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Raj Rao Nadakuditi

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Raj Nadakuditi, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Nadakuditi received his Masters and PhD in Electrical Engineering and Computer Science at MIT as part of the MIT/WHOI Joint Program in Ocean Science and Engineering. His work is at the interface of statistical signal processing and random matrix theory with applications such as sonar, radar, wireless communications and machine learning in mind.

Prof. Nadakuditi particularly enjoys using random matrix theory to address problems that arise in statistical signal processing. An important component of his work is applying it in real-world settings to tease out low-level signals from sensor, oceanographic, financial and econometric time/frequency measurements/time series. In addition to the satisfaction derived from transforming the theory into practice, real-world settings give us insight into how the underlying techniques can be refined and/or made more robust.

Kerby Shedden

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Kerby Shedden has broad interests involving applied statistics, data science and computing with data.  Through his work directing the data science consulting service he has worked in a wide variety of application domains including numerous areas within health science, social science, and transportation research.  A current major focus is development of software tools that exploit high performance computing infrastructure for statistical analysis of health records, and sensor data from vehicles and road networks.

Ivo D. Dinov

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Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

Analyzing Big observational data including thousands of Parkinson's disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

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.