Award-Winning Linear Algebra Tutors
serving Kansas City, MO

I am interested in Physics and Mathematics and working out practical problems from plumbing to electronics. I will someday go back for my Ph.D. in Physics but until then I am looking to grow as an engineer or computer programmer.

Mechanical engineering runs on linear algebra — from solving systems with matrix operations to understanding vector spaces and eigenvalues in structural analysis. Jordan brings that applied perspective to a subject that can feel painfully abstract, connecting each theorem back to problems where matrices actually do something useful.

I love to teach. I love young minds and fresh brains. Those are just like clean sheets of papers I can draw anything I like. I really like to help young people to achieve their full capacities with my long experience of teaching. I am very patient and good at explaining complex concepts in simple terms. I am looking forward to meeting students who need my help.

A PhD in Statistics built on a biomedical engineering foundation means Sam has leaned heavily on matrix algebra — from multivariate regression to principal component analysis — where understanding rank, column space, and decompositions isn't optional. He breaks down the theoretical side by showing students how each abstraction maps onto a statistical or engineering problem they can visualize. Rated 4.9 by students.

Teaching middle and high school math for several years means Jacob has watched students build from basic systems of equations all the way up to the abstraction that linear algebra demands — he knows exactly which foundational gaps cause trouble when determinants, vector spaces, and matrix operations enter the picture. His math degree and competition math background give him the formal training to tackle both the computational and theoretical sides of the course. Rated 5.0 by students.

Chemical engineering coursework throws you into systems of linear equations, matrix operations, and eigenvalue problems long before you've had time to fully digest the theory — so Adrian knows firsthand which concepts trip students up and which shortcuts actually hold up under pressure. He breaks down topics like determinants, row reduction, and vector space definitions by tying them back to the material and energy balance problems where they naturally show up, giving the abstraction a concrete anchor.

An applied mathematics degree plus doctoral-level engineering work means Professor Florence has lived in the world of matrix algebra, systems modeling, and linear transformations across multiple disciplines — from pure theory to design applications. She teaches determinants, eigenspaces, and change-of-basis not as isolated procedures but as interconnected ideas that build on each other, which is especially useful when courses demand both computation and conceptual reasoning.

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.

Eigenvalues, vector spaces, and matrix decompositions aren't just theoretical exercises — they're tools Adel used routinely during his PhD for finite element analysis and dynamic systems modeling. He unpacks linear algebra by showing students the geometric meaning behind each operation, so that abstract proofs and computational techniques reinforce each other instead of feeling like separate courses.

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.

Mechanical engineering PhD work means Ian solves systems of equations, decomposes matrices, and manipulates eigenvalue problems as routine steps in modeling heat transfer and fluid flow — so he teaches linear algebra with the instinct of someone who depends on it daily. He's particularly good at demystifying abstract operations like matrix factorizations and determinant properties by tying them back to the physical systems they describe. Rated 4.9 by students.

I am currently a graduate student in Chemical Engineering at the University of Delaware. I am working on using magnetic and flow fields to create advanced materials by directing the self-assembly process of nanoparticles . I have tutored students in Chemistry, Physics and Math all throughout undergraduate and graduate work. I truly enjoy breaking material down into its core components that allows the students to understand complicated information.

Most linear algebra courses start with comfortable matrix arithmetic and then, around week five, expect students to suddenly reason about abstract vector spaces and prove properties of linear maps — and that's where Joseph steps in. His English background sharpened the kind of close, logical reading that proof-writing actually demands, and he uses that skill to teach students how to unpack definitions of span, independence, and basis before attempting to build arguments with them. Rated 5.0 by students.

I obtained my Ph.D. in Applied Mathematics at the University of Connecticut (UConn) and I now work as a Lecturer for the University of Minnesota-Twin Cities, School of Mathematics. I also obtained both my Bachelor's and Master's of Arts degree from Rhode Island College (RIC) and have worked as a Math Tutor and Teacher's Assistant throughout my time at RIC. Intellectually, I believe knowledge is fluid and requires practice to perfect. I enjoy showing my peers/students different ways of examining problems in order to achieve a well-rounded understanding of the material through derivation, never memorization. My main mission is to showcase my passion for Math and hopefully encourage students to see the beauty and wonder of this phenomenal subject.