Award-Winning Computer Science Tutors
serving Philadelphia, PA

Pursuing a CS master's at Penn while TAing discrete math means Keenan lives in both the theoretical and practical sides of computer science every day. He unpacks core topics like algorithm complexity, data structure tradeoffs, and computational logic in a way that connects abstract ideas to real code. Rated 5.0 across all sessions.

Whether a student is writing their first loop or debugging a recursive function, Kevin connects new programming concepts to the logic underneath them so the syntax stops feeling arbitrary. His data science concentration means he's comfortable across languages and paradigms, and his experience teaching first-time coders in underserved communities sharpened his ability to explain abstract ideas without jargon.

Studying computer science at the University of Pittsburgh while working as a software engineer gives Timothy a dual perspective — he knows the theory behind data structures and algorithms and also knows which concepts actually matter on the job. He breaks down topics like recursion, Big-O analysis, and object-oriented design by tying them to problems students can visualize and build.

Studying AI at Penn State gave Lucas a deep understanding of computer science from the ground up — data structures, algorithms, object-oriented design, and how they all connect in real applications. He's taught students brand-new to coding as well as those debugging complex projects, and he tailors each session to the language and concepts at hand. Rated 4.9 by students.

From sorting algorithms to data structures to the big-picture question of how computers actually process information, Steven covers computer science at both the high school and college level. He programs in Java and C#, which means he can explain concepts like recursion or object-oriented design in whichever language a student's course uses. Rated 5.0 by students across his subjects.

Jared's CS background spans both the theoretical and the applied — his master's in computer science covered areas like machine learning and NLP, while his professional work as a software engineer means he writes production code in Java, C++, and Python daily. That range lets him teach everything from foundational data structures and algorithms to more advanced topics like relational database design with the perspective of someone who's actually built systems around these concepts. Rated 5.0 by students.

I am a current sophomore at Cornell University pursuing a B.S. in Biomedical Engineering. I have done extensive coursework in biology, physics, chemistry, math, and lab sciences. I love applying engineering problem-solving skills to the biological sciences. For the past year, I have been a teaching assistant for introductory biology classes. In my free time, I participate in cancer immunotherapy research which focuses on melanoma.

I am a rising sophomore at Harvard College, currently on leave for the semester. I am a B.A. candidate in mathematics and physics, and I have both professional and academic experience in computer science as well.

A strong computer science education isn't just about writing code — it's about thinking algorithmically, understanding data structures, and knowing when a brute-force solution won't scale. Sarah is pursuing her CS degree at Saint Vincent College and brings hands-on experience with languages like C++ and Python to break down topics from recursion to sorting algorithms.

Hello, my name is Paul, and I am passionate about helping students in the areas of computer science and math and making sure that no question goes unanswered! I can guarantee a safe environment where hard work and creativity will be the basis for success! Let's have some fun solving problems!

Julia is a computational science major at Stanford who writes code daily and understands the gap between knowing syntax and thinking like a programmer. She digs into core concepts like recursion, data structures, and algorithm design by walking through problems step by step until the logic becomes intuitive. Students learning Python, Java, or tackling their first real debugging challenge get someone who can explain the *why* behind every line.

Studying both chemical engineering and computer science at Cornell gives Jonathan an unusual angle on programming — he's constantly writing code to solve quantitative, real-world problems rather than just completing standalone assignments. That dual perspective makes him especially effective at teaching algorithmic thinking and Java or Python fundamentals, since he can show students how CS concepts like iteration and data manipulation actually get applied in technical fields outside of software development.

Three Bachelor of Science degrees — including Neuroscience — meant Anna was writing code long before she started teaching it, using Java, Python, and MATLAB to analyze data and build computational models across disciplines. That cross-field experience shapes how she teaches CS fundamentals: students don't just learn syntax, they learn to think about what a program needs to do before structuring it in any particular language. Rated 5.0 by students.

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.

Madeline's physics PhD work at Carnegie Mellon means she writes code daily — Python, Java, MATLAB, and Mathematica — to model complex systems and crunch data, which is a very different entry point into computer science than a pure software track. That scientific computing background makes her especially effective at teaching programming logic, debugging strategies, and algorithmic thinking to students who need CS skills for STEM applications rather than just app development.

Studying computer science since before college and now pursuing a master's at UMass Amherst, Milo covers the full stack — from data structures and algorithms to systems-level concepts like memory management and concurrency. He spent three years in his university's tutoring center breaking down topics like recursion, sorting complexity, and graph traversal for students at every level.

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.

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.

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.

From data structures and algorithms to computational complexity, Victoria covers the core CS concepts that show up in both coursework and technical interviews. Studying computer science and math simultaneously at WashU gave her a knack for explaining the mathematical reasoning behind topics like recursion, sorting efficiency, and graph traversal. She holds a 5.0 rating from 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.

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.