Award-Winning Linear Algebra Tutors
serving New York, NY

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Research interests in geometry and mathematical physics mean Anthony lives in the territory where linear algebra gets interesting — thinking about how transformations act on spaces, not just how to row-reduce a matrix. His teaching assistant work in multivariable calculus and mechanics gave him practice explaining the geometric intuition behind concepts like eigenvectors and change of basis to students encountering them for the first time.

I am working towards a Bachelor of Arts in Pure and Applied Mathematics as well as a Bachelor of Arts in Astronomy and Physics. I have enjoyed studying math and science since I was in elementary school. I would always help my friends out by answering their questions about the material. For about the last five years, I have had my own tutoring business where I have tutored a wide variety of math courses from elementary school math to pre-calculus and calculus. I like to make sure my students have a complete understanding of the core concepts before going into practice questions. I have also had experience helping my peers with physics and computer science courses.

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.

Sarah's Penn math degree covered linear algebra at the proof-heavy level where determinants and row reduction give way to abstract vector spaces, linear maps, and dimension arguments — and her statistics minor means she's also seen how matrix factorizations and eigendecompositions power real data analysis. She breaks down the notoriously tricky shift from computation to abstraction by building students' geometric intuition for what transformations, span, and independence actually mean. Rated 4.9 by students.

Vector spaces, eigenvalues, and matrix transformations can feel disconnected from any math students have seen before. Jett's electrical and computer engineering program at UT Austin relies heavily on linear algebra for signal processing and systems analysis, so he teaches these abstractions through the lens of what they actually *do* — rotating coordinate systems, solving coupled equations, compressing data.

Eigenvalues, vector spaces, and matrix transformations can feel impossibly abstract without someone who connects them to real applications. Michael studied biomedical engineering at the University of Rochester, where linear algebra was foundational to signal processing, imaging, and systems modeling — so he teaches these concepts with concrete examples that make the abstraction meaningful. He's especially effective at walking through proof-based problems step by step.

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.

Teaching linear algebra as adjunct faculty at Washington State University means Moayad isn't just tutoring this material — he's designing syllabi, writing exams, and watching in real time where students lose the thread between matrix computation and abstract vector space theory. His two math degrees (BS and MS, the latter from Oregon State) gave him deep fluency with everything from determinants and eigenvalue problems to the proof techniques that trip students up mid-semester.

I'm trying to work on personal projects. I really enjoy snowboarding, and have been doing that since the third grade. I also enjoy playing sports and video games.

Pursuing both a BS and MS in Computer Science and Mathematics at Tufts means Julie uses linear algebra constantly — from implementing matrix transformations in code to working through the proofs that justify why those algorithms converge. She's especially sharp at connecting the computational mechanics of determinants, eigenvalues, and row reduction to the programming contexts where students can actually see the output change when a matrix is singular or a basis is swapped. Rated 5.0 by students.

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.