Teaching

Current teaching

Methods in Data Science (COMP.4770/5770)
University of Massachusetts Lowell · Fall 2026 · Co-instructor

I redesigned the course materials and assignments to connect problem definition, data curation, and evaluation with current AI methods. Lessons cover synthetic data, post-training, retrieval, and agents. Students compare evidence across research papers and develop team projects with individual error or robustness analyses.

I use short explanations, focused activities, and teach-back to understand how students are reasoning. Project proposals and working pilots provide opportunities to revise an approach before the final report. Students may use AI with disclosure and verification, and they must be able to explain and reproduce their work.

Previous teaching

Research mentoring

During my Ph.D., I worked with more than twenty undergraduate and master’s students on research projects. I helped students define questions, implement baselines, design experiments, analyze errors, and communicate results. I adapt my guidance to students’ preparation and help them make and explain increasingly independent research decisions.

Teaching interests

My teaching interests include machine learning, data science, natural language processing, probability for computer science, LLMs, vision-language models, and AI agents. I am also interested in developing a project course in which student teams work with doctoral and industry mentors under faculty supervision.