Award-Winning Computer Science Tutors
serving Greensboro, NC

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.

Studying computer science at Duke means Susie is immersed in everything from algorithm analysis to systems design right now, which keeps her explanations current and grounded. She's especially sharp at connecting theoretical ideas like Big-O notation and recursion to the practical code students actually write. Co-founding a tutoring club in high school gave her early practice at making tough material click.

Studying computer science at UNC Chapel Hill means Jack lives in the material — algorithms, data structures, operating systems, and computational theory aren't abstract topics for him but daily coursework. He breaks down concepts like recursion, Big-O analysis, and memory management into intuitive explanations that stick.

Tackling a computer science minor alongside mechanical engineering at Duke, Shaan lives at the intersection of hardware thinking and software logic. He teaches core CS concepts — algorithm complexity, recursion, data structures, and object-oriented design — by connecting abstract ideas to concrete implementations, which is especially useful for students encountering Big-O notation or sorting algorithms for the first time.

Understanding computer science means thinking algorithmically: knowing not just that a sorting algorithm works, but why merge sort outperforms bubble sort at scale and how to analyze that difference with Big-O notation. Shourya codes in Python, C++, and MATLAB, which lets him adapt explanations to whichever language a student's course uses while keeping the focus on the underlying logic of data structures and algorithms.

I graduated from the University of North Carolina at Chapel Hill with a Bachelor of Science in Physics. I work with students in a variety of subjects, including Math, Physics, and Chemistry. I have experience working with students on the Autism spectrum and with ADHD.

I am a graduate student at George Mason University studying Computer Science. I also graduated at Vanderbilt University, with Bachelors Degrees in Computer Science, Mathematics, and Philosophy. When a student does not understand a topic or concept, I believe the best way to teach it is to meet students where they are at, providing individually catered feedback, and exploring the aspects of a student's weaknesses, finding a way to explain the concept in a way that resonates with and makes sense to the student. I am most familiar with tutoring for the SAT, where I have generally increased scores by 120-300 points.

As an engineering student at Brown, Kashish works with programming languages and computational thinking daily — from writing algorithms to debugging code and understanding data structures like arrays, linked lists, and trees. She breaks down abstract CS concepts by connecting them to concrete problems, making topics like recursion or object-oriented design click for students encountering them for the first time. Her analytical precision and 5.0 rating make her a strong fit for introductory through intermediate computer science.

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.

Biomedical engineering at Rice requires heavy computational coursework, so Theresa has tackled core computer science concepts — from object-oriented programming and data structures to algorithm complexity — in the context of solving real problems. She explains abstract ideas like recursion and sorting algorithms by connecting them to concrete examples rather than letting students drown in theory. Rated 5.0 by students.

Jeff tackles computer science by connecting abstract concepts like recursion, data structures, and algorithmic complexity to concrete problem-solving steps students can follow. His science background means he's comfortable with the mathematical logic underlying CS, from Boolean algebra to Big-O analysis.

John transitioned from law into co-founding a software company, which meant teaching himself to think in algorithms, data structures, and system design under real deadlines. He approaches computer science the same way — breaking problems into smaller, solvable pieces before writing a single line of code. That builder's mindset makes debugging and logic design feel less intimidating.

Studying computer science at Cornell, Eric tackles everything from algorithm analysis and Big-O notation to systems-level concepts like memory management and recursion. He breaks down problems the way a CS student actually encounters them — whiteboarding a solution, tracing through edge cases, then translating logic into clean code. His 5.0 rating speaks to how clearly that approach lands.

From recursion and sorting algorithms to Big-O analysis and object-oriented design, Ethan covers core computer science concepts with the rigor of someone studying it as a second major alongside aerospace engineering. He emphasizes understanding why an algorithm works — tracing through execution step by step — so students can adapt their thinking to new problems on exams and in projects.

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.

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.

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.

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.

As a computer science major at Boston University, Irene is immersed in the subject daily — from data structures and algorithms to object-oriented design. She breaks down abstract programming concepts into concrete steps, walking through how to trace logic, debug code, and think like a developer rather than just memorize syntax.