Trained engineer teaching Calculus, Physics, & Python/MATLAB

Ray T.

I hold a master's degree in Mechanical Engineering and a master's degree in Computer Science from the University of Virginia, with a background that combines hands-on engineering research and computational problem-solving. My work has focused on computational fluid dynamics, thermal-fluid systems, and physics-informed machine learning, and I have co-authored technical papers presented at ASME and EMNLP conferences. I have also passed the Fundamentals of Engineering (FE) exam, which reflects a strong, verified foundation across calculus, physics, thermodynamics, and fluid mechanics.

As a graduate teaching assistant, I've supported students across math, engineering, and computer science courses at two universities. At the University of Virginia, I did the same for Applied Partial Differential Equations, Differential Equations and Linear Algebra, Probability and Statistics in Computer Science, Foundations of Data Analysis, Machine Learning, and Bayesian Machine Learning, and I also graded for an undergraduate probability course at the University of Delaware. Most of that time was spent one-on-one with students during office hours, walking through problem sets step by step and helping them find where their own reasoning broke down, rather than just handing them the answer.

I enjoy breaking down abstract concepts into concrete, intuitive explanations, and I'm comfortable teaching everything from calculus, differential equations, linear algebra, and probability/statistics to physics, vibrations, fluid mechanics, and Python/MATLAB or machine learning. Whether a student needs to catch up on fundamentals or wants to go deeper into coursework, I tailor my approach to how they learn best and focus on building real understanding.