College of Science

Padmanabhan Seshaiyer, PhD

Professor, Department of Mathematical Sciences | Associate Dean for Academic Affairs
Director,
Education

PhD, Applied Mathematics, University of Maryland, Baltimore County

Key Interests
Mathematical Modeling | Computational Biomechanics | Infectious Diseases | Bio-inspired Systems | Parameter Identification | Deep Learning

Research Focus

My research interest is in big data and health informatics. My primary focus is developing algorithms which optimize data analysis and predictive modeling using electronic health records (EHR). One project examines the association of antibiotics and hemoglobin A1c (HbA1c) with mortality. Another project concerns diabetes, obesity and the discordance between county-level diabetes and obesity prevalence among veterans. I am also developing tools which can predict short- and long-term risk of mortality. My other research interests include examining outcomes associated with Clostridium difficile infection and developing a decision support tool for early identification of Alzheimer’s disease in a pre-clinical stage.

Current Projects

■ Mathematical modeling, analysis, and simulation of the spread of infectious diseases, such as Zika

■ Use of Technology to Manage Stimulus Cues and Reduce Drug Relapse: A STEAM-H Initiative

■ Quantification of biomechanical properties to predict rupture potential of intracranial saccular aneurysms

■ Investigating mathematical modeling, experiential learning, and research through professional development and an integrated online network for elementary teachers

Select Publications

H. C. Matto et al., Harnessing the power of the recovering brain to promote recovery commitment and reduce relapse risk. J Soc Social Work Res. 9(2), 341-358 (2018).

P. Padmanabhan et al., Mathematical modeling, analysis and simulation of the spread of Zika with influence of sexual transmission and preventive measures. Biomathematics 4(1), 148-166 (2017).

P. Seshaiyer et al., Leading undergraduate research projects in mathematical modeling. PRIMUS, 1-18 (2017).

P. Seshaiyer et al., A sub-domain inverse finite element characterization of hyperelastic membranes including soft tissues. J Biomech Eng. 125(3), 363-371 (2003).
 


Exploratory Hall | 10431 Rivanna River Way, Fairfax, VA 22030