With degrees in both mechanical and electrical engineering, Steve has spent his career translating calculus, differential equations, and linear algebra into tools that solve real physical problems — from circuit analysis to stress modeling in structures. That dual-engineering perspective means he can show students how a single applied math technique behaves across completely different systems, which builds the kind of flexible thinking these courses demand. Rated 4.9 by students.

Machine learning research at Princeton is applied mathematics in its most computationally intensive form — Firas's PhD and postdoctoral work center on building mathematical models that learn from massive datasets, which means he lives inside optimization theory, linear algebra, and probability every day. That background is especially useful for students whose applied math courses lean toward data-driven methods like gradient descent, matrix decomposition, or stochastic modeling. Rated 5.0 by students.

Studying neuropsychology at Princeton means Samantha constantly uses mathematical modeling — from statistical analysis of behavioral data to differential equations describing neural dynamics. She brings that applied lens to topics like optimization, linear programming, and numerical methods, connecting abstract math to the real-world problems it was built to solve.

An applied math degree means Valerie spent years studying how abstract mathematical tools — differential equations, optimization, linear models — actually get used to solve real problems. She brings that same mindset to tutoring: connecting theory to application so the material has context, not just procedure. Her 5.0 rating speaks to how well that approach lands with students.

Logan's physics degree means he's spent years translating calculus and differential equations into models of real systems — projectile motion, wave behavior, gravitational fields — which is exactly the muscle applied math courses demand. He pairs that quantitative training with unusually strong communication skills from his writing background, so he can break down a modeling problem or optimization setup in language that actually clarifies rather than adds confusion.

A Master's in Statistics built on a math degree gives Nicholas an unusually data-fluent take on applied math — he's spent years working with probability models, regression techniques, and statistical inference that sit at the core of many applied math curricula. He also teaches Python and computational problem solving, so when a course requires implementing a numerical method or running a simulation, he can walk through both the math and the code. Rated 5.0 by students.

Scoring in the 99th percentile on the GMAT's quantitative section speaks to Jing's ability to rapidly dissect multi-step problems — exactly the skill applied math courses demand when students face optimization scenarios, financial modeling, or quantitative reasoning under constraints. Her accounting and business management degree means she's most at home in the applied math territory where mathematical tools meet real business decisions, like cost minimization or resource allocation models. Rated 5.0 by students.

Irene earned her PhD in Mathematics and Computer Science, which means she's worked with applied math at the level where you're proving convergence of numerical methods and then coding the algorithms yourself. That combination of deep theory and implementation experience is especially useful for students tackling topics like mathematical optimization or graph theory, where understanding why a method works often determines whether you can adapt it to a new problem. Rated 4.9 by students.

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

Applied math is where equations stop being abstract and start solving real problems — optimization, modeling, statistical inference. With three science degrees from Dartmouth, Arianna has spent years using math as a tool for research and analysis, and she brings that practical fluency into every session.

A Computer Science PhD candidate who also teaches competition math, calculus, and discrete math, Dibyendu approaches applied mathematics from the computational side — algorithm design, numerical analysis, and mathematical modeling that translates directly into code. That dual fluency means he can teach a technique like gradient descent or matrix decomposition and then show exactly how it gets implemented to solve real problems. Rated 4.8 by students.

A PhD in Chemical and Biomolecular Engineering means Sabry didn't just study applied math — he used it daily, building transport models with PDEs, running numerical simulations, and applying linear algebra to reactor design problems. That nuclear engineering undergraduate training adds another layer, since reactor physics leans heavily on techniques like eigenvalue problems and integral transforms that many applied math courses cover in the abstract without showing where they land.

Mechanical engineering coursework at Case Western Reserve is where Kevin first encountered applied math as a daily working language — using differential equations to model thermal systems, linear algebra to analyze forces, and numerical methods to approximate solutions that don't close neatly. That hands-on problem-solving habit carries into his tutoring, where he connects abstract techniques like optimization or matrix operations to the physical scenarios that motivated them. Rated 4.8 by students.

Engineering PhD research is essentially applied mathematics under pressure — Ellyn spent years using differential equations, finite element methods, and biomechanical modeling to solve problems that don't have neat textbook answers. That mechanical and biomedical engineering background means she teaches applied math concepts by connecting them to the physical systems they actually describe. Rated 5.0 by students.

From his PhD research in mechanical engineering through coursework in thermodynamics, statics, and materials science, Adel has spent years turning differential equations and linear algebra into tools for modeling physical systems — heat transfer, stress analysis, fluid flow. That hands-on engineering fluency means he can teach applied math topics by showing students exactly where the math lands in practice, not just how to manipulate symbols on a page. Rated 4.8 by students.

Most applied math tutors come at the subject from engineering or science — Shahnawaz comes from mathematics itself, with both a bachelor's in math and a master's in applied math from ETH Zurich, one of the world's top programs for the field. That pure-to-applied trajectory means he can teach topics like PDEs, numerical methods, or mathematical modeling with full command of the rigorous theory underneath while keeping the focus on how these tools actually get used. Rated 4.9 by students.

Samuel earned his Ph.D. in Applied Mathematics, which means he doesn't just know the theory — he's spent years building and analyzing mathematical models that solve real engineering and physics problems. Whether the topic is optimization, numerical methods, or dynamical systems, he teaches the reasoning behind each technique so students can adapt it to new contexts. Rated 5.0 by students.

I am a firm believer of this and, as such, I do not spoon feed students during sessions but rather guide them to figure out how to answer their own questions and solve their own problems. Thus, I focus not only on what to do, but how and why to do it. One of the most significant drivers of independent learning is curiosity, and this is one of the primary traits I aim to cultivate in students.

Terry's path through criminal justice and fine arts might seem unconventional for applied math, but both fields sharpened his ability to build logical arguments and recognize structural patterns — skills that translate directly when tackling optimization problems or mathematical modeling scenarios. He's particularly effective at teaching students how to set up a real-world problem mathematically before worrying about solving it, which is often the step where applied math courses trip people up. Rated 4.9 by students.

Earning her B.S. in Applied Mathematics from Barrett Honors College at ASU, Madeleine spent her coursework bridging abstract math to real-world physics problems — differential equations modeling physical systems, numerical methods, and optimization. Her experience as a student researcher in a physics lab means she teaches applied math the way it's actually used: translating messy, real scenarios into solvable mathematical frameworks. Rated 5.0 by students.

Operations Research at Princeton is essentially applied mathematics with teeth — Jeff's coursework centered on optimization, probabilistic modeling, and computational methods designed to solve real decision-making problems under constraints. That training, plus his computer science minor, means he can teach applied math topics like linear programming or stochastic models while showing students how to implement solutions in code. Rated 4.8 by students.

Studying mathematics at Georgia Tech means Sally's coursework lives at the intersection of theory and application — she's working through the same differential equations, optimization techniques, and modeling problems that applied math students encounter. That active immersion in a rigorous math program lets her explain not just the mechanics of a method but the reasoning behind choosing one approach over another. Rated 4.9 by students.

Biomedical engineering at UVA meant applying differential equations, linear algebra, and statistical modeling to real biological systems every semester. Colton brings that same applied lens to topics like optimization, numerical methods, and mathematical modeling, connecting abstract techniques to problems students can actually visualize.

Chemical engineering PhD work is dense with applied math — Alexander spent years solving PDEs for transport phenomena, optimizing reaction kinetics models, and running numerical simulations rooted in linear algebra and differential equations. That biosystems engineering foundation underneath means he's comfortable teaching the math both as abstract technique and as something tethered to real mass and energy balance problems.

What makes applied math click for most students isn't more theory — it's seeing how a technique like linear programming or a probability model actually maps onto a decision someone needs to make. Nikhil's mathematics coursework at NYU, combined with his economics and finance fluency, means he can ground abstract methods in business and market scenarios that give the math a clear purpose. Rated 4.8 by students.

Biomedical engineering coursework is where applied math stops being abstract — Thomas spent his degree using differential equations, linear algebra, and statistical modeling to tackle problems like signal processing in medical devices and biological system dynamics. That training means he can ground topics like modeling and numerical methods in tangible applications students can actually visualize. Rated 5.0 by students.

Mechanical engineering is essentially applied mathematics in action — optimization, differential equations, and numerical methods all show up daily. Nikki tackles these topics from the perspective of someone actively using them in her senior-level coursework at Eastern Michigan, connecting mathematical theory to real engineering problems like stress analysis and fluid dynamics.

Studying mathematics while also coding in C++ and Python gives Lawton a hands-on feel for how abstract math concepts behave when they hit real computation — particularly in areas like discrete modeling, numerical methods, and differential equations. He approaches applied math problems by building up from the underlying structure, making sure students see why a technique works before applying it to a new context. Rated 5.0 by students.

Finance students rarely think of their coursework as applied math, but Shankhadip's BS in Finance means he's worked extensively with the quantitative side — time-value-of-money models, probability-weighted forecasting, and optimization under constraints. He also teaches Python, SQL, and statistics, so when an applied math assignment requires translating a mathematical model into code or interpreting real-world data, he can connect those pieces fluently.

Pre-med coursework in behavioral neuroscience involves more applied math than most people realize — Franshesca regularly works with statistical models, dose-response curves, and quantitative data analysis that require translating biological questions into mathematical frameworks. That science-heavy background gives her a practical angle on topics like modeling and probability, especially for students who need to see why the math matters before the formulas start to click. Rated 4.8 by students.

What makes Michael effective in applied math is that he's simultaneously immersed in the pure math theory — through his BS in Mathematics — and the science applications where that math gets deployed, given his parallel teaching across physics, chemistry, and biology. When a student needs to model a biological growth curve or set up a physics-based differential equation, he can explain both the mathematical machinery and the scientific context it's serving. Rated 4.9 by students.

I graduated from the University of North Carolina at Chapel Hill with a Bachelor of Science in Physics. I work with students in a variety of subjects, including Math, Physics, and Chemistry. I have experience working with students on the Autism spectrum and with ADHD.

I am listening to and learning about him or her as an individual. I can also discover what motivates the student during this conversation and plan for how to frame future tutoring sessions in terms of what the student already knows and enjoys.

As a former IB student, my love for knowledge runs deep. I majored in Biomedical Engineering at the University of Alabama at Birmingham, graduating in December 2015 with honors (BME honors and Science and Technology Honors program) and going on to earn a Master's degree in Management Information Systems from the same university in 2019. I am currently pursuing a Master of Engineering in Artificial Intelligence and Machine Learning from the University of Illinois at Chicago and hope to start a career in data science and analytics. I greatly enjoy helping others learn and tutored throughout high school and college; I love seeing the light flash on in a student's eyes when they finally understand something they had been struggling with or when they easily solve a problem. I have tutored both young children as well as high school and college students, in just about every subject. I particularly enjoy language tutoring; I am fluent in Arabic and French and I love literature and poetry. I also love to tutor math and science subjects. My tutoring style is very much student-first; I listen and observe to determine the best approach for each individual student and what methods or strategies will be most helpful for you specifically. I am not averse to making a fool of myself with song or dance if it will help! I enjoy what I do and I want the students I work with to enjoy learning as well.

Computer science at UT Austin means Shvetan regularly turns math into working solutions — algorithm design, for instance, relies on the same discrete modeling and optimization logic that applied math courses test. He's especially useful for students whose applied math work overlaps with computational problem-solving, since he can connect a concept like recurrence relations or graph theory to the code that brings it to life.

Applied math lives at the intersection of theory and real-world modeling — optimization problems, differential equations, numerical methods. Sameeullah's economics degree from UT Austin, paired with minors in both mathematics and electrical engineering, means he's spent years using mathematical tools to solve problems in signal processing, economic forecasting, and data analysis. That cross-disciplinary fluency lets him show students not just how techniques work but where they actually get used.

Leo's mathematical engineering degree means he built models, ran simulations, and solved optimization problems long before he started teaching — and his Master's in Education Mathematics means he knows how to make those techniques land for students who are encountering them for the first time. He's especially strong at bridging the gap between pure math theory and the messy, real-world formulations that applied math courses throw at students, like setting up a differential equation from a physical scenario or choosing the right numerical method for a given problem. Rated 4.9 by students.

Biology might seem like an odd launchpad for applied math, but Michelle's work in biostatistics, bioinformatics, and quantitative reasoning means she's been building and interpreting mathematical models of biological systems — population dynamics, enzyme kinetics, epidemiological curves — where getting the math right has real consequences. That science-first perspective is especially useful for students who need to see why a differential equation or a probability distribution matters before they can commit to learning how it works. Rated 4.9 by students.

I am currently a Senior with a math major at Loyola University Chicago and have been accepted to their masters program. I also like playing tennis, whiffle ball, and writing poetry.

Mechanical engineering is applied mathematics in action — optimization, differential equations, numerical methods, and linear algebra all show up daily. Noah draws on that engineering background to teach applied math concepts through real-world modeling problems, making abstract techniques feel purposeful. Rated 5.0 by students.