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By Purnamrita Sarkar, explicitly named on the title slide. The University of Texas at Austin, Department of Statistics and Data Science, is the listed affiliation. The author’s GitHub Pages site hosts the slides. No publication, revision or course year is established.

Lecture slides: psarkar.github.io/SDS384/efron-stein.pdf

Cited passages: PDF pp. 1–9, title and byline; independent inputs; conditional-variance bound and independent-copy form; equality for sums.

Purnamrita Sarkar works at the intersection of high-dimensional statistics, machine-learning theory, differential privacy and large-scale optimization. Her University of Texas at Austin profile describes this combination of statistical and computational research.^[ref]

Her lecture on the Efron–Stein inequality presents a general way to control the variance of a function of independent inputs: compare the function with versions in which one input is independently replaced. For a sum, the resulting bound recovers the familiar sum of variances; for a complicated estimator, it gives a route to uncertainty control without expanding every covariance term.^[ref]