Teaching Statement

As a devoted educator, I draw inspiration from my mother's approach to tutoring high school students, where her primary goal was to ignite their curiosity and inspire them. My early involvement in assisting her with teaching science and mathematics kindled my passion for education. My enthusiasm for history led me to share captivating stories about the individuals behind the mathematical and scientific concepts, such as the remarkable dialogue between G.H. Hardy and Ramanujan regarding taxicab number 1729. These narratives serve to pique students' curiosity about subjects like sums of cubes, Ramanujan's genius, and his contributions. My belief in infusing the human element into STEM subjects encourages students to explore and discover knowledge independently.

During my tenure at Duke University, I had the privilege of leading recitation and tutorial sections as a head teaching assistant, spanning a range of courses from introductory Data Structures and Algorithms to advanced graduate-level offerings in Machine Learning and Computational Economics. My responsibilities extended to designing assignments, creating assessments, and organizing specialized workshops.

My teaching philosophy and pedagogical approach are anchored in three core principles:

1. Creating a Growth and Collaborative Mindset: I prioritize learning over performance, cultivating a growth mindset by utilizing pre and post-lecture ungraded quizzes to quantify progress and receive feedback. To promote collaboration, I design assignments and projects that necessitate teamwork among students from diverse backgrounds. This approach fosters a deeper focus on learning from peers rather than fostering a competitive environment. Additionally, I encourage students to explore independently, allowing them to employ web-based resources with proper referencing.

2. Coupling Theory and Applications: While the theoretical foundations of Computer Science and Statistics are rooted in mathematics, their real-world applications span diverse fields. I bridge this gap by introducing concepts with practical examples, helping students develop intuition and appreciate the complexities of real-world scenarios. For instance, I elucidate the Bayes theorem through the problem of estimating disease probability after a positive test. I design assignments that combine theoretical proofs, method implementation, and real-world data applications. For instance, a Support Vector Machine (SVM) assignment involved analyzing Diabetic retinopathy data, offering students a tangible understanding of SVM's applicability and limitations.

3. Fostering Inclusivity and Embracing Diversity: In today's dynamic educational landscape, classrooms are vibrant tapestries of students hailing from diverse nationalities, races, genders, and sexual orientations. To create a truly inclusive environment, I leverage the power of storytelling and historical narratives. For instance, I enhance discussions on the Universal Turing Machine by delving into Alan Turing's biography, celebrating his contributions to society while shedding light on the challenges he faced due to his sexual orientation. This approach underscores that science is an integral facet of human culture, equal in importance to arts, philosophy, and social sciences. By infusing STEM education with historical accounts, I provide students with a broader perspective on mathematical concepts, enhancing their engagement and comprehension.

Looking ahead, I am committed to developing and teaching a diverse range of courses, leveraging my formal training in computer science, statistics, and economics. My goal is to continue inspiring students, nurturing their curiosity, and fostering an inclusive learning environment where knowledge knows no boundaries.

Experience

1. Instructor Workshop: Introduction to Causal Inference (Advanced), Duke Datathon, Fall 2019 

2. Teaching Assistant COMPSCI 671D Machine Learning, Spring 2019

3. Instructor, Focus Group: Introduction to Data Science, Duke MEMPDC, Fall 2018.

4. Teaching Assistant COMPSCI 590.2 Computational Microeconomics, Fall 2018 (link)

5.  Teaching Assistant COMPSCI 223 Computational Microeconomics, Spring 2018 (link)

6. Teaching Assistant COMPSCI 230, Discrete Mathematics, Fall 2017 (link)

7. Teaching Assistant COMPSCI 230, Discrete Mathematics, Spring 2017 (link)

8. Teaching Assistant COMPSCI 201, Data Structures and Algorithms, Fall 2016 (link)