Mathematics

Research interests

Professor Bokil's general research interests are in applied mathematics, scientific computing, numerical analysis and mathematical biology. Her primary research interests are in the numerical solution of wave propagation problems. Specifically, she has conducted research on the numerical solution of Maxwell's equations using a variety of finite difference and finite element methods. Bokil is also working on several problems in mathematical ecology which involve the construction and analyses of  deterministic and stochastic models for applications in population dynamics, epidemiology and spatial ecology.

Associate Editorships:

1. IEEE JOURNAL ON Multiscale and Multiphysics Computational Techniques
2. International Journal of Numerical Analysis and Modeling

Current Grant Funding

Dr. Bokil is currently a co-PI on two NSF funded projects in computational mathematics, one project in mathematical biology funded by the FACE foundation and an OSU internal grant funded by the SciRiS program in the college of science.

1. NSF DMS #1720116 OP: Collaborative Research: Compatible Discretizations for Maxwell Models in Nonlinear Optics in collaboration with Drs Yingda Cheng at Michigan State and Fengyan Li at RPI.
2. NSF DMS #2012882: Computational and Multi-Scale Methods for Nonlinear Electromagnetic Models in Plasmas and Nanocomposites in collaboration with Drs Nathan Gibson and Pallavi Dhagat, both at Oregon State.
3. Mathematical epidemiology of viruses coinfecting plants: Modeling, Analysis and Optimal Control Strategies, funded by the Thomas Jefferson Fund launched by the French-American Cultural Exchange (FACE) Foundation  Along with French collaborator and co-PI, Professor Frederic Hamelin of Agrocampus Ouest in Rennes, France, this project will study coinfection in viral plant epidemics.
4. Optimal Control of Stochastic Epidemics, funded by the College of Science Research and Innovation Seed (SciRIS-II). In this project we address the modeling of coinfecting viruses in plants. Our goals are to use stochastic models and optimal control theory to understand the mechanisms that drive patterns of coinfection in plant populations and the effective techniques that can control spread of disease.