Jing Sun

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My areas of interest are control, estimation, and optimization, with applications to energy systems in transportation, automotive, and marine domains. My group develops model-based and data-driven tools to explore underlying system dynamics and understand the operational environments. We develop computational frameworks and numerical algorithms to achieve real-time optimization and explore connectivity and data analytics to reduce uncertainties and improve performance through predictive control and planning.

Nicole Seiberlich

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My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.

Sol Bermann

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I am interested in the intersection of big data, data science, privacy, security, public policy, and law. At U-M, this includes co-convening the Dissonance Event Series, a multi-disciplinary collaboration of faculty and graduate students that explore the confluence of technology, policy, privacy, security, and law. I frequently guest lecture on these subject across campus, including at the School of Information, Ford School of Public Policy, and the Law School.

Niccolò Meneghetti

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Dr. Niccolò Meneghetti is an Assistant Professor of Computer and Information Science at the University of Michigan-Dearborn.
His major research interests are in the broad area of database systems, with primary focus on probabilistic databases, statistical relational learning and uncertain data management.

Z. Tuba Suzer-Gurtekin

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Z. Tuba Suzer Gurtekin is an Assistant Research Scientist at the University of Michigan’s Institute for Social Research. Her research includes managing monthly surveys of consumer attitudes, expectations and behavior. Her published research focuses on methods to quantify nonresponse and measurement survey errors in probability and nonprobability sample surveys, and mixed-mode survey design and inference. Her research experience has included development of alternative sample, methodology and questionnaire designs, data collection and analysis methods for a general population in parallel survey modes. She also teaches Survey Sampling for Clinical Research at the University of Michigan’s Clinical Research Design and Statistical Analysis program (OJOC CRDSA).

Birhanu Eshete

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I study cybercrime using data-driven methods to analyze, characterize, and measure the infrastructure and modus operandi used by criminal activities on the Internet. In particular, I focus on collection, analysis, and semantic characterization of cyber threat intelligence that comes in many shapes and forms (e.g., natural language, network traffic, system audit logs). The ultimate goal is to learn insights that will inform decisions on building robust defense against online criminal activities that involve threats such as ransomware, exploit kits, and botnets. To achieve these goals, I find graph theory and analytics, machine learning (deep learning), longitudinal analysis, and causality inference to be the natural methods. I also study the training and deployment of cyber threat classification/prediction systems in adversarial settings.

From behavioral fingerprinting and detection of cybercrime toolkits to analytics and detection of online cyber threats; from semantic characterization of cyber threat intelligence to detection and forensics of advanced cyber attacks, machine learning, graph theory and analytics, graph isomorphism, and causal inference serve as the core ingredients to build robust defense against cyber threats.

John E Marcotte

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John E. Marcotte, PhD is a statistician and data security expert. His research concerns data sharing, data security, data management, disclosure, health policy, nursing staffing and patient outcomes. He has over 25 years of experience implementing computing systems and performing quantitative analysis. During his career, Marcotte has served as a quantitative researcher, biostatistician, data archivist, data security officer and computing director. Among Marcotte’s statistical fortes are linear and logistic regression, survival analysis and sampling while his computing specialties include secure systems, high performance systems and numerical methods. He has collaborated with social and natural scientists as well as nurses and physicians. Marcotte regularly presents at professional conferences and contributes to invited panels on data security and disclosure. He has formal training in Demography, Statistics and Computer Science.

Research Data Security Options

Aaron A. King

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The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.

Nicholson Price

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I study how law shapes innovation in the life sciences, with a substantial focus on big data and artificial intelligence in medicine. I write about the intellectual property incentives and protections for data and AI algorithms, the privacy issues with wide-scale health- and health-related data collection, the medical malpractice implications of AI in medicine, and how FDA should regulate the use of medical AI.

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.