstaff, AZ
Thesis Title: Fast Folding Proteins: Analysis Based on the Energy Landscape and Transition State Ensemble
Graduated with Distinction (4.0 GPA)
B.S., Astronomy & Physics
University of Arizona, Tucson, AZ
Mathematics Minor
Contact
Email
ryangodwin@uabmc.edu