Murali Mani

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Murali Mani, PhD, is Associate Professor of Computer Science at the University of Michigan, Flint.

The significant research problems Prof. Mani is investigating include the following: big data management, big data analytics and visualization, provenance, query processing of encrypted data, event stream processing, XML stream processing. data modeling using XML schemas, and effective computer science education. In addition, he has worked in industry on clickstream analytics (2015), and on web search engines (1999-2000). Prof. Mani’s significant publications are listed on DBLP at: http://dblp.uni-trier.de/pers/hd/m/Mani:Murali.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

Illustrating how our SMART system effectively integrates big data processing and data visualization to enable big data visualization. The left side shows a typical data visualization scenario, where the different analysts are using their different visualization systems. These visualization systems can provide interactive visualizations but cannot handle the complexities of big data. They interact with a distributed data processing system that can handle the complexities of big data. The SMART system improves the user experience by carefully sending additional data to the visualization system in response to a request from an analyst so that future visualization requests can be answered directly by the visualization system without accessing the data processing system.

 

Kevin Dombkowski

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Kevin Dombkowski, DrPH, is Research Associate Professor in the department of Pediatrics, Medical School, and holds a secondary appointment in the School of Public Health at the University of Michigan, Ann Arbor.

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.

Further, his research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services, including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.

Rie Suzuki

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Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.

Greg Rybarczyk

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Using GIS, visual analytics, and spatiotemporal modeling, Dr. Rybarczyk examines the utility of Big Data for gaining insight into the causal mechanisms that influence travel patterns and urban dynamics. In particular, his research sets out to provide a fuller understanding of “what” and “where” micro-scale conditions affect human sentiment and hence wayfinding ability, movement patterns, and travel mode-choices.

Recent works: Rybarczyk, G. and S. Banerjee. (2015) Visualizing active travel sentiment in an urban context, Journal of Transport and Health, 2(2): 30

Michael Cafarella

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Michael Cafarella, PhD, is Associate Professor of Electrical Engineering and Computer Science, College of Engineering and Faculty Associate, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Cafarella’s research focuses on data management problems that arise from extreme diversity in large data collections. Big data is not just big in terms of bytes, but also type (e.g., a single hard disk likely contains relations, text, images, and spreadsheets) and structure (e.g., a large corpus of relational databases may have millions of unique schemas). As a result, certain long-held assumptions — e.g., that the database schema is always known before writing a query — are no longer useful guides for building data management systems. As a result, my work focuses heavily on information extraction and data mining methods that can either improve the quality of existing information or work in spite of lower-quality information.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

A peek inside a Michigan data center! My students and I visit whenever I am teaching EECS485, which teaches many modern data-intensive methods and their application to the Web.

Nils G. Walter

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Nils G. Walter, PhD, is the Francis S. Collins Collegiate Professor of Chemistry, Biophysics and Biological Chemistry, College of Literature, Science, and the Arts and Professor of Biological Chemistry, Medical School, at the University of Michigan, Ann Arbor.

Nature and Nanotechnology likewise employ nanoscale machines that self-assemble into structures of complex architecture and functionality.  Fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblies in real-time.  In particular, single molecule fluorescence resonance energy transfer (smFRET) allows us to measure distances at the 2-8 nm scale, whereas complementary super-resolution localization techniques based on Gaussian fitting of imaged point spread functions (PSFs) measure distances in the 10 nm and longer range.  In terms of Big Data Analysis, we have developed a method for the intracellular single molecule, high-resolution localization and counting (iSHiRLoC) of microRNAs (miRNAs), a large group of gene silencers with profound roles in our body, from stem cell development to cancer.  Microinjected, singly-fluorophore labeled, functional miRNAs are tracked at super-resolution within individual diffusing particles.  Observed mobility and mRNA dependent assembly changes suggest the existence of two kinetically distinct assembly processes.  We are currently feeding these data into a single molecule systems biology pipeline to bring into focus the unifying molecular mechanism of such a ubiquitous gene regulatory pathway.  In addition, we are using cluster analysis of smFRET time traces to show that large RNA processing machines such as single spliceosomes – responsible for the accurate removal of all intervening sequences (introns) in pre-messenger RNAs – are working as biased Brownian ratchet machines.  On the opposite end of the application spectrum, we utilize smFRET and super-resolution fluorescence microscopy to monitor enhanced enzyme cascades and nanorobots engineered to self-assemble and function on DNA origami.

Artistic depiction of the SiMREPS platform we are building for the direct single molecule counting of miRNA biomarkers in crude biofluids (Johnson-Buck, A. et al. Kinetic fingerprinting to identify and count single nucleic acids. Nat Biotechnol 33, 730-732 (2015)).

Artistic depiction of the SiMREPS platform we are building for the direct single molecule counting of miRNA biomarkers in crude biofluids (Johnson-Buck, A. et al. Kinetic fingerprinting to identify and count single nucleic acids. Nat Biotechnol 33, 730-732 (2015)).

Romesh P. Nalliah

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Dr. Nalliah’s expertise in research focuses on process evaluation. He has studied healthcare delivery processes, educational processes and healthcare outcomes. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight hospital resource utilization for managing dental caries, pulpal lesions, periapical lesions and general oral conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals.

Dr. Nalliah’s interest for future research is to expand experience in various areas of public health but not forget his connection to dentistry. Dr. Nalliah has conducted research related to gun violence, facial fractures, spinal fusion, oral cancer, dental conditions, educational debt, mental health, suicide, sports injuries, poisoning and the characteristics of patients discharged against medical advice. National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of dental and health education curriculum and practice.

Dr. Nalliah’s passion in research is improving healthcare delivery systems and he’s interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.

Jason Owen-Smith

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Our data architecture combines naturally-occurring data from research grant inputs with scientific outputs including publications, citations, dissertations, and patents, as well as with biographic data on researchers scraped from the web and in databases. These data integrate with STAR METRICS administrative data on grant purchases and employment, which can in turn be linked to Longitudinal Employer-Household Dynamics (LEHD) Census data enabling individuals to be traced as they move across employers and start businesses. These data are then linked using cutting edge disambiguation/name-entity resolution, web scraping and entity extraction. This IRIS methodology is advancing the underlying computational sciences and creating more useful data for broader applications.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

Jessica K. Camp

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Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.

Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.

Christopher Brooks

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The basis of my work is to make the often invisible traces created by interactions students have with learning technologies available to instructors, technology solutions, and students themselves. This often requires the creation of new novel educational technologies which are designed from the beginning with detailed tracking of user activities. Coupled with machine learning and data mining techniques (e.g. classification, regression, and clustering methods), clickstream data from these technologies is used to build predictive models of student success and to better understand how technology affords benefits in teaching and learning. I’m interested in broadly scaled teaching and learning through Massive Open Online Courses (MOOCs), how predictive models can be used to understand student success, and the analysis of educational discourse and student writing.