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Anna Kratz

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Anna Kratz, PhD, is Assistant Professor of Physical Medicine and Rehabilitation and the Center for Clinical Outcomes Development and Application (CODA) at the University of Michigan, Ann Arbor.

Dr. Kratz’s clinical research is focused on the characteristics and mechanisms of common symptoms (e.g. pain, fatigue, cognitive dysfunction) and functional outcomes in those with chronic clinical conditions. ¬†Using a combination of ambulatory measurement methods of physical activity (actigraphy), heart rate variability, galvanic skin response, and self-reported experiences, her research aims to overlay the patient’s day-to-day experience with physiological markers of stress, sleep quality, and physical activity. She utilizes a number of computational approaches, including multilevel statistical modeling, signal processing, and machine learning to analyze these data. The ultimate goal is to use insights from these data to design better clinical interventions to help patients better manage symptoms and optimize functioning and quality of life.

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Stilian A. Stoev

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Stilian Stoev’s research is in the area of applied probability and statistics for stochastic processes with emphasis on extremes, heavy tails, self-similarity, and long-range dependence. His recent theoretical contributions are in the area of max-stable processes, which is the class of processes emerging as a canonical model for the dependence in the extremes. This includes the representation, characterization, ergodicity, mixing, and prediction for this class of processes. Dr. Stoev is also working on applied problems in the area of computer network traffic monitoring, analysis and modeling. A recent joint project focuses on developing efficient statistical methods and algorithms for the visualization and analysis of fast multi-gigabit network traffic streams, which can help unveil the structure of traffic flows, detect anomalies and cyber attacks in real-time. This involves advanced low-level packet capture, efficient computation and rapid communication of summary statistics using non-relational data bases. More broadly, Dr. Stoev’s research is motivated by large-scale and data intensive applied problems arising in the areas of:

  1. environmental, weather and climate extremes.
  2. insurance and finance.
  3. Internet traffic monitoring, modeling and prediction.
Hash-binned array of 10+Gbps traffic stream measured at Merit Network. Bin (i,j) corresponds to traffic intensity in bytes of the data transferred from source IPs hashed in bin i with corresponding destination IPs hashed in bin j. The picture corresponds to a 10 second aggregation period. Bright horizontal lines indicate server-type communication from one bin to many, while unusual vertical lines are indicative of distributed denial of service (DDoS) type many-to-one attacks. The data were obtained using the PF_RING module in zero-copy mode, which by-passes the OS kernel and processes all packets passing through the interface. These and related statistical summaries derived via a recently developed AMON (All packet MONintoring) framework allows for a near-instantaneous visualization and automatic detection of structural changes in the network traffic conditions.

Hash-binned array of 10+Gbps traffic stream measured at Merit Network. Bin (i,j) corresponds to traffic intensity in bytes of the data transferred from source IPs hashed in bin i with corresponding destination IPs hashed in bin j. The picture corresponds to a 10 second aggregation period. Bright horizontal lines indicate server-type communication from one bin to many, while unusual vertical lines are indicative of distributed denial of service (DDoS) type many-to-one attacks.
The data were obtained using the PF_RING module in zero-copy mode, which by-passes the OS kernel and processes all packets passing through the interface. These and related statistical summaries derived via a recently developed AMON (All packet MONintoring) framework allows for a near-instantaneous visualization and automatic detection of structural changes in the network traffic conditions.

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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.

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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.