Aditya Guntuboyina

Aditya Guntuboyina is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He has held this position since January 2012, following a postdoctoral stint at the Wharton Statistics Department and a PhD in Statistics from Yale University (2011) under Professor David Pollard. He earned his B.Stat and M.Stat degrees from the Indian Statistical Institute, Kolkata.

• PhD: Statistics, Yale University (2011)
• B.Stat/M.Stat: Indian Statistical Institute, Kolkata

His research focuses on nonparametric and high-dimensional statistics, particularly shape-constrained estimation and Bayesian/Empirical Bayes methods. Key themes include convex regression, isotonic regression, mixture models, and total variation denoising. Recent publications analyze multivariate scale mixtures, convergence rates, and suboptimality of least squares in constrained settings.

Aditya has supervised multiple PhD students in the Berkeley Statistics and EECS programs. He teaches courses such as Time Series Analysis (Stat 153/248), Data, Inference, and Decisions (Data 102), and advanced probability (Stat 201A). His work often intersects with machine learning, optimization, and information theory.

Scientific contributions include theoretical advances in shape-restricted regression, adaptation in log-concave density estimation, and risk bounds for convex-constrained models. He has published in top journals like Annals of Statistics, Journal of the Royal Statistical Society: Series B, and IEEE Transactions on Information Theory.