Prof. Huang is specialized in satellite remote sensing, atmospheric radiation, and climate modeling. Optimization, pattern analysis, and dimensional reduction are extensively used in his research for explaining observed spectrally resolved infrared spectra, estimating geophysical parameters from such hyperspectral observations, and deducing human influence on the climate in the presence of natural variability of the climate system. His group has also developed a deep-learning model to make a data-driven solar forecast model for use in the renewable energy sector.
Prof. Avestruz is a computational cosmologist leading the ALCCA (Avestruz Lab for Computational Cosmology and Astrophysics) research group. Her research group 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. With the Rubin Observatory’s Legacy Survey of Space and Time, Avestruz’ group is leading software development efforts within the Dark Energy Science Collaboration including applications of machine learning in cosmology.
Tamas Gombosi is the Konstantin Gringauz Distinguished University Professor of Space Science and the Gerstacker Professor of Engineering at the University of Michigan.
Over his four-decade-long career at Michigan he participated in a number of space missions (Cassini, Rosetta, Stereo, MMS and others). In the last two decades he has led a highly interdisciplinary team that developed the first solution adaptive (AMR) global magnetohydrodynamic (MHD) simulation code of space plasmas. His most recent research focus is to bring advanced machine learning to space weather modeling.
He is Fellow of the AGU (1996), Member of the International Academy of Astronautics (1997), recipient of AGU’s inaugural Space Weather Prize (2013), Van Allen Lecturer of AGU’s SPA section (2017), recipient of the Kristian Birkeland Medal (2018), and recipient of AGU’s John Adam Fleming Medal (2020).
Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.
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.
Yang Chen received her Ph.D. (2017) in Statistics from Harvard University and then joined the University of Michigan as an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute of Data Science (MIDAS). She received her B.A. in Mathematics and Applied Mathematics from the University of Science and Technology of China. Research interests include computational algorithms in statistical inference and applied statistics in the field of biology and astronomy.
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.
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:
- Cosmological parameter inference using the abundance and spatial distribution of clusters of galaxies.
- Evolution of the physical properties of the brightest galaxy cluster members.
- Morphological classification of galaxies.
- Dynamical techniques to trace the gravitational potential of galaxy clusters and probe the theory of gravity over large scales.
- Advanced data reduction pipelines for multi-object spectroscopic data.
- Concurrent real-world and simulated data analysis