Award-Winning Computer Science Tutors
serving Boston, MA

As a computer science major at Boston University, Irene is immersed in the subject daily — from data structures and algorithms to object-oriented design. She breaks down abstract programming concepts into concrete steps, walking through how to trace logic, debug code, and think like a developer rather than just memorize syntax.

As a computer science major heading into her senior year, Allison is deep in the material students are encountering for the first time — data structures, algorithm analysis, object-oriented design. She explains concepts like recursion and Big-O notation by building up from simple examples in Python before introducing the formal definitions, which makes abstract ideas click faster.

Christine is a third-year CS student at Northeastern who lives this material daily — from data structures and algorithms to object-oriented design and recursion. She walks through problems by connecting the abstract logic to concrete code, so concepts like linked lists or graph traversals click rather than feel like rote memorization. Rated 4.9 by students.

I am passionate about helping students succeed in computer science by making complex concepts clear and engaging. With over seven years of teaching experience in higher education, I have taught a range of courses in programming and information systems. I hold a Master's degree in Computer Science from the University of Trento, Italy, and a Bachelor's degree in Information Systems from Addis Ababa University, Ethiopia, and I am currently pursuing my Ph.D. in Computer Science. My favorite subjects to tutor are C++, Java, and Systems Analysis and Design because they build strong problem-solving and analytical skills. I focus on creating an interactive, supportive learning environment that helps students gain confidence and apply their knowledge. Outside of academia, I am interested in pursuing roles in the tech industry as a system analyst or software engineer.

Studying computer science at MIT, Brice digs into everything from data structures and algorithms to systems-level thinking with students at any stage. He's tutored over 30 students in the past year alone, tackling topics like recursion, object-oriented design, and algorithmic complexity. Rated 4.9 by students.

Studying computer science since before college and now pursuing a master's at UMass Amherst, Milo covers the full stack — from data structures and algorithms to systems-level concepts like memory management and concurrency. He spent three years in his university's tutoring center breaking down topics like recursion, sorting complexity, and graph traversal for students at every level.

Between Java, Python, and the AP Computer Science A curriculum, William covers the languages and frameworks that most high school and intro-level college students actually encounter. His Northeastern CS program keeps him close to the material — he's working through the same data structures, algorithms, and OOP principles his students are learning, which means he can pinpoint exactly where a concept like recursion or inheritance starts to feel slippery. Rated 5.0 by students.

Allison holds a B.S. in Computer Science from Mount Holyoke College, so she's been through the full gauntlet — from writing first programs to tackling data structures, algorithms, and debugging logic that refuses to cooperate. She teaches coding concepts by building up from how the computer actually processes instructions, which makes abstract ideas like recursion and iteration far more intuitive.

A working software developer with a BS in Computer Science, Andy covers core CS topics — data structures, algorithms, recursion, Big-O analysis — with the perspective of someone who uses them daily in mobile application design. He explains not just how an algorithm works but why you'd choose it over alternatives in a real codebase.

An Electronic Design degree from Hampshire College means Micah lives at the intersection of code and hardware — he's built electronics curricula and understands programming as a tool for making things work in the real world. He tackles core CS concepts like control flow, data structures, and debugging by grounding them in tangible projects rather than abstract exercises.

Studying computer science at Tufts alongside civil engineering gives Adam a dual perspective — he understands both the theoretical foundations like data structures and algorithms and how code gets applied to solve real-world problems. He unpacks concepts like recursion, object-oriented design, and sorting algorithms by building small programs that make abstract ideas concrete. That hands-on approach clicks especially well for students who learn by doing.

Adriano leans into the proof-based, mathematical side of computer science — the discrete math foundations, formal logic, and algorithmic correctness arguments that many introductory courses rush past. As an MIT CS undergrad, he's immersed in that rigor daily and brings it to sessions where students need to understand not just what an algorithm does but how to prove it works.

I'm Cindy! I'm a freshman at Harvard University and graduated from San Mateo High School this past year. I love to teach and have years tutoring students of all ages in a variety of subjects. Feel free to send me a message if you have any questions or would like to schedule a tutoring session!

I'm Connor, a computer science, linguistics, and Japanese major at the University of Massachusetts Amherst. I'm in my third year expecting to graduate in May next year.

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.

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.

From computer architecture and theory to parallel computing and machine learning, Brandon's master's coursework at RIT covers the full stack of computer science concepts. His two years of professional industry experience mean he can connect abstract topics — algorithm complexity, concurrency models, data structures — to how they actually show up in production code. Students rated him 4.9, which tracks with his ability to make dense CS material feel approachable.

Between his coursework at Rice and his background in algorithms, Daniel tackles computer science from both the practical and theoretical sides — writing clean code and understanding why one sorting algorithm outperforms another for a given dataset. He's especially strong at breaking down recursion, data structures, and algorithmic complexity into steps that build logically on each other.

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.

Studying computer science at Cornell, Eric tackles everything from algorithm analysis and Big-O notation to systems-level concepts like memory management and recursion. He breaks down problems the way a CS student actually encounters them — whiteboarding a solution, tracing through edge cases, then translating logic into clean code. His 5.0 rating speaks to how clearly that approach lands.

Three Bachelor of Science degrees — including Neuroscience — meant Anna was writing code long before she started teaching it, using Java, Python, and MATLAB to analyze data and build computational models across disciplines. That cross-field experience shapes how she teaches CS fundamentals: students don't just learn syntax, they learn to think about what a program needs to do before structuring it in any particular language. Rated 5.0 by students.

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.

Programming starts making sense when you stop memorizing syntax and start thinking about what the computer is actually doing step by step. June's electrical engineering background at Brown gives her insight into both the hardware and software sides — she can explain why an algorithm is efficient, not just how to write it. From loops and conditionals to data structures and recursion, she connects each concept to real projects she's built in robotics and hackathons.

Building AI systems and low-level software at Stanford — in both Python and C++ — Kevin knows where the theoretical meets the practical in computer science. His biocomputation specialization means he can explain not just how to implement an algorithm, but why certain computational approaches work better for different problem domains. Rated 5.0 by students.

Philosophy trained Calin to build rigorous logical arguments; his math and CS degrees taught him to express that logic in C++ and JavaScript. That combination means he teaches programming concepts — from algorithm design to data structure selection — as exercises in precise reasoning, where every line of code follows from a clear chain of thought. Rated 5.0 by students.

I am a undergraduate freshman of the University of Michigan, studying business at the Ross School of Business. Working together with students and having a good time while seeing steady improvements has proven to provide me great joy. I believe that communication and relationship building is crucial for students to open up about their struggles and also for me to identify problems they don't realize they can improve on, so this is a key aspect of all of my lessons. During my free time, I enjoy playing sports or snacking on desserts while binge-watching Friends!