Moulinath Banerjee

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Moulinath Banerjee, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor.

Moulinath Banerjee was born and raised in India where he completed both his Bachelors and Masters in Statistics at the Indian Statistical Institute, Kolkata. He obtained his Ph.D. from the Statistics department at University of Washington, Seattle, in December 2000, served as lecturer there for Winter and Spring quarters, 2001, and joined University of Michigan in Fall 2001. Mouli’s research interests are in the fields of non-standard asymptotics, empirical process theory, threshold and boundary estimation, and graphical networks. His main contributions to date are in the areas of inference under shape-restrictions, especially monotone functions, and inference in the setting of designed multistage procedures. Mouli is the recipient of the 2011 IISA Young Investigators Award and an elected fellow of ISI. He has a broad range of interests outside of statistics which include classical music, literature, history, philosophy, physics and ancestral genetics. He is, also, most emphatically, a gourmet and believes that a life without good food is a life less lived.

Rich Gonzalez

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My research makes use of state-of-the-art statistical learning and exploratory tools to answer questions at the interface of biology and behavioral science.

Xuming He

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Research interests include quantile regression modeling for associations related to possibly unusual or extreme events, subgroup analysis, and uncertainty quantification after model selection.

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).

Edward Ionides

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Time series analysis with applications to ecology, epidemiology, health economics, cell motion and neuroscience. Methodological work on inference for partially observed stochastic dynamic systems.

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, "Inference for dynamic and latent variable models via iterated, perturbed Bayes maps," PNAS 112:719-724).

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, “Inference for dynamic and latent variable models via iterated, perturbed Bayes maps,” PNAS 112:719-724).