Award-Winning Linear Algebra Tutors
serving Knoxville, TN

Aerospace engineering runs on linear algebra — from coordinate transformations in flight dynamics to solving systems that model structural loads. Aubrey uses these tools daily in her coursework at the University of Tennessee, so she teaches eigenvalues, matrix decompositions, and vector spaces with the kind of intuition that comes from applying them to real engineering problems.

Vector spaces, eigenvalues, and matrix decompositions can feel impossibly abstract without someone who lives in that world daily. As a PhD student in mathematics at the University of Memphis with degrees from Delhi University and IIT Bombay, Monika teaches Linear Algebra with the depth of someone who uses these tools in her own research. She unpacks proofs and computational techniques side by side so students see both the logic and the application.

I'm a graduate student in Applied Statistics with experience in general math and computer programming to boot. I currently have a PhD in Applied Statistics with experience in data analytics. I've had the opportunity to do some math and statistics work for hospitals and research facilities, and I enjoy getting to share my passion for math with others to assist them in their education.

Jacob's math degree and computer science master's give him two distinct lenses for linear algebra — he can work through the abstract proof side (subspaces, dimension, linear maps) and then turn around and show how those same ideas drive algorithms in machine learning and graphics. That dual fluency is especially useful when a course suddenly shifts from Gaussian elimination to proving properties of inner product spaces. Holds a 5.0 rating.

Studying applied mathematics as an undergrad means Daniel is working through linear algebra right now — not remembering it from a decade ago, but actively sitting with determinants, subspaces, and eigenvalue decompositions in his current coursework. He's the kind of tutor who had to grind through the confusing parts himself and build understanding step by step, so he knows exactly which explanations actually clarify things versus which ones only make sense if you already get it. Rated 4.7 by students.

Vector spaces, matrix transformations, and eigenvalues require a shift in thinking from computation to abstraction that trips up many students. Tracey's graduate work in mathematics education gives her a framework for breaking down that conceptual leap — she connects linear algebra ideas to geometric intuition so that proofs and definitions feel grounded rather than arbitrary.

One thing which draws me to teaching mathematics and physics is that I have always been passionate about the beauty of mathematics and its deep connections to nature. Mathematical beauty is underappreciated and I like to evangelize. The more people understand mathematics, the more people can learn to recognize its beauty. The best education teaches a love of learning in itself, which is something I hope to impart to any students I work with. I also come from a family of teachers, as both my mother and her mother were teachers, and I have various cousins who are also involved in education. Education is in my blood, so to speak. I also have several years of personal experience tutoring and teaching courses. I have an extensive background in mathematics and physics. I have a dual bachelor's degree in the subjects, as well as graduate school in physics. My research in physics was focused on a particular aspect of string theory known as conformal field theory which elucidates deep connections between algebra, geometry, complex analysis, and physics. A full explanation of the research is beyond the scope of this statement, but I hope to convey my experience with the relevant subjects.

Samuel holds a Ph.D. in Applied Mathematics, which means linear algebra isn't a course he passed — it's a language he works in daily, from inner product spaces to spectral decompositions. He's particularly effective at teaching the proof-writing transition that trips students up mid-semester, when the course shifts from row reduction to reasoning about abstract vector spaces and linear maps. Rated 5.0 by students.

Studying statistics and machine learning at Princeton means Julie uses linear algebra daily — from matrix transformations to eigenvalues to vector spaces. She teaches the subject with an eye toward both theoretical understanding and practical application, connecting abstract proofs to the computational intuition students need to actually work problems.

Currently studying mathematics as an undergrad, Henry is working through the same linear algebra material his students are — which means he knows exactly which definitions trip people up when a course introduces abstract vector spaces and suddenly expects fluency with concepts like span and dimension. He breaks down the leap from matrix arithmetic to formal proof-writing in a way that's still fresh, not filtered through years of distance from the struggle. Rated 4.9 by students.

A Ph.D. in Biomedical Engineering means Andrew has relied on eigenvalue problems, matrix decompositions, and systems of linear equations as everyday tools for modeling biological systems — not just as homework exercises. He's especially strong at bridging the gap when courses shift from row reduction mechanics to the abstract reasoning behind vector spaces and linear maps, drawing on years of applying those concepts in research. Rated 4.9 by students.

Philosophy trains you to build rigorous arguments from axioms — which turns out to be exactly the skill linear algebra demands once a course moves past computation into proofs about vector spaces, linear independence, and spanning sets. Joshua's background in formal logic means he treats proof-writing as structured reasoning rather than guesswork, breaking down what each definition actually requires before students attempt to use it. He's especially useful for the mid-semester shift when homework stops being row reduction and starts asking "prove that this map is injective."

I am highly praised by my students and supervisors. Even today I still kept the communication with many students.

I am passionate about the importance of math and science, I enjoy making them more relatable to a student by explaining their real world applications whenever possible.

Eigenvalues, vector spaces, and matrix decompositions sit at the heart of nearly every applied math discipline — and Dr's Ph.D. in Applied Mathematics means he's used these tools in practice, not just taught them from a textbook. He unpacks abstract proofs by tying them to concrete computations, so students see why a basis matters before they're asked to find one. That combination of theory and application is especially useful for students heading into data science, physics, or engineering coursework.