Award-Winning Computer Science Tutors
serving Madison, WI

I am a Senior undergraduate student at the University of Wisconsin-Madison. I major in Computer Science and Data Science, while minoring in Mathematics and Leadership. I have experience teaching complex computer science concepts to others and specialize in teaching Python and Java. My experience in this topic also extends to making websites, applications, and AI models for personal use. Therefore, I am confident in teaching hard-to-understand cs topics in an easily consumable way, As for standardized tests, I have experience getting a 34 on the ACT (with and without super score) and a 1550 on the SAT (with super score). For both tests I have took them multiple times for the score (especially the ACT, which I started with a test score of 28 during my Sophomore year), meaning that I understand the struggles of students trying to increase their standardized test scores.

Studying Computer Science alongside Math/Stats at Carleton College, Thomas lives at the intersection of algorithms, data structures, and mathematical reasoning. He digs into topics like recursion, sorting algorithms, and object-oriented design by building understanding from first principles rather than rote code memorization.

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.

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.

Josh approaches computer science by breaking problems into smaller logical pieces before writing a single line of code. His actuarial and business training sharpened his ability to think algorithmically — translating real-world problems into data structures, loops, and conditional logic. Whether students are debugging their first program or designing a sorting algorithm, he emphasizes understanding *why* code works, not just copying syntax.

From sorting algorithms and Big-O analysis to database design and operating system concepts, computer science covers enormous ground. Wei's career as a systems design engineer and automation technician means he's applied these ideas professionally — not just studied them — which lets him connect theory to tangible examples. He's particularly sharp on the intersection of hardware and software, where many CS tutors have blind spots.

A math background gives Taylor an edge when teaching computer science, because she treats code the way she treats a proof: every line should have a reason. She digs into core concepts like control flow, data structures, and algorithmic thinking so students can debug logically instead of guessing.

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.

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.

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.

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.

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 data representation and Boolean logic to algorithm design and computational thinking, Jack covers the full spread of computer science fundamentals as a CS major at the University of Chicago. He's the person his peers turn to when they're stuck on a problem set, which means he's constantly practicing the skill of explaining abstract concepts — recursion, runtime analysis, data abstraction — in plain language.

Allison's CS degree from Dartmouth means she's worked through the full arc — from writing first programs to tackling data structures, algorithms, and computational theory. She unpacks abstract concepts like recursion and Big-O analysis by walking through concrete code examples, making the logic visible before the notation takes over.

From basic programming constructs all the way up to computational analysis and finite automata, Andrew has tutored the full range of computer science topics at both the high school and college level. His CS degree gives him the depth to explain not just how an algorithm works but why its time complexity matters. Rated 5.0 by students.

Pursuing a computer engineering degree at Georgia Tech means Yash lives in the overlap between hardware and software — algorithms, data structures, computational logic, and systems design are daily coursework. He's especially strong at explaining foundational concepts like Big-O analysis, sorting algorithms, and binary representation in ways that click for students seeing them for the first time.

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.

Alston is pursuing a Computer Science degree at the University of Virginia, which means he's actively working through data structures, algorithms, and object-oriented design — not recalling them from years ago. He breaks down concepts like recursion and sorting algorithms by walking through the logic step by step, letting students trace the code themselves until the pattern clicks.

Benjamin's finance and economics training at Notre Dame means he learned to code as a problem-solving tool — building models, analyzing datasets, and automating calculations — rather than through a traditional CS curriculum. That pragmatic entry point makes him effective at teaching programming logic and computational thinking to students who want to understand how code actually gets used in business and quantitative fields. Rated 5.0 by students.