Adaptive Algorithms

Teaching is fundamentally an act of service: to the students, to their goals, and to the subject itself. As a TA for Algorithm Design & Analysis, which is a challenging theoretical course required for all students regardless of their career plans, I have learned that flexibility and empathy are essential to effective teaching. My view on teaching centers on recognizing the diverse goals and backgrounds of each student and adapting my approach to help them succeed on their own terms.

One of the most important lessons I learned as a TA is that there is no single definition of success for a course like this. For some students, success might mean acquiring enough comfort with formal reasoning to recognize when algorithmic efficiency matters in their future work as a software engineer. For others, success might mean mastering advanced proof techniques and developing the intuition needed to contribute to theoretical computer science research. All outcomes are valid, and all deserve to be supported.

This idea shapes how I structure my teaching. When leading recitations or hosting office hours, I make a point of learning the goals of students and seeing how the course fits into their larger academic or career plans. Knowing a student's goals allows me to adjust my explanations accordingly: for a student aiming for research-level depth, I might dive into the subtleties of a proof technique; and for a student who wants to strengthen practical skills, I might focus on the key algorithmic ideas and why they matter.

Ultimately, I view teaching as a partnership. Students bring their goals, efforts, and experiences; I bring my knowledge, flexibility , and commitment to their success. My role is not to impose a single vision of what mastery looks like, but to help each student find their own path through the material, supporting them with clear explanations, empathetic encouragement, and adaptable strategies.