Award-Winning Computer Science Tutors
serving Seattle, WA

Applied mathematics at UCLA means Maria writes code constantly — from implementing numerical methods to building optimization algorithms for economics models. She brings that practical, problem-solving mindset to computer science topics like data structures, algorithm analysis, and debugging logic errors in real programs.

As a dedicated tutor with over 2 years of experience, I am passionate about fostering a supportive learning environment where students can thrive in subjects like Algebra, Business Analytics, and Data Science. Currently pursuing my Bachelor's in Informatics at the University of Washington, I incorporate real-world applications into my teaching to engage students and enhance their understanding of complex concepts. My approach emphasizes personalized learning, encouraging students to ask questions and develop critical thinking skills. I find great joy in witnessing my students' growth and success, and I strive to instill a love for learning that extends beyond the classroom.

Intensely curious, I am interested in STEM subjects and the liberal arts. I tutor to help you reach your educational goals and because it's immensely gratifying to see my students succeed.

Studying Computer Science at the University of Washington means Antonia is immersed in algorithms, data structures, and computational thinking every day. She unpacks topics like sorting algorithms, Big-O analysis, and abstraction by connecting them to practical problems students can visualize. Her dual-language tutoring background also makes her particularly skilled at explaining technical jargon in plain, accessible terms.

I'm a tutor who loves the moment when a concept finally clickswhether that's solving a calculus problem or debugging a first Python script. With experience teaching math from elementary to college level and professional training in full-stack development, I help students build confidence alongside competence in both math and programming. At Mathnasium, I worked with groups of 4-6 students at different levels, learning to adapt quickly to different learning styles. I bring that same flexibility to teaching HTML, CSS, JavaScript, Python, and other programming languages. I don't just give answersI guide students to discover solutions themselves because that's what sticks. Whether it's explaining why a for-loop isn't working, walking through derivatives, preparing for the SAT, or building a first web page, I focus on understanding over memorization. I'm patient but direct, and I adjust my approach based on what's actually working for each student. My goal is simple: make learning feel achievable, not intimidating, and give students tools they can use long after our sessions end.

A Stanford MS in Computer Science means David can teach everything from data structures and algorithms to object-oriented design with the depth that comes from building real systems — not just reading about them. He spent a summer teaching web and app development to high school students in Palestine, so he knows how to make abstract CS concepts click through hands-on projects.

Ritesh's applied physics program at Cornell involves significant programming, from numerical simulations to data analysis, giving him hands-on fluency with core computer science concepts like algorithm design, data structures, and debugging logic. He unpacks topics such as recursion, sorting algorithms, and object-oriented principles by tying them to concrete problems rather than abstract definitions.

From data structures and algorithms to computational complexity, Michelle covers the core CS curriculum with the depth you'd expect from a Duke CS graduate heading into a PhD at Michigan. She's especially strong at explaining abstract concepts like recursion and graph traversal by connecting them to concrete, visual examples that make the logic intuitive.

Engineering science at Vanderbilt means Ethan writes code to solve real problems — simulations, data analysis, algorithm design — not just textbook exercises. He breaks down core concepts like recursion, data structures, and object-oriented design by connecting them to projects that actually do something interesting.

Biomedical engineering at Rice requires heavy computational coursework, so Theresa has tackled core computer science concepts — from object-oriented programming and data structures to algorithm complexity — in the context of solving real problems. She explains abstract ideas like recursion and sorting algorithms by connecting them to concrete examples rather than letting students drown in theory. Rated 5.0 by students.

From data structures and algorithm analysis to the fundamentals of how operating systems and networks function, Nicholas covers computer science with the depth his Penn State CS degree provided. He's especially strong at explaining recursion, sorting algorithms, and Big-O notation — the concepts that separate students who can code from students who truly understand computation. Rated 5.0 by students.

Trained in computer science at UT Austin and currently pursuing a PhD that blends computational methods with social science, David brings both theoretical depth and applied versatility to CS instruction. He digs into core topics like algorithm analysis, data structures, and computational complexity, connecting them to the kind of real-world problem-solving that makes the discipline click.

A computer science degree paired with coursework in theoretical mathematics gives Lance an unusually strong grasp of the abstractions underneath the code — automata theory, algorithm complexity, data structures, and computational logic. He spent multiple semesters as a TA and independent instructor for CS courses, so he's seen firsthand which concepts trip students up and how to reframe them. Whether the topic is graph traversal or NP-completeness, he breaks it into pieces that actually make sense.

Between a master's in Computer Science, an active PhD program, and a full-time software development career, Daniel lives this subject from multiple angles — theoretical and applied. He unpacks everything from algorithmic complexity and data structures to systems-level thinking, tailoring the depth to wherever a student currently sits in their CS journey.

Sakibul's graduate work at Rice sits at the intersection of computer science and applied mathematics, which means he tackles programming concepts — loops, recursion, data structures — with the analytical rigor of a mathematician. He breaks down abstract ideas like algorithmic complexity into concrete, step-by-step reasoning that clicks for students encountering CS for the first time.

From sorting algorithms to recursion to object-oriented architecture, computer science rewards the ability to think in layers of abstraction. Joel is pursuing CS at Cornell alongside physics, which means he approaches programming problems with both mathematical rigor and practical debugging instincts. He's comfortable across Python and Java and adapts to whatever language a student's course requires.

Pursuing both computer science and data science at NYU's Courant Institute with a cybersecurity minor, Diego lives in this material daily — from algorithms and data structures to networking and systems-level thinking. He breaks abstract CS concepts into smaller, buildable pieces so students can trace the logic themselves rather than just copying solutions.