I'm a University of Washington Informatics student (Data Science concentration) with a Mathematics minor who loves turning tricky ideas into "oh, I get it" moments. I've worked with middle- and high-school learners as well as college peers teaching coding basics, walking through probability and statistics, and coaching study strategies that actually stick. On campus, I've led AI sessions and contributed to AI in accessibility-focused research, which keeps my teaching grounded in real problems and clear outcomes.

I tutor Python, JavaScript, HTML/CSS, introductory computer science, algebra through calculus, probability, statistics, and machine learning. My favorite subjects to teach are Python and statistics because they unlock fast, visible progress students can test ideas immediately, debug thoughtfully, and connect concepts to real-world data.

My style is structured and efficient without being rigid: we set a goal, diagnose gaps, then use a mix of worked examples, quick checks for understanding, and spaced review. I focus on intuition first, then formalize pictures before proofs, pseudocode before syntax so students build confidence and accuracy together. I also share lightweight habits (error logs, "teach-back" summaries, and exam-day checklists) that raise scores and reduce stress.

Outside academics, I care about accessibility in tech, coach youth soccer, and unwind with running, drawing, and weekend dog-friendly trips. If you want sessions that are focused, friendly, and results-driven, I'm excited to help you hit your next milestone.