Kathleen M Bergen

By |

Kathleen M Bergen, PhD, is Associate Research Scientist in the School for Environment and Sustainability at the University of Michigan, Ann Arbor. Dr. Bergen currently has interim administrative oversight of the SEAS Environmental Spatial Analysis Laboratory (ESALab) and is interim Director of the campus-wide Graduate Certificate Program in Spatial Analysis.

Prof. Bergen¬†works in the areas of human dimensions of environmental change; remote sensing, GIS and biodiversity informatics; and environmental health and informatics. Her focus is on combining field and geospatial data and methods to study the pattern and process of ecological systems, biodiversity and health. She also strives to build bridges between science and social science to understand the implications of human actions on the social and natural systems of which we are a part. She teaches courses in Remote Sensing and Geographic Information Systems. Formerly she served as a founding member of the UM LIbrary’s MIRLYN implementation team, directed the University Map Collection, and set up the M-Link reference information network.

Danny Forger

By |

Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.

Omid Dehzangi

By |

Omid Dehzangi, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, at the University of Michigan, Dearborn.

Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to the continuous personalized data will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines into data science which is the methodological terminology for data collection, data management, data analysis, and data visualization. Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytic algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary. Embedded Processing Platform Design The majority of my work concentrates on designing wearable embedded processing platforms in order to shift the conventional paradigms from hospital-centric healthcare with episodic and reactive focus on diseases to patient-centric and home-based healthcare as an alternative segment which demands outstanding specialized design in terms of hardware design, software development, signal processing and uncertainty reduction, data analysis, predictive modeling and information extraction. The objective is to reduce the costs and improve the effectiveness of healthcare by proactive early monitoring, diagnosis, and treatment of diseases (i.e. preventive) as shown in Figure 1.

Figure 1. Embedded processing platform in healthcare

Jie Shen

By |

Jie Shen, PhD, is Professor of Computer and Information Science at the University of Michigan, Dearborn.

Prof. Shen’s research interests are¬†in the digital diagnosis of material damage based on sensors, computational science and numerical analysis with large-scale 3D computed tomography data: (1) Establishment of a multi-resolution transformation rule of material defects. (2) Design of an accurate digital diagnosis method for material damage. (3) Reconstruction of defects in material domains from X-ray CT data . (4) Parallel computation of materials damage. His team also conducted a series of studies for improving the quality of large-scale laser scanning data in reverse engineering and industrial inspection: (1) Detection and removal of non-isolated Outlier Data Clusters (2) Accurate correction of surface data noise of polygonal meshes (3) Denoising of two-dimensional geometric discontinuities.

Processing and Analysis of 3D Large-Scale Engineering Data

Processing and Analysis of 3D Large-Scale Engineering Data

Pascal Van Hentenryck

By |

Pascal Van Hentenryck, Phd, is the Seth Bonder Collegiate Professor of Industrial and Operations Engineering, Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

His research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. He applies evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

Raj Rao Nadakuditi

By |

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.

Laura Balzano

By |

Laura Balzano, PhD, is Assistant Professor in the Electrical Engineering and Computer Science department at the University of Michigan, Ann Arbor.

Professor Balzano and her students investigate problems in statistical signal processing and optimization, particularly dealing with large and messy data. Her applications typically have missing, corrupted, and uncalibrated data as well as heterogeneous data in terms of sensors, sensor quality, and scale in both time and space. Her theoretical interests involve classes of non-convex problems that include Principal Components Analysis (or the Singular Value Decomposition) and many interesting variants such as PCA with sparse or structured principal components, orthogonality and non-negativity constraints, and even categorical data or human preference data. She concentrates on fast gradient methods and related optimization methods that are scalable to real-time operation and massive data. Her work provides algorithmic and statistical guarantees for these algorithms on the aforementioned non-convex problems, and she focuses carefully on assumptions that are realistic for the relevant applications. She has worked in the areas of online algorithms, real-time computer vision, compressed sensing and matrix completion, network inference, and sensor networks.

Real-time dynamic background tracking and foreground separation. At time t = 101, the virtual camera slightly pans to right 20 pixels. We show how GRASTA quickly adapts to the new subspace by t = 125. The first row is the original video frame; the middle row is the tracked background; the bottom row is the separated foreground.

Real-time dynamic background tracking and foreground separation. At time t = 101, the virtual camera slightly pans to right 20 pixels. We show how GRASTA quickly adapts to the new subspace by t = 125. The first row is the original video frame; the middle row is the tracked background; the bottom row is the separated foreground.

Matthew Johnson-Roberson

By |

Matthew Johnson-Roberson, PhD, is Assistant Professor of Naval Architecture and Marine Engineering and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, the University of Michigan, Ann Arbor.

The increasing economic and environmental pressures facing the planet require cost-effective technological solutions to monitor and predict the health of the earth. Increasing volumes of data and the geographic dispersion of researchers and data gathering sites has created new challenges for computer science. Remote collaboration and data abstraction offer the promise of aiding science for great social benefit. Prof. Johnson-Roberson’s research in this field has been focused on developing novel methods for the visualization and interpretation of massive environments from multiple sensing modalities and creating abstractions and reconstructions that allow natural scientists to predict and monitor the earth through remote collaboration. Through the promotion of these economically efficient solutions, his work aims to increase access to hundreds of scientific sites instantly without traveling. In undertaking this challenge he is constantly aiming to engage in research that will benefit society.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.

Kevin Ward

By |

Kevin Ward, MD, is Professor of Emergency Medicine in the department of Emergency Medicine in the University of Michigan Medical School.

Dr. Ward is the director of the Michigan Center for Integrative Research in Critical Care (MCIRCC) and a new Medical School-wide innovation program, Fast Forward Medical Innovation. He has successfully developed monitors for measuring tissue oxygenation, volume status, redox potential, coagulation monitoring, image and physiologic signal analysis, and other physiologic parameters leading teams of engineers, basic scientists, and clinicians, bridging the translation gap.