Calendar
During the fall 2018 semester, the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs have merged their seminar/colloquium series where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public. This series takes place on Fridays from 3-4:30 in Center for Social Complexity Suite which is located on the third floor of Research Hall.
If you would like to join the seminar mailing list please email Karen Underwood.
RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES
Ben Intoy
Full Stack Developer
Dan Baeder
Data Scientist
Deloitte Consulting LLP
Massive-Scale Models of Urban Infrastructure and Populations
Friday, September 06, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Abstract:
As the world becomes more dense, connected, and complex, it is increasingly difficult to answer “what-if” questions about our cities and populations. Most modeling and simulation tools struggle with scale and connectivity. We present a new method for creating digital twin simulations of city infrastructure and populations from open source and commercial data. We transform cellular location data into activity patterns for synthetic agents and use geospatial data to create the infrastructure and world in which these agents interact. We then leverage technologies and techniques intended for massive online gaming to create 1:1 scale simulations to answer these “what-if” questions about the future.
Bios:
Ben Intoy is a full stack developer at Deloitte Consulting LLP. He received his PhD in Physics at Virginia Tech in 2015 where he used high throughput computing simulations to study stability properties of cyclically competing species in varying spatial dimensions. Ben then went to the University of Minnesota, Twin Cities campus, as a postdoctoral research associate where he used tools he learned in his PhD to abstractly study the origin of life on earth and the probability of finding life elsewhere in the universe. In fall 2018 Ben went to the Deloitte Arlington VA Office to work on the FutureScape project (www.futurescape.ai).
Dan Baeder is a data scientist at Deloitte Consulting LLP, and has been on the FutureScape project since joining the firm last year. While at Deloitte, Dan has focused on the use of cellular phone geolocation data for the development of synthetic traffic models, as well as the application of geospatial analysis techniques to human behavior modeling. He is a noted R-phile in a sea of Python users. Dan received an MS in Public Policy and Management with a focus on data analytics from Carnegie Mellon University in 2018.
Research Colloquium on Computational Social Science/Data Science
Kim McEligot
PhD Candidate, Department of Systems Engineering and Operations Research
George Mason University
Sea Bright, NJ Reconstructed:
Agent-Based Protection Theory Model Responses to Hurricane Sandy
Friday, September 13, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Vadim Sokolov
Assistant Professor
Department of Systems Engineering and Operations Research
George Mason University
Dimensionality Reduction for Agent Based Models
Friday, September 20, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Bayesian algorithms such as Markov Chain Monte Carlo or Bayesian optimization can quickly become computationally prohibitive or even infeasible for high dimensional ABM problems. In many applications, however, the underlying dynamics of an ABM typically can be represented in a lower dimensional space. We will review existing linear and nonlinear dimensionality reduction methods, such as Laplacian eigenmaps and restricted Boltzmann machines. Further, we will present some new results for nonlinear dimensionality techniques based on deep learning models. We will demonstrate our approach in the context of Bayesian optimization algorithms applied to a transportation agent-based model. Finally, we discuss directions for future research.
Bio:
Vadim Sokolov is an assistant professor in the Systems Engineering and Operations Research Department at George Mason University. He works on building robust solutions for large scale complex system analysis, at the interface of simulation-based modeling and statistics. This involves, developing new methodologies that rely on deep learning, Bayesian analysis of time series data, design of computational experiments and development of open-source software that implements those methodologies. Inspired by an interest in urban systems he co-developed mobility simulator called Polaris that is currently used for large scale transportation networks analysis by both local and federal governments. Prior to joining GMU he was a principal computational scientist at Argonne National Laboratory, a fellow at the Computation Institute at the University of Chicago and lecturer at the Master of Science in Analytics program at the University of Chicago.
Research Colloquium on Computational Social Science/Data Science
AnaMaria Berea, PhD
Blue Marble Space Institute of Science Research Scientist
Visiting Research Assistant Professor
University of Central Florida
Exploring what is universally possible for life with a wealth of computational methods
Friday, September 27, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Amira Al-Khulaidy
with Valentin Vergara
CSS PhD Students
George Mason University
Corruption and the effects of influence within social networks: An agent-based model of the “Lava Jato” scandal.
Friday, October 04, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Corruption, and more specifically corruption in Latin America, is a complex phenomenon which is affected by politics, social structures and institutions, as well as individual behaviors. The Lava Jato is a large-scale example of corruption in Brazil. As of early 2019, there is still an ongoing investigation surrounding what has been lauded as the largest corruption scheme in Latin America. Advances in data analysis, computation, and social networks have allowed progress to be made with these types of investigations. The Lava Jato case has been a clear example of how breaking up social networks and understanding the extent of crime and individual corruption has revealed webs of corruption that have influenced politics, as well as hindered economic developed in Brazil. The several layers of interactions between individuals and institutions can be difficult to grasp and understanding the patterns and relationships within complex large-scale phenomena, such as corruption can seem impossible. Agent-based models can help with understanding these complex behaviors and systems. By capturing the patterns and gaining a better understanding of how corruption emerges and is manifested, we can help inform policy, as well as create better tools and methods for crime prevention and detection.
Bio:
Amira Al-Khulaidy – Computational Social Science PhD student, MEd. in Education, University of Virginia Valentin Vergara – Computational Social Science PhD student, MSc. Natural Resource Economics, Universidad de Concepción, Chile
Research Colloquium on Computational Social Science/Data Science
Neil Johnson
Professor of Physics
George Washington University
Slaying the Online Hydra of Hate, Distrust and anti-Science
Friday, October 11, 2019 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
[2] N.F. Johnson et al., New online ecology of adversarial aggregates: ISIS and beyond, Science 352, 1459 (2016)
Research Colloquium on Computational Social Science/Data Science
Fahad Aloraini
CSS PhD student
Modeling Solar-Panel Technology adoption in Austin: a test of the power of integrating GIS and Cognitive modeling.
Friday, October 18, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Joint CDS, Math, and Physics Seminar
Mason Porter
Mathematics Professor
University of California, Los Angeles
Topological Data Analysis of Spatial Complex Systems
Thursday, October 24, 1:00 p.m.
Exploratory Hall, Room 3301, Fairfax Campus
All are welcome to attend.
Research Colloquium on Computational Social Science/Data Science
Katherine Anderson
Visiting Assistant Professor
Department of Informatics and Networked Systems
School of Computing and Information
University of Pittsburgh
Friday, October 25, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
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
Christine Harvey
Bachelor of Science, Stockton University, 2011
Master of Science, Stockton University, 2013
Modeling, Simulation, and Analysis of the US Organ Transplant System
Tuesday, October 29, 2:00 p.m.
Exploratory Hall, Room 3301
All are invited to attend.
Committee
Robert Weigel, Dissertation Director
Andrew Crooks, Committee Chair
Hamdi Kavak
James Gentle
Analysis, modeling, and simulation of organ transplantation and donation can enhance the understanding of this complex system and guide strategic policy improvements. Four major research questions are addressed in this work: (1) how can we further enable data-driven research of the transplant system for future scientists?; (2) what demographic factors influence donations and access to transplantation?; (3) how do laws and policies affect organ donations?; and (4) how do certain patient advantages impact the overall system as well as those lacking advantages?
A data pipeline and associated software were developed and published that address how to further data-driven research of the transplant system for future scientists. This software simplifies access to and analysis of data from proprietary Organ Procurement and Transplantation Network (OPTN) Standard Transplant Analysis and Research (STAR) files to an open-source database format. These files contain data on every organ donor, waitlist registrant, and transplant recipient since 1987 in the US. This data pipeline directly facilitated the next phase of research which involved performing an analysis of the transplant system using this dataset. The exploratory data analysis scales transplant data to the relative populations to gain a better understanding of the differences between demographic groups and reveals important differences across education levels, gender, race, and ethnicity.
Demographic factors influencing organ donation and access to transplants are analyzed in this work through exploratory visualizations and predictive modeling. A visual exploratory analysis is presented which examines demographic features of organ donors and highlights differences in intersectional data across the population of donors compared to the relative population described by the US Census. Additionally, a random forest model is used to determine the features of patients on the waitlist for a kidney transplant to determine if certain attributes may inadvertently drive the allocation system. This model predicts patient outcomes based on features represented in the model with an accuracy above the zero-rule baseline. The analysis found that patient age, year of listing, body weight, and zip code are important factors in determining a patient’s outcome – other demographic factors such as race and gender were not important prediction features.
State and local laws, policies, and their impact on organ donation are evaluated through a statistical analysis that compares donations after the implementation of a policy to areas without the policy implementation. A database of state and local laws and policies and the years of implementation was developed to compare donations across the country. The results demonstrated that some policies can be correlated with a change in donation, but only for certain demographic subgroups in a population.
Finally, I built discrete event simulation models of a representative patient population to determine the impact of changes to the transplant system that can not be easily demonstrated in the real world. A transplant process model was developed to determine how increasing living and deceased donation overall and within racial sub-groups would impact the number of donors each year. Additionally, an agent-based queuing model was used to understand the impact of allowing patients to register within more than one area. This model provides a valuable tool for examining policy changes that shows the global and local impacts of multiple listing. The analysis found that multiply listed patients have improved access to transplants and are less likely to die while waiting for a transplant.
