Award-Winning Computer Science Tutors
serving York, 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.

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.

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.

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!

Eric treats coding problems the same way he treats logical puzzles — by breaking them apart, finding the pattern, and building a solution step by step. As a CS major at Washington University in St. Louis, he's deep in Java and JavaScript right now, which means he can walk students through everything from writing their first function to structuring a full object-oriented program. His approach emphasizes learning to think through problems algorithmically before jumping to syntax.

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.

Learning to code is really learning to decompose problems — figuring out what a program needs to do before writing a single line. Nat is double-majoring in computer science at Vanderbilt and unpacks core topics like loops, conditionals, data structures, and algorithm design in ways that build genuine understanding. Whether a student is writing their first Python script or debugging recursive functions, he connects each concept to the logic behind it.

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.

Between his AP Computer Science 5 and his engineering coursework at Vanderbilt, William has written code across contexts — from introductory Java to computational modeling in his chemical engineering classes. He breaks down abstract concepts like recursion, data structures, and algorithm efficiency by walking through concrete examples line by line. Students who can follow the logic but freeze when writing code from a blank screen tend to gain traction quickly with his approach.

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.

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.

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.

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.

Holding both a B.S. in Computer Science from the University of Kentucky and a game development master's in progress at SCAD, Evan covers the full stack of CS fundamentals: data structures, algorithm analysis, object-oriented design, and software architecture. He connects abstract concepts like Big-O complexity or recursion to concrete implementations in C, C++, and Java so the theory actually sticks.

Philosophy trained Calin to build rigorous logical arguments; his math and CS degrees taught him to express that logic in C++ and JavaScript. That combination means he teaches programming concepts — from algorithm design to data structure selection — as exercises in precise reasoning, where every line of code follows from a clear chain of thought. 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.