Software engineering internships at Apple and Microsoft gave Jerry hands-on experience with data structures, algorithms, and system design that most CS undergrads only encounter in textbooks. Now finishing his Computer Science degree at UT Austin, he breaks down complex topics like recursion, object-oriented design, and algorithmic complexity by connecting them to real production codebases he's actually worked in. Rated 4.9 by students.

From data structures and algorithms to computational logic, computer science covers a huge range of ideas that can feel disconnected without a unifying thread. Alicia ties these concepts together through her dual background in CS and architecture, where she routinely uses programming to solve spatial and structural problems. That cross-disciplinary perspective makes abstract topics like recursion or graph traversal feel grounded and purposeful.

Computational engineering at UT Austin sits at the intersection of CS theory and applied problem-solving, which means Atharva doesn't just know algorithms and data structures — he uses them to model real systems in aerospace and physics simulations. He digs into topics like Big-O analysis, sorting algorithms, and recursion by connecting each concept to why it matters in actual software. Rated 5.0 by students.

From data structures and algorithm complexity to Boolean logic and binary representation, Jake covers computer science fundamentals with the rigor of someone who studied them as part of an electrical engineering degree. He's particularly strong at explaining how abstract concepts like recursion or Big-O notation connect to real computational performance. Students rate him 5.0.

Problem-solving is problem-solving — Roozbeh approaches computer science the same way he tackles math, by breaking complex logic into smaller, testable pieces. He walks through fundamentals like control flow, data structures, and algorithmic thinking in a way that builds real debugging instincts.

Cornell's Electrical and Computer Engineering program, combined with a CS minor, gave Alexander deep exposure to everything from algorithms and data structures to digital logic and systems-level thinking. He spent six semesters as a TA for Intro Digital Logic, where he got skilled at breaking abstract computational concepts into concrete, buildable intuition. Rated 5.0 by students.

From sorting algorithms to data structures to recursion, Lloyd approaches computer science as a toolkit for solving real problems rather than an abstract set of rules. His data science coursework at the University of Rochester keeps him fluent in both the theoretical foundations and the practical implementation side, which means he can explain why a hash map outperforms a list — not just how to use one.

Jacqueline writes production code daily as a software engineer and holds a B.S. in Computer Science from UT Austin, so she teaches concepts like recursion, data structures, and algorithm complexity from real-world experience rather than purely academic examples. Whether a student is debugging their first Python script or wrestling with graph traversal, she breaks problems into manageable pieces. Her Human-Computer Interaction minor also means she thinks carefully about how people learn technical material.

From data structures and algorithm complexity to operating systems concepts in Linux, Sourav covers the full CS curriculum with the perspective of someone who studied it formally and then applied it in technology commercialization. He's especially strong on the practical side — debugging code, tracing recursive calls, and understanding why a hash map outperforms a linked list for a given problem.

Sandra earned her CS degree writing code and solving problems — and now she teaches everything from introductory programming logic to more advanced topics like data structures and algorithm design. What sets her apart is how naturally she connects computer science to the quantitative and analytical thinking she uses across her other subjects, so students see CS as a problem-solving framework rather than just syntax to memorize. Rated 5.0 by students.

Bennet is pursuing a computer science degree while simultaneously interning as a working programmer, which means he bridges the gap between academic theory and production-level practice. He unpacks core topics like data structures, algorithm complexity, and recursion by connecting them to real code he's written professionally.

I'm a Computer Science student who's been teaching all my life. From tennis camps for kids to an engineering physics class for college kids, I really enjoy helping someone discover for themselves what they are capable of! Please let me know if there's anything you'd like me to help you with.