Research Background & Expertise
I am a Ph.D. student in Applied Mathematics at Kent State University with research interests in Digital Twin technologies, Inverse problems, Bayesian inverse problems, and data assimilation methods such as Kalman and Ensemble Kalman filters. My work focuses on integrating PDE-based mathematical modeling, scientific computing, and medical imaging data to develop predictive computational models for healthcare systems. I am particularly interested in numerical methods for partial differential equations, uncertainty quantification, and large-scale scientific computing.
My research goal is to advance digital twin technologies in healthcare by developing robust mathematical and statistical methodologies that combine physics-informed models, imaging data, and real-time measurements. I aim to design computational frameworks that support early diagnosis, continuous patient monitoring, and optimized therapeutic decision-making, while rigorously accounting for uncertainty and data variability.
In the long term, I seek to collaborate with interdisciplinary teams across mathematics, engineering, and medicine to translate theoretical advances into deployable healthcare technologies that improve patient outcomes and strengthen data-driven medical decision support systems.