Table of Contents

Teaching Assistant | AM 10 Mathematical Methods for Engineers 1 | Winter 2024 + Spring 2024

• Led discussion sections where students strengthened Linear Algebra knowledge base.
• Guided students through MatLab troubleshooting in relation to the lab reports.
• Developed collection of notes for students to review material and examples covered in section, in addition to a final exam study guide.
• Repository with code examples can be found at Github.com/m..d..p/AM10S24.
• Graded student's exams and lab reports, ensuring consistency and fairness.

Teaching Assistant | PHYS 116A Mathematical Methods For Physicists F23

• Prepared practice problems, in the context of the assignments, for students.
• Led discussion sections
• Prepared finals study guide that included extra examples and resources.
• Basic ODEs solutions, Linear Algebra, Laplace transforms, Convergence Analysis

Small Group Tutor | CSE 102 - Introduction to Algorithm Analysis

• Facilitated a peer-centered learning environment, assisting students in understanding algorithmic problem-solving strategies and in communicating mathematical abstractions.
• Provided insight into applying algorithmic thinking and optimization techniques for time and space complexity, equipping students with the knowledge to tackle a variety of algorithmic challenges.

Supplemental Instruction Leader | CSE 20 - Introduction To Python 🐍

• Provided students with supplemental instruction in Python, diagnosing and guiding debugging strategies, and helping students recognize design patterns such as linear/binary recursion, sorting, and OOP.
• Authored a series of Jupyter notebooks, acting as lecture notes, session plans, and exam preparation for CSE 20. A public copy can be found at GitHub.com/m..d..p../CSE-20-SI

Grader/Reader | CSE 140 - Artificial Intelligence

• Evaluated and provided feedback on student examinations, focusing on understanding and application of core AI principles: Constraint Satisfaction Problems, Adversarial Search, Markov Decision Processes, Reinforcement Learning, Knowledge Representation, Bayesian Networks, and Machine Learning.