Bryan R. Goldsmith

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Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

Quentin Stout

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I primarily work on developing scalable parallel algorithms to solve large scientific problems. This has been done with teams from several different disciplines and application areas. I’m most concerned with algorithms emphasizing in-memory approaches. Another area of research has developed serial algorithms for nonparametric regression. This is a flexible form of regression that only assumes a general shape, such as upward, rather than a parametric form such as linear. It can be applied to a range of learning and classification problems, such as taxonomy trees. I also work some in adaptive learning, designing efficient sampling procedures.

Stephen Smith

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The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. We develop new methods to address questions about the rates and modes of evolution using the large data sources that have become more common in the biological disciplines over the last ten years. In particular, we use DNA sequence data to construct phylogenetic trees and conduct additional analyses about processes of evolution on these trees. In addition to this research program, we also address how new data sources can facilitate new research in evolutionary biology. To this end, we sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.

A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).

A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).

Martin J. Strauss

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My interests include randomized approximation algorithms for massive data sets, including, specifically, sublinear-time algorithms for sparse recovery in the Fourier and other domains.  Other interests include data privacy, including privacy of energy usage data.