This fall I am serving as a teaching assistant for CS 260: Design and Analysis of Algorithms at KAUST.

My responsibilities include weekly office hours, supporting assignments and grading, helping with examinations, and reviewing solutions. The course covers a kind of reasoning that is fundamental across Computer Science: not only finding an algorithm that works, but explaining why it is correct and how its cost grows with the size of the problem.

Teaching creates a useful standard for an explanation. A complete solution should make three things visible:

  1. the invariant or structural idea that the algorithm relies on;
  2. the argument that connects this idea to correctness; and
  3. the time and space complexity, including the assumptions behind them.

It is easy to compress one of those steps when a method already feels familiar. In office hours and solution reviews, that missing step is often exactly where the real question lives.

This also connects back to my research. Work on optimization and distributed training involves algorithms, proofs, and complexity bounds, but research writing is usually addressed to a narrow audience. Teaching a foundational algorithms course is practice in presenting the same kind of precise reasoning without assuming that the reader already knows where the argument is going.

I expect the experience to improve more than my teaching. Explaining a proof cleanly is also a test of whether I understand its structure well enough to use it, modify it, and communicate it in research.