Dr. Morckel uses spatial and statistical methods to examine ways to improve quality of life for people living in shrinking, deindustrialized cities in the Midwestern United States. She is especially interested in the causes and consequences of population loss, including issues of vacancy, blight, and neighborhood change.
My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.
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.
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 â€œwhatâ€ and â€œwhereâ€ micro-scale conditions affect human sentiment and hence wayfinding ability, movement patterns, and travel mode-choices.
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)
Dr. Zhu’s group conducts research on various topics, ranging from foundational methodologies to challenging applications, in data science. In particular, the group has been investigating the fundamental issues and techniques for supporting various types of queries (including range queries, box queries, k-NN queries, and hybrid queries) on large datasets in a non-ordered discrete data space. A number of novel indexing and searching techniques that utilize the unique characteristics of an NDDS are developed. The group has also been studying the issues and techniques for storing and searching large scale k-mer datasets for various genome sequence analysis applications in bioinformatics. A virtual approximate store approach to supporting repetitive big data in genome sequence analyses and several new sequence analysis techniques are suggested. In addition, the group has been researching the challenges and methods for processing and optimizing a new type of so-called progressive queries that are formulated on the fly by a user in multiple steps. Such queries are widely used in many application domains including e-commerce, social media, business intelligence, and decision support. The other research topics that have been studied by the group include streaming data processing, self-management database, spatio-temporal data indexing, data privacy, Web information management, and vehicle drive-through wireless services.
Exploring properties of spatial-econometric methods for valid estimation of interdependent processes, i.e., estimation of spatially & spatiotemporally dynamic responses, primarily in political science and political economy applications. Specific applications have included international tax-competition and national tax & other economic policies, U.S. inter-state policy diffusion, the (possibly contagious) spread of intra- and inter-state conflict.
Jieping Ye, PhD, is Associate Professor of Computational Medicine and Bioinformatics in the Medical School at the University of Michigan, Ann Arbor.
The Ye Lab has been conducting fundamental research in machine learning and data mining, developing computational methods for biomedical data analysis, and building informatics software. We have developed novel machine learning algorithms for feature extraction from high-dimensional data, sparse learning, multi-task learning, transfer learning, active learning, multi-label classification, and matrix completion. We have developed the SLEP (Sparse Learning with Efficient Projections) package, which includes implementations of large-scale sparse learning models, and the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package, which includes implementations of state-of-the-art multi-task learning models. SLEP achieves state-of-the-art performance for many sparse learning models, and it has become one of the most popular sparse learning software packages. With close collaboration with researchers at the biomedical field, we have successfully applied these methods for analyzing biomedical data, including clinical image data and genotype data.
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, nonlinear variants such as low-dimensional algebraic variety models, 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.
Jenna Wiens, PhD, is Assistant Professor of Computer Science and Engineering (CSE) in the College of Engineering at the University of Michigan, Ann Arbor.
Prof. Wiens currently heads the MLD3 research group. Her primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, she is particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of her research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. Her work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, Prof. Wiens has been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US. In addition to her research in the healthcare domain, she also spends a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA.
My substantive research interest is to understand the role of geography in shaping population health. Towards this end, my methodological and data science interests are twofold. First, I seek to develop and apply spatial statistical methods to model individual- and area-level health and diseases by using survey data and government statistics. Second, in light of the advance in GIS techniques and the increasingly accessible spatial data from various sources, I am exploring new approaches to integrate traditional geo-referenced survey data with non-traditional spatial data (e.g., remote sensing data, satellite data, Google search) to reduce measurement errors in demographic health research.