Did a wide variety of courses across systems, applications and theory of Machine Learning and Deep Learning — Advanced Introduction to Machine Learning, Convex Optimization, Probabilistic Graphical Models, Diffusion Models and Flow Matching, Probability and Mathematical Statistics, LLM Systems, AI for Scientific Computing, Database Systems, and Deep RL.

Took undergraduate courses with a breadth of systems and ML, from the CS & IS core — Deep Learning, Machine Learning, Computer Networks, Design and Analysis of Algorithms, Data Structures and Algorithms, Operating Systems, Applied Statistical Methods, AI, Foundations of Data Science, and a reading course on LLMs.