Teaching

I teach students to turn an ambiguous business problem into a question they can investigate, use evidence to examine their assumptions, and explain the reasoning behind a recommendation. My teaching connects marketing and technology to managerial decisions, with particular interests in analytics, digital platforms, and AI.

In the Classroom

I independently taught Introduction to Marketing at NYU Stern in Summer 2025, receiving an overall student rating of 4.6/5.0. Alongside marketing foundations, students worked with R throughout the course, completed a coding-based midterm, and discussed business cases. In a team project, they compared two firms that meet similar customer needs through different strategies and presented a recommendation grounded in their analysis.

I use demonstrations and guided exercises to connect a business question to data preparation, analysis, and interpretation. Scaffolded scripts help students new to programming engage with the reasoning, while extensions give more experienced students room to explore. When students use generative AI to propose hypotheses or interpret results, I ask them to test its claims against data and course frameworks, identify unsupported assumptions, and explain how they used it. Clear expectations, feedback, and accessible office hours support students in developing their own judgment.

Teaching Experience

Teaching Interests