Bryan Goldsmith

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Bryan Goldsmith is an Assistant Professor in the Department of Chemical Engineering. His works focus on the development of novel catalysts and materials. The world is facing a growing population, mass consumerism, and rising greenhouse gas levels, all the while people strive to increase their standard of living. Computational modeling of catalysts and materials, and making use of its synergy with experiments, facilitates the process to design new systems since it provides a valuable way to test hypotheses and understand design criteria. His research team focuses on obtaining a deep understanding of catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy generation. They use first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., statistical learning and data mining) to extract key insights of catalysts and materials under realistic conditions, and to help create a platform for their design.

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)

Monica Valluri

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Monica Valluri is an Associate Research Professor at the U-M Department of Astronomy. She studies galactic dynamics, including accurately measuring the masses of supermassive black holes in galaxies; non-linear dynamical processes involved in sculpting galaxies; the properties of “dark matter”; understanding the dynamical structure of the Milky Way Galaxy; predicting the observable properties of dark matter and the detectability of “dark stars”; and inferring the dynamical effects of cluster environments on spiral galaxies from the distributions of their atomic hydrogen gas and stars.

Michael Cafarella

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Michael Cafarella is an Associate Professor in the Department of Electrical Engineering and Computer Science, Computer Science Division. He was appointed the Morris Wellman Faculty Development Assistant Professor of Computer Science and Engineering, and a Sloan Research Fellow (2016). Prof. Cafarella studies databases, information extraction, data integration, and data mining. His projects span several areas of data management including systems and algorithms for “messy” data management, novel data-intensive applications, and data systems infrastructure.

Barzan Mozafari

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Barzan Mozafari is an Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan (Ann Arbor), where he is a member of the Michigan Database Group and the Software Systems Lab. Prior to that, he was a postdoctoral associate at Massachusetts Institute of Technology. He earned his Ph.D. in Computer Science from the University of California at Los Angeles. He is passionate about building large-scale data-intensive systems, with a particular interest in database-as-a-service clouds, distributed systems, and crowdsourcing. In his research, he draws on advanced mathematical models to deliver practical database solutions. He has won several awards and fellowships, including SIGMOD 2012 and EuroSys 2013’s best paper awards.

Veera Sundararaghavan

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Prof. Sundararaghavan develops multi-scale computational methods for polycrystalline alloys, polymer composites, and ultra-high temperature ceramic composites to model the effect of microstructure on the overall deformation, fatigue, failure, thermal transport and oxidation response. Recent packages developed include a fully parallel multiscale approach for optimization of polycrystalline alloys during forming processes and a multiscale approach for modeling oxidative degradation in high temperature fiber reinforced ceramic matrix composites. He has made seminal contributions towards the use of multiscale models for accelerated “microstructure-sensitive design” including development of data mining methods for microstructures and reduced order techniques for graphical visualization of microstructure-process-property relationships.

Results from a parallel crystal plasticity code showing the stress distribution in Aluminum alloy microstructure during compression testing.

Results from a parallel crystal plasticity code showing the stress distribution in Aluminum alloy microstructure during compression testing.

Chris Miller

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Christopher J. Miller is an Assistant Professor of Astronomy and Physics. Professor Miller is a leader in the field of astronomical data mining and computational astrostatistics. He co-founded the INternational Computational Astrostatistics (INCA) group, a collaboration of researchers from the University of Michigan, Carnegie Mellon University, University of Washington, Georgia Tech, the NOAO, and others. From 2008-2010, he led the NOAO Science Data Management group, where he was responsible for using and delivering science quality astronomical data from the US ground-based observatories. He was hired at the University of Michigan under a U-M Presidential initiative for advancing data mining research. His research interests include studies of large-scale structure and cosmology, galaxy clustering, galaxy formation and galaxy evolution.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.

Astro-informatics is an emerging discipline which matches the large, complex, and time-varying datasets generated by earth and space-based astronomical observatories, to modern unsolved challenges in computer science and statistics. In this example, we compare a semi-blind Fourier deconvolution of an astronomical image (left) to the forward modeling of a physically motivated but smooth galaxy light profile (right). Note that the data are sparse and that the underlying point-spread functions (PSF) are not well known. The technique on the left was developed by Se Un Park (a PhD graduate from EECS) and produces an estimate of the PSF from the data. The method on the right is a traditional astronomical technique. The goal is to obtain the best shape classification of the galaxies in the Universe. With our research, we hope to uncover some of Nature’s astrophysical secrets through the interdisciplinary development and application of computer science algorithms and statistical methods on astronomical datasets.