SEEDS Presentation: Regression with Categorical Variables and Race

In this talk, Jai Uparkar will present research she has contributed to regression and categorical variables that represent race to make statistical conclusions and models more interpretable and fair. Current methods of encoding race into regression models involve establishing a baseline race, which is often non-Hispanic White. This is a problematic assumption because now entire statistical models and interpretations are relative to a dominant race group. The talk will discuss the new method of encoding fairness to gain better understanding of a wide variety of issues ranging from political to medical. Ms. Uparkar will also explain how her time at the CBSR and SEEDs helped her gain research experience and set her up to find opportunities like these.

Join us as we welcome Jai Uparkar, UCSB student and SEEDS alumni, for her virtual talk! Ms. Uparkar is a third-year Computer Science and Statistics & Data Science double major. Since her time in the SEEDS program Ms. Uparkar has participated in a Statistics REU at Rice University where she worked on a project involved with race and statistical regression. She is also a Central Coast Data Science Fellow.