Justin's PhD research in computational mathematics meant writing code daily — building simulations, implementing algorithms, and debugging in MATLAB and other languages. He teaches computer science concepts like data structures, recursion, and algorithmic complexity by connecting them to real computational problems rather than treating them as abstract definitions to memorize.

Isabella TA'd multiple computer science courses at MIT, so she's seen exactly where students get stuck — whether it's tracing recursive calls, understanding how data structures like linked lists and trees actually work in memory, or debugging logic errors in their code. She explains the underlying concepts so that writing correct programs becomes intuitive rather than trial-and-error. Rated 5.0 by students.

Earning a certificate in Statistics and Machine Learning at Princeton gave Julie hands-on experience with core computer science concepts — algorithm design, data structures, and computational complexity. She approaches CS the way she approaches philosophy: by asking students to reason through *why* a solution works, not just whether it compiles.

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.

Building AI systems and low-level software at Stanford — in both Python and C++ — Kevin knows where the theoretical meets the practical in computer science. His biocomputation specialization means he can explain not just how to implement an algorithm, but why certain computational approaches work better for different problem domains. Rated 5.0 by students.

Margaret studies Computer Science at Stanford alongside Political Science, giving her a broad perspective on how computational thinking applies beyond just writing code. She breaks down core topics like data structures, algorithms, and recursion by connecting each one to real problems students can visualize. Rated 4.8 by her students.

Most CS tutors come from pure software backgrounds — Clive's path runs through economics at Brown, where he picked up Java, Python, JavaScript, SQL, and HTML as tools for data analysis and building real projects rather than just completing problem sets. That applied angle makes him especially effective at teaching programming fundamentals and web technologies to students who learn better when code solves a tangible problem.

Benjamin's finance and economics training at Notre Dame means he learned to code as a problem-solving tool — building models, analyzing datasets, and automating calculations — rather than through a traditional CS curriculum. That pragmatic entry point makes him effective at teaching programming logic and computational thinking to students who want to understand how code actually gets used in business and quantitative fields. Rated 5.0 by students.

Between his coursework at Rice and his background in algorithms, Daniel tackles computer science from both the practical and theoretical sides — writing clean code and understanding why one sorting algorithm outperforms another for a given dataset. He's especially strong at breaking down recursion, data structures, and algorithmic complexity into steps that build logically on each other.

Michael earned his B.S. in Computer Science from UCLA, where he dug into everything from data structures and algorithms to software design principles. He breaks down abstract concepts like recursion, Big-O analysis, and object-oriented programming into concrete, step-by-step logic that clicks. He also teaches JavaScript, giving him a practical edge when students need to connect theory to actual code.

Florence doesn't just study computer science at Duke — she teaches it, having served as a TA for Intro to Databases and Computer Network Architecture while also interning in software development at IBM. That combination of academic depth and industry experience means she can explain everything from relational algebra to TCP/IP networking with concrete, real-world context. Rated 5.0 by students.

Programming starts making sense when you stop memorizing syntax and start thinking about what the computer is actually doing step by step. June's electrical engineering background at Brown gives her insight into both the hardware and software sides — she can explain why an algorithm is efficient, not just how to write it. From loops and conditionals to data structures and recursion, she connects each concept to real projects she's built in robotics and hackathons.