Award-Winning AP Computer Science Principles Tutors
serving Olympia, WA

Intensely curious, I am interested in STEM subjects and the liberal arts. I tutor to help you reach your educational goals and because it's immensely gratifying to see my students succeed.

Joshua scored a 5 on the AP Computer Science A exam, which means the Principles course's pseudocode and algorithm questions draw on knowledge he's already internalized at a deeper level. He teaches the Create Task as a scaled-down version of real software development — planning, building, and documenting — using his hands-on experience in Java, Python, and JavaScript to show students what clean, explainable code actually looks like.

Nicholas codes in Java, Python, JavaScript, C#, and HTML/CSS daily — so when AP CSP asks students to reason about algorithms or data representation in pseudocode, he can instantly translate those abstractions into real programming scenarios that make the logic click. His Penn State CS degree also means he understands the networking and cybersecurity concepts woven through the course at a technical level most high schoolers never see. Rated 5.0 by students.

With a strong background in STEM education and administration, I am passionate about helping students achieve academic success through personalized learning. My experience as a high school STEM teacher has allowed me to guide students in subjects such as artificial intelligence, machine learning, renewable energy technology, supply chain management, and manufacturing systems. Additionally, my administrative expertise has honed my ability to provide structured and efficient support to learners of all levels. I have earned certifications from the Massachusetts Institute of Technology, International Business Machines (IBM), and the New York Institute of Finance, dedicating years to making complex concepts more engaging and accessible. My tutoring style is adaptive, ensuring that each student's unique learning needs are met with patience and clarity. I believe in creating a supportive learning environment where students feel confident in tackling challenges and mastering new skills. While I tutor a broad range of subjects, I am particularly passionate about STEM-related fields, as they shape the future of technology and innovation. Beyond academics, I enjoy exploring advancements in artificial intelligence, renewable energy solutions, and automotive technology. My goal is to inspire curiosity, critical thinking, and a lifelong love of learning in every student I work with.

Ritesh's physics training at Cornell actually maps neatly onto AP CSP — both demand thinking about systems in layers, whether that's abstraction in computing or modeling forces in mechanics. He teaches the pseudocode and algorithm-tracing portions by treating them like physics problem-solving: break the system into parts, track what changes at each step, and verify the output makes sense.

Samuel's applied math training at Caltech intersects directly with AP CSP's algorithm and data units — he can trace how a sorting algorithm's efficiency scales or why lossy compression works because he uses that math daily. He also taught a discrete mathematics course through PACT, which means pseudocode logic and combinatorial reasoning come naturally when prepping students for both the multiple-choice exam and the Create Task.

Derek scored 5s on both AP Computer Science A and AP Physics C while taking 16 APs at the high school level, so he knows how to manage the breadth of a course like AP CSP without letting any Big Idea slip through the cracks. Now studying CS at Harvard with an applied math minor, he digs into the algorithmic thinking and pseudocode reasoning that drive the multiple-choice section — and coaches students through the Create Task with the structured planning habits that come from building real software projects.

Computer engineering at Georgia Tech means Yash lives in the space where hardware meets software — exactly the kind of systems-level thinking that makes AP CSP's Big Ideas about the internet, abstraction, and data representation feel intuitive rather than abstract. He teaches students to trace pseudocode and reason through algorithm questions the way an engineer debugs a circuit: step by step, with nothing assumed. Rated 5.0 by students.

Cognitive science training at Stanford gave David an unusual lens for AP CSP — he studied how humans process information before studying how computers do, which means he can explain abstraction, algorithms, and data representation in terms that actually click. His experience teaching web and app development to high schoolers abroad sharpened his ability to walk students through the Create Task from planning to polished written response.

Having TA'd computer science courses at MIT and now pursuing a PhD in Operations Research at Georgia Tech, Isabella brings real programming fluency — particularly in Python — to the algorithmic thinking and data analysis threads that run through AP CSP. She digs into how pseudocode on the exam maps to actual code students write for the Create Task, making the connection between abstract logic and working programs click. Rated 5.0 by students.

Having tutored everything from basic programming constructs to finite automata and computational theory, Andrew brings unusual depth to a course most students treat as surface-level. He digs into the algorithmic thinking and pseudocode reasoning behind AP CSP's exam questions, showing students how concepts like abstraction and data representation connect to the real CS pipeline he studied in his degree. Rated 5.0 by students.

Dalila's math degree gives her a sharper lens on the algorithmic and data-representation units that carry the most weight in AP CSP — she can explain why a particular sorting approach scales better than another or how abstraction layers simplify a complex problem. For the Create Performance Task, she emphasizes building clean pseudocode logic first, then translating it into a written response that hits every rubric point without overcomplicating the code.

Firas's machine learning research at Princeton means he can show students what abstraction, algorithms, and data representation actually look like in practice — not just as AP exam vocabulary but as tools working scientists use daily. He's particularly sharp at teaching the pseudocode reasoning and written response skills the Create Task demands, since his PhD work required translating complex computational ideas into clear, precise language. Rated 5.0 by students.