Promoting fairness, inclusivity, and excitement in undergraduate education through teaching and research | Josh Grossman

Abstract: This talk has three parts. First, I’ll conduct an interactive, undergraduate-level introduction to k-means clustering. I’ll then outline my pedagogy, making specific reference to strategies employed in the teaching demo. Finally, I’ll discuss my research on fairness in college admissions and increasing access to higher education. 

Bio: Josh is a Ph.D. candidate in Computational Social Science at Stanford University’s Department of Management Science & Engineering (MS&E). Broadly, his research applies tools from data science to issues in public policy. His recent work investigates how racial disparate impact manifests itself in judicial decisions and college admissions. Josh recently designed a new course called “Detecting Discrimination with Data” that brings his research into the classroom. He is currently the instructor for the MS&E department’s core undergraduate applied statistics course, and he won the Stanford Centennial TA Award and Department Course Assistant Award as a TA for the same course. Josh has worked as a data scientist at the Stanford Computational Policy Lab, as a product manager in K–12 education technology, and as a research data scientist at Recidiviz, a non-profit that builds technology to reduce incarceration. Josh completed his bachelor’s degree at Harvard University, majoring in Neurobiology and minoring in Statistics. His work is supported by a National Science Foundation Graduate Research Fellowship.