Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.
Aaron Towne is an Assistant Professor in the Department of Mechanical Engineering. His research develops simple models that can be used to understand, predict, and control turbulent fluid dynamical systems. His approach focuses on identifying and modeling coherent flow structures, i.e., organized motions within otherwise chaotic flows. These structures provide building blocks for an improved theoretical understanding of turbulence and also contribute significantly to engineering quantities of interest such as drag, heat transfer, and noise emission. Consequently, strategically manipulating coherent structures can potentially lead to vast performance improvements in a wide range of engineering applications. Realizing this potential requires new data mining and analysis methods that can be used to identify and extract these organized motions from the large data sets produced by high fidelity simulations and experiments, as well as new theoretical and computational approaches for modeling and controlling them. Aaron’s research focuses on developing these tools for turbulent flow applications, while also contributing more broadly to the emerging areas of large-scale data mining and machine learning.
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.
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 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 is an Associate 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.
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.
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.