📚 Academic Learning

Carnegie Mellon University (MS in Computer Science)

GPA: 4.00

Coursework:

• 15618: Parallel Computer Architecture and Programming
• 11868: Large Language Model Systems
• 11642: Machine Learning Systems
• 15640: Distributed Systems
• 15513: Introduction to Computer Systems
• 10708: Probabilistic Graphical Models
• 10725: Convex Optimization
• 36700: Probability and Mathematical Statistics
• 10701: Introduction to Machine Learning
• 10718: Machine Learning in Practice (Audit)

Dartmouth College (BA in Math)

GPA: 3.98

Awards: Summa Cum Laude; Phi Beta Kappa

Teaching Assistant Experience:

• COSC 78: Deep Learning
• COSC 74: Machine Learning

Self Study:

CMU 10714: Deep Learning Systems

See here. Very useful material to understand for any DL researcher or practioner.

Stanford CS234: Reinforcement Learning

Completed projects from Stanford Reinforcement Learning CS234 by watching online lectures and reading the free Sutton & Barto textbook. My code and theoretical analysis can be found here. My motivation for studying this was that RL is used a lot in sota LLM post training and I wanted to at least know the basics