Satish Narayanasamy

Satish Narayanasamy

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Satish Narayanasamy, Ph.D., is Associate Professor in the Electrical Engineering and Computer Science department in the College of Engineering at the University of Michigan, Ann Arbor. Satish’s interests are working at the intersection of computer architecture, software systems and program analysis. His current interests include concurrency, security, customized architectures and tools for mobile and web applications, machine learning assisted program analysis, and tools for teaching at scale.


Accomplishments and Awards

Bhramar Mukherjee

Bhramar Mukherjee

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Bhramar Mukherjee is  a Professor in the Department of Biostatistics, joining the department in Fall, 2006. Bhramar is also a Professor in the Department of Epidemiology. Bhramar completed her Ph.D. in 2001 from Purdue University. Bhramar’s principal research interests lie in Bayesian methods in epidemiology and studies of gene-environment interaction. She is also interested in modeling missingness in exposure, categorical data models, Bayesian nonparametrics, and the general area of statistical inference under outcome/exposure dependent sampling schemes. Bhramar’s methodological research is funded by NSF and NIH.   Bhramar is involved as a co-investigator in several R01s led by faculty in Internal Medicine, Epidemiology and Environment Health sciences at UM. Her collaborative interests focus on genetic and environmental epidemiology, ranging from investigating the genetic architecture of colorectal cancer in relation to environmental exposures to studies of air pollution on pediatric Asthma events in Detroit. She is actively engaged in Global Health Research.


Accomplishments and Awards

Timothy McKay

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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.

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

Raj Rao Nadakuditi

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.


Accomplishments and Awards

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.


Accomplishments and Awards

Long Nguyen

Long Nguyen

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I am broadly interested in statistical inference, which is informally defined as the process of turning data into prediction and understanding. I like to work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. My motivation for all this came originally from an early interest in machine learning, which continues to be a major source of research interest. A primary focus of my group’s research in machine learning to develop more effective inference algorithms using stochastic, variational and geometric viewpoints.

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.

9.9.2020 MIDAS Faculty Research Pitch Video.

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.

Margaret Hedstrom

Margaret Hedstrom

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Margaret Hedstrom, PhD, is the Robert M Warner Collegiate Professor of Information in the School of Information and Faculty Associate in the Institute for Social Research.

Prof. Hedstrom’s research centers on the methods, costs, incentives, and implementation of scalable digital curation and archiving services as a core element of the underlying infrastructure for research data management, reproducible research, and data analysis.  She studies the social and technical dimensions digital curation including data sharing behaviors among scientists in different research domains, techniques for automated metadata extraction and user-contributed metadata, requirements for meaningful reuse of numeric, image, and textual data, and long-term preservation of digital information.  Her current research projects span projects involving researchers in environmental science and sustainability, social science, bioinformatics, and materials science.