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Bio
As an assistant professor of instruction at Northwestern University, I have developed and taught undergraduate courses of various subjects in statistics, data science, and engineering. The courses cover theoretical building blocks and their application in real-world examples. I hold weekly advising and office hours to provide tailored guidance towards the learning of individual students. As the co-director of the minor in machine learning and data science, and as a member of the department undergraduate committee, I support the development and evaluation of curriculum to align learning outcomes and expectations. In addition, I advise undergraduate students in conducing independent research and acquiring necessary quantitative, communication, and project management skills.
Impact on teaching statement
Summary of Teaching Experience
Earlier, I emphasized classroom instruction, office hours, curriculum coordination, and undergraduate research mentorship. In my updated statement, I expand this role to include advising infrastructure, project management training, and broader student pathway support informed by edSPARK and curriculum analytics.
Cultivating Foundational Skills
My original focus centered on helping students interrogate assumptions, communicate quantitative analysis precisely, and make continuous progress. In the newer statement, I further emphasize helping students navigate increasingly complex and AI-rich learning environments while maintaining strong analytical judgment.
Building Connections with Students
Previously, I highlighted individualized guidance through office hours and mentorship. The updated statement reflects my broader view that student success also depends on transparent academic systems, accessible advising structures, and proactive curricular support inspired by edSPARK.
Adopting Contemporary Tools to Enhance Learning
Earlier, contemporary tools were framed primarily around open-source software and computational accessibility. In the updated version, I explicitly address generative AI, data-informed advising, and visualization systems such as edSPARK as mechanisms for improving planning, engagement, and long-term academic decision-making.
Continuous Learning as an Instructor
My earlier statement framed continuous learning as essential for effective teaching practice. The updated statement extends this philosophy toward institutional learning: using empirical student pathways, advising feedback, and curriculum analytics to iteratively improve instruction and educational infrastructure.