📚 Academic Learning

Carnegie Mellon University (MS in Computer Science)

GPA: 4.00

Coursework in Progress:

• 10725: Convex Optimization
• 15640: Distributed Systems
• 10718: Machine Learning in Practice (Audit)

Coursework:

• 10701: Introduction to Machine Learning (PhD)
• 36700: Probability and Mathematical Statistics
• 15513: Introduction to Computer Systems

Dartmouth College (BA in Math & Computer Science)

GPA: 3.98

Awards: Summa Cum Laude; Phi Beta Kappa

Teaching Assistant Experience:

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

Coursework (* indicate Citations for Meritorious Performance which are awarded to 2.4% of total grades):

• Math 71: Abstract Algebra (Honors)*
• Math 63: Real Analysis (Honors)
• Math 53/126: Partial Differential Equations (Grad)
• Math 38: Graph Theory
• Math 28: Combinatorics*
• Math 23: Differential Equations
• Math 22: Linear Algebra

• Math 13: Vector Calculus

• COSC 89.31: Deep Learning Robustness*
• COSC 89.21: Data Mining*
• COSC 76: Artificial Intelligence
• COSC 74: Machine Learning*
• COSC 51: Computer Architecture*
• COSC 34/234: Randomized Algorithms (Grad)
• COSC 31: Algorithms

• COSC 10: Object Oriented Programming*

• Econ 22: Macroeconomics
• Econ 21: Microeconomics
• Econ 20: Econometrics*

Self Study:

CMU 10714: Deep Learning Systems

See here. Very useful material to understand for any Deep Learning 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.