teaching

Back in grad school, I was a teaching assistant for Carnegie Mellon’s convex optimization course. We covered:

• first-order methods
• second-order methods
• interior point methods
• momentum and acceleration
• stochastic optimization
• distributed optimization
• non-convex optimization (e.g., quasi-convex optimization, mixed integer programming, …)
• contact points with statistics (e.g., implicit regularization)
• some convex analysis

Somewhat more recently, I taught an (extremely talented) group of high schoolers as part of Stanford’s “STEM to SHTEM” summer program. We covered:

• the basics of supervised learning
• how to build a (modern) recommendation system
  • matrix completion / factorization-based methods
  • neighborhood-based methods
  • prediction-based methods
  • bandits
  • uncertainty quantification
  • evaluation