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Stephanie Teasley

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Dr. Teasley’s research has focused on issues of collaboration and learning, looking specifically at how sociotechnical systems can be used to support effective collaborative processes and successful learning outcomes. As Director of the LED lab, she leads learning analytics-based research to investigate how instructional technologies and digital media are used to innovate teaching, learning, and collaboration. The LED Lab is committed to providing a significant contribution to scholarship about learning at Michigan and in the broader field as well, by building an empirical evidentiary base for the design and support of technology rich learning environments.

Cancer Center, April Harris

Jeremy M G Taylor

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I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

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Pascal Van Hentenryck

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Our research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. We apply evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

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Ji Zhu

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My research interests are in the areas of statistical learning, analysis of high-dimensional data, statistical network analysis, and their applications in biology, health sciences, finance and marketing.

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Atul Prakash

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Data analytics often need to be performed on sensitive data in domains such as healthcare, social networks, Internet of Things, etc. My research looks at design of operating systems and database mechanisms that help keep such data protected, while allowing analytics to be performed.

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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

Mathematics, Anna Gilbert

Anna Gilbert

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My research interests include mathematical analysis, probability, networking, and algorithms. I am especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, computer networking, and massive datasets.

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