Li, Vannucci recognized with Rice Engineering faculty awards

Li awarded for Teaching and Research Excellence; Vannucci for Outstanding Doctoral Thesis Adviser.

Meng Li and Marina Vannucci

Marina Vannucci, Noah Harding Professor of Statistics, and Meng Li, Noah Harding Assistant Professor of Statistics, have both been recognized by the George R. Brown School of Engineering for their performance in the 2019-20 academic year.

Li was awarded the Teaching and Research Excellence Award, tenure-track. Given annually, it recognizes one tenure-track faculty member who demonstrates excellence in both areas.

Li's research is concerned with developing and analyzing methods for challenging data that arise in scientific, medical, and industrial applications, such as images, functional data, and networks. His focus is on advancing mathematical foundations for these problems; in particular, he specializes in Bayesian inference, probabilistic image/signal processing, quantile regression, multiscale modeling, and functional data analysis. His research interweaves these threads and has wide-ranging applications in machine learning, cancer studies, neuroscience, computer vision, economics, and computational materials science.

Vannucci was awarded the Outstanding Doctoral Thesis Adviser Award, awarded in recognition of the achievements of a faculty member's doctoral students who completed all degree requirements from January 1, 2015, to December 31, 2019.

Vannucci’s research focuses on the development of statistical models for complex problems. Her methodological research has focused in particular on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their application. She also works in the areas of graphical models and nonparametric Bayes.

Both faculty members were nominated by an individual or individuals in the School of Engineering, who were also required to provide accompanying materials to validate the nomination. The selection committee for the Faculty and Staff Awards consists of nine faculty members representing all nine academic departments.

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