Philosophy

My teaching and research are grounded in the conviction that data, while powerful, is never neutral. Its value emerges only through critical thinking, ethical reflection, and interdisciplinary collaboration. As an applied economist working in energy commodities and financial markets, I study how policy, market structures, and global events shape real-world outcomes, integrating time series analysis, causal inference, and machine learning. This research perspective directly informs my teaching, where I challenge students not only to master analytical tools but also to interrogate the origins, context, and potential consequences of the data they use.

In the classroom, I create an environment built on strong personal connections, curiosity, independent thinking, and real-world relevance. From introductory to advanced courses, I guide students to build technical fluency alongside the ability to communicate, collaborate, and reason ethically. I intentionally design projects that require students to grapple with complex, often messy datasets, mirroring the realities of professional practice. 

My research approach mirrors this philosophy: rigorous in method, collaborative in spirit, and focused on questions with tangible policy and societal implications. I actively involve students in my work, fostering opportunities for co-authorship and mentorship that empower them to become confident, responsible analysts. Ultimately, my goal in teaching and scholarship is to equip students with the skills, mindset, and ethical grounding to use data thoughtfully and effectively in a complex, interconnected world.