Blog

Apr 10
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Oleg Gnedin

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I am a theoretical astrophysicist studying the origins and structure of galaxies in the universe. My research focuses on developing more realistic gasdynamics simulations, starting with the initial conditions that are well constrained by observations, and advancing them in time with high spatial resolution using adaptive mesh refinement. I use machine-learning techniques to compare simulation predictions with observational data. Such comparison leads to insights about the underlying physics that governs the formation of stars and galaxies. I have developed a Computational Astrophysics course that teaches practical application of modern techniques for big-data analysis and model fitting.

Emergence of galaxies and star clusters in cosmological gasdynamics simulations. Left panel shows large-scale cosmic structure (density of dark matter particles), which formed by gravitational instability. In the middle panel we can resolve this structure into disk galaxies with complex morphology (density of molecular/red and atomic/blue gas). These galaxies should create massive star clusters, such as shown in the right panel (real image — to be reproduced by our future simulations!).

Mar 08
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Timothy McKay

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I am a data scientist, with extensive and various experience drawing inference from large data sets. In education research, I work to understand and improve postsecondary student outcomes using the rich, extensive, and complex digital data produced in the course of educating students in the 21st century. In 2011, we launched the E2Coach computer tailored support system, and in 2014, we began the REBUILD project, a college-wide effort to increase the use of evidence-based methods in introductory STEM courses. In 2015, we launched the Digital Innovation Greenhouse, an education technology accelerator within the UM Office of Digital Education and Innovation. In astrophysics, my main research tools have been the Sloan Digital Sky Survey, the Dark Energy Survey, and the simulations which support them both. We use these tools to probe the growth and nature of cosmic structure as well as the expansion history of the Universe, especially through studies of galaxy clusters. I have also studied astrophysical transients as part of the Robotic Optical Transient Search Experiment.

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

Mar 08
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Christopher Miller

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Professor Christopher Miller is an observational cosmologist who works in the fields 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. Recently, he led the NOAO Science Data Management group, where he was responsible for using and delivering science quality astronomical data from instruments like the MOSAIC optical and NEWFIRM IR images on NOAO’s 4m-class telescopes. He was hired at the University of Michigan under a U-M Presidential initiative for advancing data mining research. His research and teaching emphasizes open source collaborative code development and the use of cloud computing to analyze large volumes of astronomical data. Professor Miller’s group develops and applies advanced computational and statistical techniques to address the following research areas:

  1. Cosmological parameter inference using the abundance and spatial distribution of clusters of galaxies.
  2. Evolution of the physical properties of the brightest galaxy cluster members.
  3. Morphological classification of galaxies.
  4. Dynamical techniques to trace the gravitational potential of galaxy clusters and probe the theory of gravity over large scales.
  5. Advanced data reduction pipelines for multi-object spectroscopic data.
  6. Concurrent real-world and simulated data analysis
Left: Three galaxies --an elliptical and an armless disk galaxy (or S0) (top); the same spiral galaxy as seen through a "low-resolution" ground-based telescope (bottom-left) versus the "high resolution" space-based Hubble telescope (bottom-right). Right: We measure the 1D elliptically projected light profile with three parameters: size, ellipticity, and power-law of the intensity. We then apply machine learning to classify the morphologies for ellipticals, spirals, and S0 disk galaxies. The parameters alone can separate spirals from ellipticals even in poorly resolved images. We have developed new machine learning algorithms to clean "gold standard" training sets which suffer from instrumental defects. Image: Chris Miller U-M Astronomy, Guillermo Cabrera Center for Mathematical Modeling, Univ de. Chile

Left: Three galaxies –an elliptical and an armless disk galaxy (or S0) (top); the same spiral galaxy as seen through a “low-resolution” ground-based telescope (bottom-left) versus the “high resolution” space-based Hubble telescope (bottom-right). Right: We measure the 1D elliptically projected light profile with three parameters: size, ellipticity, and power-law of the intensity. We then apply machine learning to classify the morphologies for ellipticals, spirals, and S0 disk galaxies. The parameters alone can separate spirals from ellipticals even in poorly resolved images. We have developed new machine learning algorithms to clean “gold standard” training sets which suffer from instrumental defects. Image: Chris Miller U-M Astronomy, Guillermo Cabrera Center for Mathematical Modeling, Univ de. Chile