academics

Teaching Experience

Since February 2024, I have served as an Adjunct Instructor at Johns Hopkins University’s Carey School of Business, where I teach machine-learning methods to master’s-level students across multiple programs including Business Analytics, Risk Management, and Financial Engineering. In this role, I have taught Big Data Machine Learning during Spring 2024 to one section of students, and I am currently teaching Practical Machine Learning to two sections in Spring 2025.

My journey in teaching at Johns Hopkins began earlier as a Graduate Teaching Assistant from March 2022 to May 2023, where I focused on enhancing student mastery of machine-learning skills across various quantitative business courses. During this period, I supported more than 36 hands-on machine learning hackathons, serving over 150 students in the Business Analytics and Risk Management programs. This experience involved curating real-world datasets, authoring interactive Jupyter notebooks with starter code, and designing challenging problems that would push students to apply their theoretical knowledge in practical settings.

Throughout my teaching assistant role, I had the opportunity to support a diverse range of courses that spanned the quantitative business curriculum. This progression from teaching assistant to instructor has allowed me to develop a comprehensive understanding of how students learn machine learning concepts and has shaped my approach to making complex quantitative methods accessible and engaging for business students.

Courses assisted as Graduate Teaching Assistant:

• Big Data Machine Learning (Spring II 2022 & Spring II 2023, Head TA)
• Operations Management (Fall I 2022)
• Data Analytics in R (Fall II 2022)
• Linear Econometrics for Finance (Fall II 2022)
• Python for Data Analytics (Spring I 2023)
• Empirical Finance (Spring II 2023)

Graduate Coursework (Biomedical Engineering, JHU)