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.

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.

I've been working with students for over seven years, from middle school all the way through college, across subjects like math, calculus, statistics, linear algebra, chemistry, and physics, with a lot of SAT and ACT prep mixed in. My background is perhaps a little unconventional. I have two bachelor's degrees, one in Engineering and one in Communication Studies, plus a Master's in Design. That combination means I can guide you through challenging technical material and communicate it in a way that is easy to grasp. What I care most about is helping students get to a place where they don't need me anymore. I know that sounds like a strange thing for a tutor to say, but I think it's the right goal. I'm not here to walk you through steps to copy down. I want you to understand why something works, because that's what holds up under pressure, on a test you haven't seen before. If you're ready to ace that test or prove that theorem that's been bugging you, reach out and let's work together

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.

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.

Fresh out of Brown's math program with a 3.87 GPA, Zofia studied linear algebra in the context of both pure and applied mathematics — so she's comfortable moving between determinants and dimension theorems without losing the thread. She's especially sharp at breaking down the moment a course shifts from mechanical row reduction to questions about why certain transformations preserve structure, a transition that derails a lot of otherwise strong math students.

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, eigenvalues, and matrix transformations require a different kind of mathematical thinking than most students have encountered before. Mike's medical and biostatistics training gave him hands-on experience applying linear algebra to data analysis and modeling, so he can ground abstract proofs in concrete applications that make the material more intuitive.

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.

A year as a course assistant in Harvard's math department — teaching introductory calculus — gave Richard a front-row seat to where students first stumble with abstraction, a skill that translates directly to linear algebra's shift from matrix arithmetic to reasoning about vector spaces and linear maps. His government major might seem unrelated, but formal logical argumentation is central to both fields, and he leans on that structured thinking when breaking down proofs involving span, basis, and dimension.

Vector spaces, eigenvalues, and matrix transformations can feel completely disconnected from any math a student has seen before. Nikhil's NYU math program puts linear algebra at the center of his training, and he teaches it by grounding abstract definitions in geometric intuition — showing what a linear transformation actually does before diving into the computation.

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.