Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Towards Data-Driven Large Scale Scientific Visualization and Exploration(2013) Ip, Cheuk Yiu; Varshney, Amitabh; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Technological advances have enabled us to acquire extremely large datasets but it remains a challenge to store, process, and extract information from them. This dissertation builds upon recent advances in machine learning, visualization, and user interactions to facilitate exploration of large-scale scientific datasets. First, we use data-driven approaches to computationally identify regions of interest in the datasets. Second, we use visual presentation for effective user comprehension. Third, we provide interactions for human users to integrate domain knowledge and semantic information into this exploration process. Our research shows how to extract, visualize, and explore informative regions on very large 2D landscape images, 3D volumetric datasets, high-dimensional volumetric mouse brain datasets with thousands of spatially-mapped gene expression profiles, and geospatial trajectories that evolve over time. The contribution of this dissertation include: (1) We introduce a sliding-window saliency model that discovers regions of user interest in very large images; (2) We develop visual segmentation of intensity-gradient histograms to identify meaningful components from volumetric datasets; (3) We extract boundary surfaces from a wealth of volumetric gene expression mouse brain profiles to personalize the reference brain atlas; (4) We show how to efficiently cluster geospatial trajectories by mapping each sequence of locations to a high-dimensional point with the kernel distance framework. We aim to discover patterns, relationships, and anomalies that would lead to new scientific, engineering, and medical advances. This work represents one of the first steps toward better visual understanding of large-scale scientific data by combining machine learning and human intelligence.Item Causal Inference with Group-Based Trajectories and Propensity Score Matching: Is High School Dropout a Turning Point?(2006-04-28) Sweeten, Gary Allen; Bushway, Shawn D; Criminology and Criminal Justice; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Life course criminology focuses on trajectories of deviant or criminal behavior punctuated by turning point events that redirect trajectories onto a different path. There is no consensus in the field on how to measure turning points. In this study I ask: Is high school dropout a turning point in offending trajectories? I utilize two kinds of matching methods to answer this question: matching based on semi-parametric group-based trajectory models, and propensity score matching. These methods are ideally suited to measure turning points because they explicitly model counterfactual outcomes which can be used to estimate the effect of turning point events over time. It has been suggested that dropout is the end result of a process of disengagement from school. In order to assess the effect of the event of dropout, it is necessary to separate dropout from the processes that lead to it. The extent to which this is accomplished by matching is assessed by comparing dropouts to matched non-dropouts on numerous background characteristics. As such, it is desirable to use a wide range of measures to compare the two groups. I use the National Longitudinal Survey of Youth 1997 to address this question. Delinquency is measured in two ways: a six-item variety scale and a scale based on a graded-response model. Dropout is based on self-reports of educational attainment supplemented with official transcripts provided by high schools. Because of the breadth of topics covered in this survey, it is very well-suited to matching methods. The richness of these data allows comparisons on over 300 characteristics to assess whether the assumptions of matching methods are plausible. I find that matching based on trajectory models is unable to achieve balance in pre-dropout characteristics between dropouts and non-dropouts. Propensity score matching successfully achieves balance, but dropout effects are indistinguishable from zero. I conclude that first-time dropout between the ages of 16 and 18 is not a turning point in offending trajectories. Implications for life course criminology and dropout research are discussed.