Professional Summary

I am a Ph.D. student in Mathematics at Oregon State University, focusing on the development and analysis of numerical methods for differential equations and their applications in computational electromagnetics, probability theory, uncertainty quantification, inverse problems, and machine learning. My research bridges mathematical rigor with computational efficiency, contributing to the next generation of tools for simulation, modeling, and data-driven discovery.

Research Interests

• Numerical Methods for Ordinary and Partial Differential Equations (ODEs/PDEs)
• Computational Electromagnetics and Simulation of Maxwell’s Equations
• Probability Theory and Stochastic Modeling
• Uncertainty Quantification and Bayesian Inference
• Inverse Problems and Optimization-based Parameter Estimation
• Machine Learning for Scientific Computing and PDE Modeling

Education

Research Experience

Graduate Research Assistant — Oregon State University

Duration: [Start Year] – Present

• Developed high-order numerical schemes for time-dependent PDEs.
• Applied stochastic Galerkin and Monte Carlo methods for uncertainty quantification.
• Integrated machine learning models with PDE-based inverse problem frameworks.
• Collaborated with interdisciplinary teams in applied physics and computational engineering.

Teaching Assistant — Oregon State University

Duration: [Start Year] – Present

• Led problem-solving sessions in Calculus, Linear Algebra, and Differential Equations.
• Designed computational assignments using MATLAB and Python.
• Mentored undergraduate students in mathematical modeling projects.

Technical Skills

• Programming: Python, MATLAB, Julia, C++, R
• Mathematical Tools: NumPy, SciPy, FEniCS, TensorFlow (for PINNs)
• Computational Techniques: Finite Element/Finite Difference Methods, Monte Carlo Simulation
• Machine Learning: Physics-Informed Neural Networks, Surrogate Modeling

Publications & Presentations

• [Your Name], “High-Order Numerical Schemes for Maxwell’s Equations,” Journal of Computational Physics, (submitted).
• [Your Name], “Bayesian Inference for PDE-Constrained Inverse Problems,” SIAM Conference on Computational Science and Engineering, 2025.
• [Your Name], “Physics-Informed Neural Networks for Stochastic PDEs,” arXiv preprint, (in preparation).

Professional Memberships

• Society for Industrial and Applied Mathematics (SIAM)
• American Mathematical Society (AMS)
• IEEE Computational Electromagnetics Society (ACES)