Award-Winning Linear Algebra Tutors
serving Detroit, MI

I'm a recent graduate from Reed College where I majored in Political science with additional concentrations in environmental science, sociology, philosophy, and 3D art.

Studying both biomedical and chemical engineering at Vanderbilt means William encounters linear algebra from two applied angles — modeling biological systems and solving material balance equations — which gives him an intuitive grasp of why concepts like matrix operations and eigenvalue problems matter beyond the homework set. He breaks down the mechanics of row reduction and determinants while connecting them to the engineering contexts that make the abstraction feel purposeful. Rated 4.8 by students.

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 linear transformations often feel abstract until someone shows you what they actually do — and Bahaeddine's statistics PhD means he uses linear algebra constantly, from regression in matrix form to principal component analysis. He unpacks proofs and computations side by side so the theory and the mechanics reinforce each other.

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.

Eigenvalues, vector spaces, and matrix decompositions show up everywhere in engineering — and Sabry used them extensively in his doctoral research on computational modeling. He unpacks linear algebra by tying each concept to a geometric or physical interpretation: what a determinant actually measures, why eigenvectors matter for system stability, how a change of basis simplifies a problem. That dual perspective makes the subject far more intuitive than rote row-reduction ever could.

Kaitlin's linguistics training — parsing formal grammars, mapping structural relationships, building logical proofs about language systems — translates surprisingly well to the abstract reasoning linear algebra demands. She tackles concepts like span, linear independence, and basis by treating them as structural puzzles rather than purely computational exercises. It's an approach that clicks especially well for students who struggle when the course shifts from row reduction to proof-writing.

Eigenvalues, vector spaces, and matrix decompositions stop being abstract once you've used them to solve real systems — and Moe's electrical engineering master's work relied on linear algebra constantly, from signal processing to circuit analysis. He unpacks proofs and computations side by side so students understand both the theory and the mechanics of each operation.

Eigenvalues, vector spaces, and matrix transformations can feel impossibly abstract the first time through. As a math major at Georgia Tech, Sally has worked through linear algebra at a proof-based level and can unpack ideas like span and linear independence using concrete geometric intuition alongside the formal definitions.

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.

A math degree from Colby plus plans for a PhD means Alexander isn't just passing through linear algebra — he's building a career on the kind of abstract reasoning the course demands. He's particularly good at unpacking the shift from matrix computation to the proof-heavy second half, where students suddenly need to show why a transformation is onto or why a set spans a subspace. Rated 5.0 by students.

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.

Benjamin's master's dissertation at the University of Essex centered on graph theory and group theory — areas where linear algebra isn't just a tool but the structural backbone, from adjacency matrices to representation theory. That research-level immersion means he teaches eigenvalues, vector spaces, and linear maps with the fluency of someone who's built arguments on top of them, not just solved textbook exercises about them.