Calendar
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Dates for the 2017-18 Academic Year
September 27th
October 10th
October 25th
November 14th [Cancelled]
November 29th
December 12th
January 16th
January 31st
February 13th
February 28th
March 20th
April 3rd
April 18th
May 8th
May 23rd
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Kirk Borne, PhD
Principal Data Scientist and an Executive Advisor
Booz Allen Hamilton
Some Interesting Applications of Machine Learning Algorithms
Tuesday, October 10, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: I will present a variety of atypical use cases and applications (in science and in business) of some typical textbook machine learning algorithms, including regression, clustering analysis, association mining, time series analysis, and network analysis.
Dr. Borne advises and consults with numerous organizations, agencies, and partners in the use of data and analytics for discovery, decision support, and innovation. Previously, he was Professor at George Mason University (GMU) for 12 years in the CSI and CDS programs, where he did research, taught, and advised students in data science. Prior to that, Dr. Borne spent nearly 20 years supporting data systems activities on NASA space science research programs, including a role as NASA’s Data Archive Project Scientist for the Hubble Space Telescope.
Recently, Dr. Borne was ranked #2 worldwide among all Big Data experts to follow. http://ipfconline.fr/blog/2017/05/22/fine-list-of-50-top-world-big-data-experts-to-follow-in-2017-with-moz-social-score/
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University
Daniel Pulido
Bachelor of Science, Boston University, 1998
Master of Science, Worcester Polytechnic Institute, 2003
Self-Similar Spin Images for Point Cloud Matching
Friday, October 13, 2017, 10:00 a.m.
Research Hall, Room 162
All are invited to attend.
Committee
Anthony Stefanidis, Dissertation Director
Estela Blaisten-Barojas
Arie Croitoru
Juan Cebral
The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. Simultaneously, the growth of other forms of geospatial data (e.g., crowdsourced Web 2.0 data) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. However, combining data from disparate sources requires overcoming numerous technical challenges in order to generate products that mitigate their respective disadvantages and combine their advantages.
Therefore, this dissertation addresses the problem of fusing two point clouds from potentially different sources by considering two specific problems: scale matching and feature matching. To address the problem of feature matching we develop a novel feature descriptor referred to as “Self-Similar Spin Images” which combine the concept of local self-similarity with the descriptive power of Spin Images. To address the problem of scale matching we develop a novel scale detection metric referred to as “Self-Similar Keyscale” which analyzes the self-similarity of two point clouds to identify a characteristic scale to match them. Finally, we develop a novel change detection method as a sample use case of the developed matching techniques.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Annetta Burger, PhD Candidate
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Organizing Theories for Disaster Study in Computational Social Science
Friday, October 13, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
Based on evolving understandings of the complex, interconnected characteristics of disasters and social adaptations we propose to organize disaster theories in the interdisciplinary context of complex adaptive systems (CAS). Within this context disasters can be understood to consist of three sets of interacting systems, the socio-ecological system, the system of collective, social behavior, and the individual actor’s cognitive system. Ongoing dynamic forces within each set periodically build to disrupt events and cause failures in the overall system. In this talk, we will explore the dominant theory and frameworks in disaster studies from the perspective of these sets, demonstrate how they explain disasters, and discuss how Computational Social Science methodologies can support theory-building. By identifying evolving understandings and research questions co-located in disaster theory and CAS we find that the CAS features of heterogeneity, flows, interacting subsystems, emergence from self-organization and bottom-up processes, and adaptation and learning are integral to disaster studies.
Join the College of Science for an exclusive Mason Alumni opening reception of the Potomac Science Center.
This new facility will be primarily occupied by the departments of Environmental Science and Policy, and Geography and Geoinformation Science. The Potomac Science Center will host eight state of the art research lab suites to be occupied by professors from Environmental Science and Policy, Chemistry, and Atmosphere, Oceanic, and Earth Sciences, as well as an enhanced geospatial intelligence center. In addition to scientific research, the facility is equipped with teaching labs and space for environmental science K-12 programs and public outreach through the Potomac Environmental Research and Education Center (PEREC).
Tour the new facility, hear from leading researchers in a variety of scientific fields, and enjoy an evening with fellow Mason alumni. Heavy hors d’oeuvres and a cash bar will be available.
Oktoberfest meets trivia night in this fun, family-friendly college celebration. Join the College of Humanities and Social Sciences and College of Science Alumni Chapters as we enjoy tastings from Mason alumni-owned breweries.
Network, reconnect, receive Mason themed prizes and learn about the sciences behind these frothy beverages. Save $5 when you register for the Sciences of Beer + Green and Gold Bash package before Friday, Sept. 22! At registration, if you select the Sciences of Beer + Green and Gold Bash package, then you do not need to register separately for the Green and Gold Bash.
Cost:
Early Bird: $20 (available through Sept. 6);
General: $25;
Non-Drinker: $10;
Day-Of: $30 (credit cards only at the door)
Discounts:
Recent alumni (2017) and faculty/staff who are also alumni automatically receive $5 off at registration.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Stephen Lockett, PhD
Principal Scientist and Director of the Optical Microscopy and Analysis Laboratory
Frederick National Laboratory for Cancer Research, National Cancer Institute
Transitioning Microscopy from 2D to 3D for Tumor Biology and Pathology
Monday, October 16, 4:30-5:45
Exploratory Hall, Room 3301
Abstract: Discerning the 3D context of individual cells in tissue is fundamental for understanding tissue development, homeostasis in healthy adult organs and tumor emergence. Determining this context, which requires 3D microscopic imaging to 100s of microns depth into tissues, is hampered in multiple ways: (1) penetration of fluorescent dyes deep into tissue, (2) absorption and scattering of light, (3) sufficiently high speed image acquisition and (4) automated image analysis, because datasets are too large for human visual interpretation. Furthermore, in the case of tumors, standard practice in clinical pathology is to only image a thin slice about 5 microns thick, which has several limitations: there is significant inter-observer variability in the diagnosis, the slice can miss the tumor and determining the spatial relationships of cells to each other is incomplete. To address these limitations, we have evaluated tissue clearing protocols, which reduce scattering and spherical aberration, and we have achieved high spatial resolution imaging up 0.35 mm depth utilizing spinning disk confocal microscopy or lightsheet microscopy for high speed image acquisition. The image analysis tasks for 3D images are much more demanding than 2D images for the following reasons: (1) visualization of the entirety of each object (cell or cell nucleus) is not possible in a single display, (2) purely interactive segmentation is impractical even for one object, (3) automatic algorithms are imperfect, and (4) in the case of basic research relatively few cells are imaged increasing the need to analyze each cell as accurately as possible. Consequently, we are working on tools that merge automatic segmentation algorithms, computer vision and human annotation for delineating objects in 3D images. We have developed a graphcut-based algorithm for finding the globally-optimal surface delineating each manually seeded nucleus. The algorithm is restricted to point convex objects, which is generally satisfactory for nuclei, but not for entire cells that can have arbitrary shapes. For whole cell segmentation in 3D, our approach is slice-by-slice based. In the first 2D image slice (approximately through the center of the cell), the user draws an approximate 2D border and the border is automatically optimized using active contour modeling. This optimized border then serves as the approximate border for adjacent slices, which are in turn automatically optimized. The method is accurate except where the cell surface is in the plane of the slice, so a next step is to facilitate segmenting each cell in different orientations. We are starting to utilize this technology to understand the complex interplay between cancer and normal cells, particularly immune system cells and supporting mesenchyme leading to tumor growth or regression in the case of treatment. The research was funded by NCI Contract No. HHSN261200800001E and supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research.
Stephen Lockett received the Ph.D. degree from the Department of Medicine, Birmingham University, England. He has published over 120 research papers and has received several international awards. His research interests include fluorescence microscopy and the development of analysis software for extracting quantitative information from images.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Peter Revay, Ph.D. Candidate
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Modeling the Co-Evolution of Culture, Signs and Network Structure: Theory and Applications
Friday, October 20,3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
ABSTRACT: I focus on the drivers of diffusion and adoption of cultural traits, such as values, beliefs, and behaviors. I adopt an evolutionary view of cultural dynamics. I use concepts from dual-inheritance theories of cultural evolution to develop and test an agent-based model capable of simulating the changing distributions of cultural traits in a large population of actors over the course of prolonged periods of time. Particularly, I pay close attention to the mechanisms of indirectly biased transmission of traits and guided variation, which are both hypothesized to be significant aspects of cultural dynamics. Indirectly biased transmission consists of the adoption of specific trait variants on the basis of possession of initially unrelated external markers. Guided variation is then individual adaptation driven by self-exploration.
Furthermore, I make use of large publicly available datasets to validate my models. The first one of these is the database of bill co-authorship in the U.S. House of Representatives from 1973 to 2008. The other is a comprehensive dataset of scientific co-authorship in various disciplines stretching back for over a century.
The results show that cultural evolution models based on indirectly biased transmission and guided variation are suitable to explaining the dynamics of various complex social networks.