I am a joint Mathematics and Computer Science major at New York University, graduating in 2026. My academic background includes upper-level coursework in Real and Complex Analysis, Abstract Linear Algebra, Probability Theory, Machine Learning, Algorithms, and a graduate-level independent study in Information Theory. I also conduct research with Professor Yanjun Han on information-theoretic methods for empirical Bayes estimation. This work has strengthened both my mathematical problem-solving...
I am a joint Mathematics and Computer Science major at New York University, graduating in 2026. My academic background includes upper-level coursework in Real and Complex Analysis, Abstract Linear Algebra, Probability Theory, Machine Learning, Algorithms, and a graduate-level independent study in Information Theory. I also conduct research with Professor Yanjun Han on information-theoretic methods for empirical Bayes estimation. This work has strengthened both my mathematical problem-solving skills and my ability to explain difficult ideas clearly and precisely.
I have experience tutoring college undergraduates and high-school juniors and seniors in settings ranging from 45- to 90-minute one-on-one Zoom sessions to small-group whiteboard reviews with three to five students. I am especially well prepared to help with AP Calculus BC, honors algebra, introductory college mathematics, probability, linear algebra, algorithms, and related subjects. My background in both theoretical mathematics and computer science also allows me to connect abstract concepts with concrete examples and computational demonstrations.
I begin each session by identifying what the student already understands and where the difficulty starts. I often ask the student to think aloud while working through a representative problem, which helps me distinguish between a conceptual gap, an algebra mistake, and uncertainty about how to begin. From there, I build intuition before introducing formal definitions or procedures. For example, I might use traffic flow to motivate divergence or Lego blocks to explain basis vectors before moving to the underlying mathematics.
After demonstrating a method, I give the student a similar problem to solve independently while I provide real-time feedback. At the end of the session, I ask the student to give a brief summary of why each step works. This helps confirm genuine understanding rather than short-term memorization. I also adapt my explanations to the individual student, using diagrams and Desmos for
