I am a research scientist (PhD) at the University of Massachusetts Amherst. My field is efficient and scalable applied AI, including foundation models (LLMs, Diffusion, GANs, MOE, Sparse, Vector DB, Graphs).
I also work as an instructor teaching computer science courses to undergraduate students for over five years. In both my research and teaching, I use a data driven approach to setup measurements for continuous review of the progress made and to create guarantees of outcomes. For...
I am a research scientist (PhD) at the University of Massachusetts Amherst. My field is efficient and scalable applied AI, including foundation models (LLMs, Diffusion, GANs, MOE, Sparse, Vector DB, Graphs).
I also work as an instructor teaching computer science courses to undergraduate students for over five years. In both my research and teaching, I use a data driven approach to setup measurements for continuous review of the progress made and to create guarantees of outcomes. For example, while students are resistant to an active classroom setting, students who participate in my feedback oriented class activities have a guarantee of succeeding in the course and in the next course level, with a high probability, independent (conditioned on participation) of work they do outside of the course (i.e. homework) and their knowledge of the subject when entering the course. I developed the course material and conducted the analysis of students throughout multiple semesters to guarantee these outcomes.
I have taught in both a classroom setting and online over video conferencing platforms. Classroom sizes spanned from ten to sixty students. I also volunteered to teach computer science to high school and middle school students over the summer.
