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Samuel K Handelman

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Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

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 J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics.   He is a health services researcher working extensively with public health information systems and large administrative claims databases.  

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.  Kevin is currently collaborating with Michigan State University on the design and development of the Flint Lead Exposure Registry (FLExR) information architecture.

Kevin’s 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 (MDHHS), 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.  Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records and MCIR.   He is conducting a similar statewide evaluation as new messaging protocols are adopted by electronic laboratory systems for reporting blood lead testing results to MDHHS.

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.

Suleyman Uludag

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My research spans security, privacy, and optimization of data collection particularly as applied to the Smart Grid, an augmented and enhanced paradigm for the conventional power grid. I am particularly interested in optimization approaches that take a notion of security and/or privacy into the modeling explicitly. At the intersection of the Intelligent Transportation Systems, Smart Grid, and Smart Cities, I am interested in data privacy and energy usage in smart parking lots. Protection of data and availability, especially under assault through a Denial-of-Service attacks, represents another dimension of my area of research interests. I am working on developing data privacy-aware bidding applications for the Smart Grid Demand Response systems without relying on trusted third parties. Finally, I am interested in educational and pedagogical research about teaching computer science, Smart Grid, cyber security, and data privacy.

This figure shows the data collection model I used in developing a practical and secure Machine-to-Machine data collection protocol for the Smart Grid.

This figure shows the data collection model I used in developing a practical and secure
Machine-to-Machine data collection protocol for the Smart Grid.

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

Rybarczyk, G., S. Banerjee, and M. Starking-Szymanski, and R. Shaker. (2018) “Travel and us: The impact of mode share on sentiment using geosocial media data and GIS” Journal of Location-Based Services (forthcoming)

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 research expertise is process evaluation. He has studied various healthcare processes, educational processes and healthcare economics. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight emergency room resource utilization for managing dental 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. After completing a masters degree at Harvard School of Public Health, Dr. Nalliah’s interests have expanded and he has studied various public health issues including sports injuries, poisoning, child abuse, motor vehicle accidents and surgical processes (like stem cell transplants, cardiac valve surgery and fracture reduction). 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 health education curriculum and practice.

Dr. Nalliah’s professional mission is to improve healthcare delivery systems and he is 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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Professor Owen-Smith conducts research on the collective dynamics of large scale networks and their implications for scientific and technological innovation and surgical care. He is the executive director of the Institution for Research on Innovation and Science (IRIS, http://iris.isr.umich.edu).  IRIS is a national consortium of research universities who share data and support infrastructure designed to support research to understand, explain, and eventually improve the public value of academic research and research training.

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.