Three teaching assistant roles at Duke — spanning databases, electromagnetics, and network architecture — have given Florence a front-row view of where college CS students get stuck. She tackles topics like query optimization, data structures, and systems-level networking with the practical fluency of someone who's also shipped code at IBM and handled cybersecurity analysis at TIAA. Rated 5.0 by students.

College CS ramps up quickly once you hit algorithm design, time complexity, and data structure implementation. Rhamy's Vanderbilt computer engineering coursework means he's recently worked through these exact problem sets, and he explains tricky topics like graph traversal and dynamic programming by tracing through code line by line.

Upper-level CS courses demand more than just writing code that runs — they require understanding why an algorithm scales, how memory allocation affects performance, and what makes one data structure better than another for a given problem. Sakibul's computational mathematics research at Rice means he lives in this territory daily, connecting theory to implementation across languages and paradigms.

College CS courses ramp up fast once you hit topics like graph algorithms, dynamic programming, and runtime analysis. Brice is living that curriculum right now at MIT, which means he knows exactly which concepts trip people up in courses like data structures or discrete math. He approaches each session by tracing through problems step by step until the logic clicks.

Studying both biomedical and chemical engineering at Vanderbilt means William writes code that interfaces with real systems — from data analysis pipelines to simulation models. He brings that applied perspective to college CS topics like data structures, object-oriented design, and algorithm complexity, making abstract concepts click through concrete engineering examples.

I am graduated from Penn State University in Industrial Engineering in 2017. I've tutored ever since I was in high school, and I love helping people! I like to help my students understand math (and other topics) instead of just doing it blindly. My goal is to help my students improve their math (and other topics) and build skills that will help them find learning easier in the future! Fun fact, I used to work for Disney and I like to salsa dance!

Currently deep in his own CS coursework at Washington University in St. Louis, Eric tackles college-level topics — algorithm analysis, data structures like hash maps and binary trees, and object-oriented design patterns — with the perspective of someone actively working through the same material. That proximity to the content means he knows exactly which concepts tend to be poorly explained in lectures and where students typically lose the thread. He's especially strong at connecting theoretical Big-O analysis to practical coding decisions.

College CS ramps up fast — one week it's Big-O analysis, the next it's graph traversal or dynamic programming. Jonathan is working through that same curriculum at Cornell right now, which means he knows exactly where the tricky conceptual jumps are and how to explain them before a student gets lost. He pairs his engineering mindset with hands-on coding to make theory stick.

Upper-division CS courses ramp up fast, whether it's operating systems, databases, or algorithms. David studied computer science at UT Austin and now applies computational methods in his doctoral research at Columbia, so he tackles these topics from both an academic and a practical research perspective. Rated 4.9 by students.

College-level CS courses ramp up fast, and the jump from writing simple programs to implementing linked lists, trees, and graph algorithms can feel brutal. Theresa's engineering program at Rice put her through rigorous programming coursework in C++ and beyond, and she unpacks topics like memory allocation, Big-O analysis, and object-oriented design with the specificity that college assignments demand.

Three teaching assistant roles at Duke — spanning databases, electromagnetics, and network architecture — have given Florence a front-row view of where college CS students get stuck. She tackles topics like query optimization, data structures, and systems-level networking with the practical fluency of someone who's also shipped code at IBM and handled cybersecurity analysis at TIAA. Rated 5.0 by students.

College CS ramps up quickly once you hit algorithm design, time complexity, and data structure implementation. Rhamy's Vanderbilt computer engineering coursework means he's recently worked through these exact problem sets, and he explains tricky topics like graph traversal and dynamic programming by tracing through code line by line.

Upper-level CS courses demand more than just writing code that runs — they require understanding why an algorithm scales, how memory allocation affects performance, and what makes one data structure better than another for a given problem. Sakibul's computational mathematics research at Rice means he lives in this territory daily, connecting theory to implementation across languages and paradigms.

College CS courses ramp up fast once you hit topics like graph algorithms, dynamic programming, and runtime analysis. Brice is living that curriculum right now at MIT, which means he knows exactly which concepts trip people up in courses like data structures or discrete math. He approaches each session by tracing through problems step by step until the logic clicks.

Studying both biomedical and chemical engineering at Vanderbilt means William writes code that interfaces with real systems — from data analysis pipelines to simulation models. He brings that applied perspective to college CS topics like data structures, object-oriented design, and algorithm complexity, making abstract concepts click through concrete engineering examples.

I am graduated from Penn State University in Industrial Engineering in 2017. I've tutored ever since I was in high school, and I love helping people! I like to help my students understand math (and other topics) instead of just doing it blindly. My goal is to help my students improve their math (and other topics) and build skills that will help them find learning easier in the future! Fun fact, I used to work for Disney and I like to salsa dance!

Currently deep in his own CS coursework at Washington University in St. Louis, Eric tackles college-level topics — algorithm analysis, data structures like hash maps and binary trees, and object-oriented design patterns — with the perspective of someone actively working through the same material. That proximity to the content means he knows exactly which concepts tend to be poorly explained in lectures and where students typically lose the thread. He's especially strong at connecting theoretical Big-O analysis to practical coding decisions.

College CS ramps up fast — one week it's Big-O analysis, the next it's graph traversal or dynamic programming. Jonathan is working through that same curriculum at Cornell right now, which means he knows exactly where the tricky conceptual jumps are and how to explain them before a student gets lost. He pairs his engineering mindset with hands-on coding to make theory stick.

Upper-division CS courses ramp up fast, whether it's operating systems, databases, or algorithms. David studied computer science at UT Austin and now applies computational methods in his doctoral research at Columbia, so he tackles these topics from both an academic and a practical research perspective. Rated 4.9 by students.

College-level CS courses ramp up fast, and the jump from writing simple programs to implementing linked lists, trees, and graph algorithms can feel brutal. Theresa's engineering program at Rice put her through rigorous programming coursework in C++ and beyond, and she unpacks topics like memory allocation, Big-O analysis, and object-oriented design with the specificity that college assignments demand.