I am currently active in both the GeoVISTA Center and the Human Factors in GIScience Lab at Penn State. I recently finished a two-year appointment as a two-year trainee in the NSF Interdisciplinary Graduate Education, Research, and Training (IGERT) program on Big Data and Social Science. Visit those webpages for more information about current projects and members.

Here is a link to my CV: SamStehleCV2016

Masters Thesis

  • PATTERN MATCHING VIA SEQUENCE ALIGNMENT: ANALYZING SPATIO-TEMPORAL PATTERNS AND THEIR DISTANCES

    Sequential alignment is a technique that originated in biology to compare the similiarity of two (or more) proteins based on their amino acid composition. The sequential composition of events in time makes this technique very applicable to temporal analysis. I plan to implement, manipulate, and improve sequence alignment methods as they apply to spatio-temporal properties in event data. Here's a link to the abstract: StehleThesisAbstract

    For your sequence alignmnent browsing pleasure, here are some key articles:

  • The first article introducing the sequence alingment technique. Needleman, S. B., & Wunsch, C. D. (1969). A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins. J. Mol. Biol., 48, 443-453.

    A rare example of sequence alignment in a geographical context. Shoval, N., & Isaacson, M. (2007). Sequence Alignment as a Method for Human Activity Analysis in Space and Time. Annals of the Association of American Geographers, 97(2), 282-297.

    Some great visualization examples of aligned sequences. Albers, D., Dewey, C., & Gleicher, M. (2011). Sequence Surveyor: Leveraging for Scalable Genomic Alignment Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2392-2401.

    Other Current Projects

  • Mapping Moods: Geo-Mapped Sentiment Analysis During Hurricane SandyCollaborative work with IST at Penn State and CSE at North Texas. To be presented at ISCRAM 2014
  • STempo interactive environment for analyzing patterns in space-time data more info
  • Past Work (for more up-to-date projects, see my CV)

  • "Cognitive Approach to Feature Generalization at Multiple Scales" collaborative work with J. Smith and J. Yang
  • Presented at AAG 2012 New York in the Cognition, Behavior, Representation IV: Reasoning about Space session

  • "Improving Forecasts of International Events of Interest" collaborative work with B. Arva, J. Beieler, B. Fisher, G. Lara, P. A. Schrodt, W. Song, and M. Sowell
  • Paper presented by Dr. Schrodt at European Political Science Association Annual General Conference, Barcelona, Spain, June 20-22

  • Various projects through The DIGIT Lab at the University of Utah
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