Award-Winning Computer Science Tutors
serving Wilmington, 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.

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.

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.

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.

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.

Corrina's mechanical engineering degree required extensive programming coursework, and she now teaches core computer science concepts — data structures, algorithms, Boolean logic, and computational thinking — in a way that makes abstract ideas tangible. She connects each concept to real applications, whether that's sorting algorithms in a search engine or conditionals inside a robot's control loop.

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.

Building AI systems and low-level software at Stanford — in both Python and C++ — Kevin knows where the theoretical meets the practical in computer science. His biocomputation specialization means he can explain not just how to implement an algorithm, but why certain computational approaches work better for different problem domains. Rated 5.0 by students.

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.

Ritesh's applied physics program at Cornell involves significant programming, from numerical simulations to data analysis, giving him hands-on fluency with core computer science concepts like algorithm design, data structures, and debugging logic. He unpacks topics such as recursion, sorting algorithms, and object-oriented principles by tying them to concrete problems rather than abstract definitions.

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.

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.

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.