Award-Winning Computer Science Tutors
serving Lancaster, PA

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.

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 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.

I am a graduate from Rochester Institute of Technology with a master's in Game Design and Development. My passions lie in everything related to games and mathematics. In the past, I have tutored various subjects in mathematics throughout high school and college, including but not limited to Algebra, Algebra II, Trigonometry, Calculus, Discrete Mathematics, Mathematics of Graphical Simulation, and Linear Algebra. As for technology, I am more than happy to reach out for help in Web Development (HTML, CSS, Javascript) or C# programming. I believe that every person can learn any topic. While every individual has different tastes, strengths, and weaknesses, there is no such thing as an "incapability" to know a subject. Education often possesses a guise of anti-fun, but I can promise you that all topics can be engaging, and I am willing to show you how engaging mathematics and technology can be. As a Game Designer, I have a deep interest in both playing games and making games. This includes games of all kinds: video games, board games, tabletop role-playing games, trading card games, miniatures, and even some sports like tennis or ping pong. Games act as a fantastic teaching tool. They teach by design without users recognizing. It is always a satisfying moment when somebody says "I learned that word from Magic" or "D&D taught me that." Remember: you can succeed. If something is important to you, then it's always worthwhile.

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!

Elizabeth's computer science degree and professional software development career mean she teaches CS concepts the way they're actually used — not just as textbook definitions. Whether it's understanding recursion, tracing through data structures, or reasoning about algorithmic efficiency, she connects abstract ideas to real code she's written on the job.

From sorting algorithms and Big-O analysis to data structures like linked lists and binary trees, Rhamy covers the foundational CS concepts that show up in coursework and technical interviews alike. His computer engineering degree at Vanderbilt, paired with experience in multiple languages, lets him explain abstract ideas through concrete code. Rated 5.0 by students.

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.

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.

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.

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.

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.

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.

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.

Ryan is a computer science major at Cornell, which means he's actively working through the same core curriculum — algorithms, data structures, computational complexity — that college CS students encounter. He explains concepts like recursion, Big-O analysis, and graph traversal by tracing through concrete examples rather than relying on abstract definitions. Rated 4.8 across his sessions.

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.