Department awards announced in spring 2024

In May 2024, the Department of Statistics presented awards to its 2024 recipients. They are as follows: 

Thomas and Jean Powers Teaching Award - Dr. Radu Herbei (Tenure Track faculty) and Michelle Everson (Teaching Track faculty) 

The Thomas and Jean Powers Teaching Awards are presented each year in two categories to (1) the best TAs teaching either recitations or lectures, and (2) an outstanding professor in the department. These awards were instituted in 1986 through a generous gift to the Statistics Development Fund by Tom and Jean Powers. In 2022, the faculty category was expanded to recognize both outstanding tenure track and teaching track faculty. 

Teaching Associate Recipients 

Max Cartwright, Wenxin Du, Torey Hilbert, Jiwon Hong, Colleen Sunderland (2024) 

Staff Award - Caitlin Donahue 

This award was instituted in 2011-2012 to recognize outstanding work by our staff. Winners are selected by a vote of our graduate students. 

Craig Cooley Memorial Prize - Rui Qiang   

Each year this award is presented to a graduate student in the department demonstrating exceptional scholarly excellence and leadership abilities. Craig embodied these two qualities throughout his graduate career. Tragically, he was killed just before receiving his PhD in 1996. To honor his memory, the department created the Craig Cooley Memorial Prize. 

Whitney Award 

In 1992, Professor Emeritus Ransom Whitney and his wife Marian Whitney made a generous gift to the Statistics Department Fund to institute several awards for graduate students. They added to this gift in 2008, allowing us to increase the number of awards as our graduate enrollment increases. 

Best Research Associate Recipients 

Wenxin Du, Yue Ma, Xuerong Wang, John Yannotty 

Research Leading to a PhD Recipients 

Hyoin An for work on Bayesian Quantile Regression via Dependent Quantile Pyramids 

Abstract: The simultaneous estimation of multiple quantile regression (QR) curves has gained increasing attention as an alternative to mean regression, yet it remains challenging. We develop a new class of stochastic processes, a process of dependent quantile pyramids (DQPs). This class is applied to build a flexible simultaneous QR model that falls within the Bayesian nonparametric framework. The process of DQPs generalizes the quantile pyramid, a model for a single set of quantiles without a covariate. The generalization replaces each scalar variate in the quantile pyramid with a stochastic process whose index set is a covariate space. The resulting model is a distribution-valued stochastic process which provides a nonparametric distribution at each value of the covariate. We rigorously establish the existence of the model. Simulation studies document the performance of our approach. An application to cyclone intensity data is presented. 

Meijia Shao for work on a Network Two-Sample Test 

Abstract: Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly regarding finite-sample accuracy and minimax optimality. In this paper, we develop a comprehensive toolbox, featuring a novel main method and its variants, all accompanied by strong theoretical guarantees, to address these challenges. Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal. Our algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). We also develop an innovative framework for offline hashing and fast querying as a very useful tool for large network databases. We showcase the effectiveness of our method through comprehensive simulations and applications to two real-world datasets, which revealed intriguing new structures.