Natural Sciences
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John Walters
Ph.D. Student in Chemistry, admitted Autumn 2024
Current Research and Scholarly InterestsI am interested in the attosecond dynamics of atoms and molecules. Specifically, my interest lies in investigating the effect of electron-electron correlation and the influence of non Born-Oppenheimer dynamics on isolated quantum systems to drive understanding of both ground-state and excited-state processes. Additionally, I am interesting in applying covariance and machine-learning based techniques to improve measurement resolution in high-repetition rate experiments.
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Guenther Walther
John A. Overdeck Professor
BioGuenther Walther studied mathematics, economics, and computer science at the University of Karlsruhe in Germany and received his Ph.D. in Statistics from UC Berkeley in 1994.
His research has focused on statistical methodology for detection problems, shape-restricted inference, and mixture analysis, and on statistical problems in astrophysics and in flow cytometry.
He received a Terman fellowship, a NSF CAREER award, and the Distinguished Teaching Award of the Dean of Humanities and Sciences at Stanford. He has served on the editorial boards of the Journal of Computational and Graphical Statistics, the Journal of the Royal Statistical Society, the Annals of Statistics, the Annals of Applied Statistics, and Statistical Science. He was program co-chair of the 2006 Annual Meeting of the Institute of Mathematical Statistics and served on the executive committee of IMS from 1998 to 2012. -
Karen D. Wang
Affiliate, Physics
BioMy research is situated at the intersection of machine learning and human cognition. In my work, I apply learning analytics and data mining techniques to students’ interaction data in technology-based learning environments. The goal is to translate fine-grained behavioral data into meaningful evidence about students’ cognitive and metacognitive processes. These enhanced understandings of students’ mental processes and competencies are then used to guide the design of and evaluate instructional materials embedded in educational technology.
