Adityanand Guntuboyina is an **Associate Professor and Deputy Chair** in the **Department of Statistics** at the **University of California, Berkeley**. His research focuses on **nonparametric and high-dimensional statistics**, **shape-constrained statistical estimation**, **empirical processes**, and **statistical information theory**. He holds a PhD and has contributed to foundational work in modern statistical methodologies. His research emphasizes theoretical advancements in statistical estimation under shape constraints, with applications to machine learning and high-dimensional data analysis. Notable contributions include methodologies in convex regression, mixture models, and empirical Bayes approaches. Prof. Guntuboyina has published extensively in top-tier journals like the *Journal of the American Statistical Association* and *The Annals of Statistics*. His work bridges statistical theory with computational methods, addressing challenges in modern data science. He advises on grants related to nonparametric estimation and collaborates widely in the statistics community. His lab focuses on developing robust statistical tools for complex datasets, emphasizing both theoretical rigor and practical applicability.
