Award-Winning Linear Algebra Tutors
serving Manhattan, NY

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.

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.

Studying physics at Stony Brook means Kiran has diagonalized Hamiltonians, decomposed tensors, and solved coupled systems where linear algebra isn't a separate course but the backbone of every calculation. That physics-native fluency is especially useful for teaching determinants, eigenvectors, and change-of-basis — he can explain what these operations actually do to a system rather than just how to execute them. Rated 4.7 by students.

Rebecca's background is in international development and sociology rather than pure mathematics, so she approaches linear algebra as someone who had to build real understanding of matrix operations, systems of equations, and transformations from the ground up. That perspective makes her especially effective at breaking down the logic behind each step — she remembers what it's like when row reduction or determinant properties don't yet feel intuitive. Rated 5.0 by students.

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

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.

I graduated from Dartmouth College with a double major, receiving a Bachelor of Arts in both Biochemistry/Molecular Biology and Music. I continued my education at Columbia University and received Master of Arts in Biology. Starting in middle school and continuing through my graduate career, I have tutored students in a wide variety of subjects, but I was most effective at tutoring math and science because of my lifelong love and aptitude for these subjects. Since I am also working towards a career in molecular biology, I use math and science every day, and I can explain real-world applications and uses for these subjects that may not seem obvious. By demonstrating the use of math and science in everyday life, I am able to help interact with the student and increase their interest in a subject in which they may experience difficulty. I also believe that as a tutor, it is my responsibility to engage with the student to help them achieve and even surpass their goals. In my spare time, I am heavily involved with music in New York City, being part of multiple choirs and continuing to play piano. I also enjoy exercising and exploring the city whenever I have the chance.

Training at ETH Zurich's applied math program means Shahnawaz worked through linear algebra at a level where concepts like spectral decompositions, Jordan normal forms, and singular value factorizations were prerequisites for more advanced coursework — not endpoints. He digs into the geometric intuition behind abstract definitions, showing students what a null space or eigenvector actually looks like before formalizing the algebra around it. Rated 4.9 by students.

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.

Double-majoring in applied mathematics and physics at RPI meant Daniel spent four years using linear algebra as connective tissue between disciplines — diagonalizing operators in quantum mechanics one day, then proving properties of vector spaces in a pure math course the next. That constant back-and-forth between computation and theory gives him a sharp sense for where students lose the thread, particularly when eigenvalue problems or abstract definitions of span and independence stop feeling like calculator work and start requiring real reasoning.

Decision sciences at the graduate level means Benedetto spent serious time with matrix operations, optimization models, and systems of equations — the applied side of linear algebra that many pure-math tutors gloss over. He's particularly strong at walking through how concepts like rank, null space, and linear transformations show up in real decision-making and quantitative modeling contexts. Rated 4.7 by students.

Eigenvalues, vector spaces, and matrix transformations aren't just abstract theory for Kirollos — his dual CS and Electrical Engineering program at NYU puts linear algebra at the center of everything from machine learning algorithms to circuit analysis. He unpacks the geometric meaning behind row reduction and change-of-basis so the computations actually make sense.

Most linear algebra students can mechanically row-reduce a matrix but freeze when asked what the result actually means about the underlying system — Nick zeros in on that interpretive gap, connecting procedures like finding determinants and solving Ax=b to the geometric and structural ideas they represent. His math degree and experience teaching across the full calculus sequence through multivariable and beyond means the prerequisite connections are always at his fingertips. Rated 4.9 by students.

Tackling vector spaces, matrix operations, and eigenvalues requires a tutor who can connect abstract theory to concrete applications. Cole's finance coursework at Fordham's Gabelli School of Business means he regularly uses linear algebra in portfolio modeling and data analysis, so he teaches these concepts with real-world context that makes the abstraction click.

When a linear algebra course suddenly expects students to prove that a set of vectors forms a basis or that a map preserves dimension, the jump from computation to abstraction can be disorienting. Jonathan's math degree and his experience teaching across the full K-through-college spectrum means he's seen exactly where that conceptual gap opens up and knows how to close it — building from familiar matrix operations toward the reasoning behind them. Rated 5.0 by students.

Pharmacy and pharmaceutical chemistry might not scream linear algebra, but Zachary's doctoral training required heavy quantitative modeling — pharmacokinetic systems, multivariate data analysis, and the matrix math underneath statistical methods he uses across his science and math tutoring. He breaks down concepts like matrix operations, determinants, and systems of equations by tying them to concrete problem-solving rather than leaving them as abstract definitions. Rated 4.9 by students.

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.

Biomedical engineering coursework throws you into systems of linear equations, matrix transformations, and eigenvalue problems long before you'd encounter them in a standalone math class — Thomas worked through all of it earning his engineering degree, where linear algebra was the language for modeling everything from signal processing to biomechanical systems. He breaks down the mechanics of each operation step by step, making sure students understand what a determinant or null space actually represents before moving on to the next concept. Rated 5.0 by students.

I am a graduate of Cornell University's College of Arts and Sciences. I received my Bachelor of Arts in Chemistry with Distinction in 2015. Since graduation, I was a physics/chemistry teacher and soccer coach at a private school in Virginia for a year, where I led the soccer team to an undefeated season. Before teaching and coaching professionally, I was a Teaching Assistant for the Cornell Math and Physics Departments, where I taught many subjects including calculus, mechanics, electromagnetism. Throughout my time at Cornell and as a teacher, I tutored subjects ranging from the SAT to AP Physics and Algebra II, which is where my true talents lie: in small group or one-on-one settings where I can give students the full attention they deserve and tailor my approach specifically to their learning styles. This is why I am now pursuing tutoring as a part-time occupation at Varsity Tutors. I embrace teaching all math and science subjects, especially physics and calculus, at both the college and high school level and will go above and beyond to make sure all of my students succeed, according to their definition of success. In my spare time, I enjoy playing league soccer, basketball, tennis and guitar, and also like to travel and see as much of the world as I can.

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.

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."

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.

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.

I am a senior with a Neuroscience major at Swarthmore College. My favorite subjects include Biology and Psychology. I am interested in teaching students how to develop a better grasp of their academic material, improve their learning skills, and succeed in whatever course they take. Outside of the classroom, I enjoy playing violin, reading, and traveling. I also have extensive community service experience and have traveled to China, Kenya, and the Dominican Republic to engage in volunteer work.

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.

Every physics problem Cory solved during his B.S. — from coupled oscillators to electromagnetic field equations — depended on manipulating matrices, decomposing systems, and thinking in terms of vector spaces, so linear algebra is baked into how he reasons about math. He zeroes in on the spots where students lose the thread, like understanding what an eigenvector actually represents geometrically or why a change of basis simplifies a problem instead of complicating it. Rated 4.9 by students.

Bioproducts and Biosystems Engineering at Minnesota means Shannon has worked through systems of equations, matrix operations, and eigenvalue problems in the context of modeling real physical and biological processes — so the material isn't abstract theory she learned once and forgot. She breaks down concepts like determinants and vector spaces by tying them back to the engineering applications where they actually do something, which tends to unstick students who can follow the mechanics but don't see the point. Scored a 32 on the ACT.

Engineering physics at Colorado School of Mines means Jude is constantly using eigenvalue problems, matrix transformations, and decompositions to model real physical systems — so the concepts in a linear algebra course aren't abstract hoops to jump through but tools he actively relies on. He breaks down the transition from mechanical row reduction to reasoning about vector spaces and linear maps by tying each new definition back to something concrete and computable. Rated 4.9 by students.