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.

Earning a computer science minor at Penn meant Cody went deep on data structures, algorithms, and programming logic alongside students in one of the country's top CS programs. His cognitive science major adds an unusual edge: he understands how people learn to think computationally, which makes him effective at explaining recursion, sorting algorithms, or Big-O analysis in ways that actually stick.

Recursion, data structures, algorithmic complexity — these topics trip up students who try to memorize patterns without understanding why they work. Kevin holds both a bachelor's and master's in computer science from NYU, and he's the kind of tutor who will explain a concept three different ways until the logic genuinely lands. Rated 4.8 by students.

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.

David is a Computer Science major at UCLA's Engineering School with hands-on industry experience from a software engineering internship at Adobe. He tackles core CS topics — data structures, algorithm analysis, recursion, and computational complexity — by tying abstract ideas back to real implementation decisions. Rated 4.8 by students.

Studying computer science alongside finance and statistics at NYU, Eric sees programming not as an isolated skill but as a tool for solving real quantitative problems — from building data pipelines to automating financial models. He unpacks core concepts like data structures, algorithmic complexity, and object-oriented design by tying them to practical applications students can visualize.

From data structures and algorithms to Big-O analysis, Aiden tackles the core CS concepts that show up in both AP Computer Science and introductory college courses. He's currently a computer science major at SUNY Binghamton, so the material is fresh — he can connect classroom theory to the actual coding assignments students are working through.

A computer science major at Columbia, Brendon tackles core topics like data structures, algorithms, and object-oriented design by connecting abstract concepts to concrete problem-solving steps. His statistics background adds an extra dimension when students encounter topics like computational complexity analysis or probability-driven algorithms. Rated 5.0 by students.

I'm a professional software engineer at a top tech company in New York City. I have a strong passion for software development, most notably in the areas of full-stack web development, iOS development, Artificial intelligence, large scale distributed systems, and micro services.

Debugging a program teaches more about computer science than writing one that works on the first try. Ankit's approach to CS leans heavily on problem decomposition: breaking a complex task into smaller functions, tracing logic flow, and understanding data structures like arrays, linked lists, and trees at a conceptual level before coding them. His analytical training in chemistry translates directly to the systematic thinking CS demands.

Between her neural engineering research and coursework at Barnard, Meghna uses programming as a practical tool — not just an academic exercise. She teaches core concepts like loops, conditionals, data structures, and algorithmic thinking by connecting them to real problems, so students understand why code works the way it does.

Kirollos is pursuing dual degrees in Computer Science and Electrical Engineering at NYU, which means he lives at the intersection of software and hardware every day. He breaks down core concepts like data structures, algorithm complexity, and object-oriented design by connecting them to real systems he's built in both Java and C++.