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Jeff Fessler

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My research group develops models and algorithms for large-scale inverse problems, especially image reconstruction for X-ray CT and MRI.  The models include those based on sparsity using dictionaries learned from large-scale data sets.  Developing efficient and accurate methods for dictionary learning is a recent focus.

For a summary of how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/re

For a summary of how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/re

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Jonathan Rupp

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Trauma in motor vehicle crashes is a major societal problem.  Globally, road traffic injuries are the eighth leading cause of death.  In the United States, motor-vehicle crashes are the leading cause of death for older children and young adults and resulted in almost 33,000 deaths last year.  This figure is expected to increase to over 34,000 deaths this year.

My research focuses on reducing death and injury from physical trauma, particularly in civilian and military motor-vehicle crashes and under body blast events to military vehicles by:

  1. collecting field (i.e., real-world) data and developing innovative analysis methods to characterize the factors that influence injury causation and the type of pre-hospital triage care needed,
  2. conducting laboratory testing and performing computational simulations that quantify human mechanical responses and tissue tolerances to dynamic loading, and
  3. developing criteria and tools for assessing the risk of injury that will aid in the development of countermeasures to reduce or eliminate injury, and
  4. applying these tools and criteria, as well as transportation data-analysis methods, to assess injury prevention technologies such as seat belts and crash-avoidance systems.

My research on civilian passenger vehicle injuries applies to all motor-vehicle occupants.  However, my most recent work has focused on improving occupant protection for vulnerable segments of the population, including the obese, the elderly, and women.

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Alfred Hero

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Alfred O. Hero, PhD, is the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan and co-Director of the Michigan Institute for Data Science.

The Hero group focuses on building foundational theory and methodology for data science and engineering. Data science is the methodological underpinning for data collection, data management, data analysis, and data visualization. Lying at the intersection of mathematics, statistics, computer science, information science, and engineering, data science has a wide range of application in areas including: public health and personalized medicine, brain sciences, environmental and earth sciences, astronomy, materials science, genomics and proteomics, computational social science, business analytics, computational finance, information forensics, and national defense. The Hero group is developing theory and algorithms for data collection, analysis and visualization that use statistical machine learning and distributed optimization. These are being to applied to network data analysis, personalized health, multi-modality information fusion, data-driven physical simulation, materials science, dynamic social media, and database indexing and retrieval. Several thrusts are being pursued:

  1. Development of tools to extract useful information from high dimensional datasets with many variables and few samples (large p small n). A major focus here is on the mathematics of “big data” that can establish fundamental limits; aiding data analysts to “right size” their sample for reliable extraction of information. Areas of interest include: correlation mining in high dimension, i.e., inference of correlations between the behaviors of multiple agents from limited statistical samples, and dimensionality reduction, i.e., finding low dimensional projections of the data that preserve information in the data that is relevant to the analyst.
  2. Data representation, analysis and fusion on non-linear non-euclidean structures. Examples of such data include: data that comes in the form of a probability distribution or histogram (lies on a hypersphere with the Hellinger metric); data that are defined on graphs or networks (combinatorial non-commutative structures); data on spheres with point symmetry group structure, e.g., quaternion representations of orientation or pose.
  3. Resource constrained information-driven adaptive data collection. We are interested in sequential data collection strategies that utilize feedback to successively select among a number of available data sources in such a way to minimize energy, maximize information gains, or minimize delay to decision. A principal objective has been to develop good proxies for the reward or risk associated with collecting data for a particular task (detection, estimation, classification, tracking). We are developing strategies for model-free empirical estimation of surrogate measures including Fisher information, R'{e}nyi entropy, mutual information, and Kullback-Liebler divergence. In addition we are quantifying the loss of plan-ahead sensing performance due to use of such proxies.
Correlation mining pipeline transforms raw high dimensional data (bottom) to information that can be rendered in interpretable sparse graphs and networks, simple screeplots, and denoised images (top). The pipeline controls data collection, feature extraction and correlation mining by integrating domain information and its assessed value relative to the desired task (on left) and accounting for constraints on data collection budget and uncertainty bounds (on right).

Correlation mining pipeline transforms raw high dimensional data (bottom) to information that can be rendered in interpretable sparse graphs and networks, simple screeplots, and denoised images (top). The pipeline controls data collection, feature extraction and correlation mining by integrating domain information and its assessed value relative to the desired task (on left) and accounting for constraints on data collection budget and uncertainty bounds (on right).