Award-Winning Computer Science Tutors
serving Fort Wayne, IN

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.

Having TA'd courses in C Programming, Digital Systems Design, and iOS development at Purdue, Akio has debugged student code across enough languages and paradigms to spot the conceptual gaps that cause the most frustration — whether it's misunderstanding pointer arithmetic or struggling with event-driven programming patterns. His computer engineering degree means he teaches software concepts with a hardware-aware perspective, connecting ideas like memory allocation and data representation to what's physically happening on the chip. Rated 4.8 by students.

Christopher approaches computer science as structured problem-solving, breaking down concepts like algorithms, data types, and control flow into digestible steps before ever touching a keyboard. His business analytics coursework at Indiana University keeps him actively coding and thinking computationally, which translates into clear, practical explanations for students just getting started.

Having a triple major that includes both Computer Science and Mathematics, Darren connects programming concepts to the formal logic and problem-solving frameworks that make them click. Whether it's data structures, algorithm efficiency, or debugging stubborn code, he walks through each problem step by step until the reasoning — not just the syntax — makes sense.

I am an incoming college sophomoore studying Aerospace Engineering at the Georgia Institute of Technology. I was one of the top scholars at Carmel High School in Indiana and have various experiences teaching and tutoring through leading the Carmel High School Science Olympiad team to Nationals for two years in a row. Many of my friends consistently come to me for help with various subjects because of my willingness to help and teach in a way that makes sense to everyone. My specialities are physics, chemistry, math, and economics but I am comfortable tutoring any STEM subject, the SAT, and the ACT. Some of my accomplishments include a perfect 36 ACT score, 800 on the Physics, Math Level 2, and Chemistry SAT subject tests, and a 5 on all the physics, Calculus BC, Chemistry, Macroeconomics, Computer Science, etc. AP exams. When teaching, I focus on making sure that not only do students understand different concepts, but also that students understand why and practical applications of the different concepts. As a recent high school graduate, I have a deep understanding of how high-schoolers think, what is covered in various courses now, and can connect with my students a lot more. Additionally, as an undergraduate teaching assistant at the College of Computing at Georgia Tech, I taught weekly recitation sections of 30 students and held weekly office hours. Therefore, I have lots of experience teaching and working with students.

I am currently a sophomore at the University of Notre Dame. I am majoring in Applied Mathematics and Statistics.

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.

Margaret studies Computer Science at Stanford alongside Political Science, giving her a broad perspective on how computational thinking applies beyond just writing code. She breaks down core topics like data structures, algorithms, and recursion by connecting each one to real problems students can visualize. Rated 4.8 by her students.

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.

A Stanford MS in Computer Science means David can teach everything from data structures and algorithms to object-oriented design with the depth that comes from building real systems — not just reading about them. He spent a summer teaching web and app development to high school students in Palestine, so he knows how to make abstract CS concepts click through hands-on projects.

Between a master's in Computer Science, an active PhD program, and a full-time software development career, Daniel lives this subject from multiple angles — theoretical and applied. He unpacks everything from algorithmic complexity and data structures to systems-level thinking, tailoring the depth to wherever a student currently sits in their CS journey.

Julia is a computational science major at Stanford who writes code daily and understands the gap between knowing syntax and thinking like a programmer. She digs into core concepts like recursion, data structures, and algorithm design by walking through problems step by step until the logic becomes intuitive. Students learning Python, Java, or tackling their first real debugging challenge get someone who can explain the *why* behind every line.

Holding both a B.S. in Computer Science from the University of Kentucky and a game development master's in progress at SCAD, Evan covers the full stack of CS fundamentals: data structures, algorithm analysis, object-oriented design, and software architecture. He connects abstract concepts like Big-O complexity or recursion to concrete implementations in C, C++, and Java so the theory actually sticks.

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.

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.

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.

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.

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.