Award-Winning Computer Science Tutors
serving Hartford, CT

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.

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.

From automata theory and computational complexity to practical algorithm design, Firas covers computer science as both a theoretical discipline and a hands-on craft. His Ph.D. research at the intersection of machine learning and big data means he can connect abstract CS concepts — graph traversals, runtime analysis, NP-completeness — to the systems that actually use them. Rated 5.0 by students.

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.

I'm trying to work on personal projects. I really enjoy snowboarding, and have been doing that since the third grade. I also enjoy playing sports and video games.

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.

A computer science degree paired with coursework in theoretical mathematics gives Lance an unusually strong grasp of the abstractions underneath the code — automata theory, algorithm complexity, data structures, and computational logic. He spent multiple semesters as a TA and independent instructor for CS courses, so he's seen firsthand which concepts trip students up and how to reframe them. Whether the topic is graph traversal or NP-completeness, he breaks it into pieces that actually make sense.

Trained in computer science at UT Austin and currently pursuing a PhD that blends computational methods with social science, David brings both theoretical depth and applied versatility to CS instruction. He digs into core topics like algorithm analysis, data structures, and computational complexity, connecting them to the kind of real-world problem-solving that makes the discipline click.

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.

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.

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.

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.