 Purnamrita Sarkar

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]