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 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.
Research Colloquium on Computational Social Science/Data Science
Robert Axtell
Professor Computational Social Science PhD Program
Department of Computational and Data Sciences
George Mason University
Working with Heavy-Tailed Data: A Tutorial
Friday, November 01, 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
John Schuler
PhD Student
Department of Economics
George Mason University
The Econometrics of Prices in a Network Economy
Friday, November 08, 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
Dr. Seth Brown
Steam Solution LLC
National Municipal Stormwater Alliance
To Be or Not To Be: Introducing the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) Model
Friday, November 15, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
The use of incentives in stormwater programs is a common feature that is used to motivate private property owners as well as land developers to adopt specific types of stormwater management infrastructure at the site or parcel level. While incentives for land developers, such as reduced plan review time or reduced plan review fee for projects that utilize specific BMPs, such as green stormwater infrastructure (GSI), are helpful policies in driving implementation of innovative stormwater practices, this implementation is limited to land development activity. Approximately 75% of existing impervious cover is associated with land development activities that took place prior to federal legislation focused on urban stormwater runoff. The implication of this is that a majority of impervious cover across the U.S. discharges runoff that is either inadequately managed or not managed at all. Until we address these existing areas, impacts from these areas will continue to impact our waters. This is reflected by evolving regulations that require a certain amount of existing impervious cover to be retrofitted to provide stormwater management. Many cities, such as Milwaukee, Seattle and Atlanta, also have retention volume goals as part of a regulatory program as well as an effort to increase the resilience and sustainability of urban areas.
The motivation to retrofit existing impervious areas is a driver to retrofit both public and private lands. Public rights-of-way (ROWs) are often challenging to work within, and there is a limited amount of public ROW available. Overall, 60% of land in the U.S. is privately held, with large portions of these areas located in large public parks in Mountain Region states. The result is that many states have private land ownership rates at 80% or higher; clearly this is a need to find ways to locate urban stormwater retrofits on private lands.
The default method of incentivizing private land owners to adopt onsite stormwater infrastructure is a stormwater fee reduction according to the 2018 Black and Veatch Stormwater Utility Survey. The limitation with this approach comes in when a community does not have a stormwater utility established, which is the case for at least 2/3 of the regulated stormwater entities in the country. And even if a utility exists, the fees are often not high enough to make economic sense for onsite adoption when considering payback periods and other financial metrics. The reason for this is simple – stormwater utility fees are set at a level/rate to pay for needed stormwater programmatic and implementation rather than to create an effective financial incentive for private parcel owners to adopt BMPs onsite. The result of this are participation rates in incentive-based stormwater infrastructure on-site investments of 2-5% or lower associated with traditional incentive programs, which also include cost-sharing and subsidy programs as well. Due to this reason, communities are considering market-based approaches, such as stormwater credit trading, that can reward private property owners in a more robust way for onsite BMP adoption.
While market-based programs hold much promise, the focus of research in this area has been (rightly) on program architecture and policies with the view of “if we build it, they will come”. However, this leaves a void in understanding on how parcel owners will respond to market-based option. Questions regarding the motivations for adoption, how decisions on adoption are made, and how adoption on parcels affect adoptions in neighboring areas or parcels. This presentation will outline research done to begin to address the “consumer behavior” view of BMP adoption. Specifically, a socio-economic model based upon cellular automata-style agent-based modeling will be presented to illustrate a method to capture the adoption of GSI across multiple urban neighborhoods that comprise a city-wide landscape.
This model – the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) model – provides insights on neighboring effects, spatial dynamics, and decision-making aspects of GSI adoption based upon social theory. Model sensitivity analysis highlights the significance of social and spatial model elements to overall GSI adoption rates and pattern. An applied G-SSA model has been developed and explored to simulate the complex emergent patterns for GSI adoption across a specific cityscape (Washington, D.C.). Applied G-SSA model output is consistent with expected model behavior as well as observed and document GSI adoption patterns in Washington, D.C.
Research Colloquium on Computational Social Science/Data Science
Dr. Mahdi Hashemi
Assistant Professor
Department of Information Sciences and Technology
George Mason University
Machine learning for smart cities
Friday, November 22, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Cities are growing physically and digitally, faster than ever. The ever-growing population of cities, along with their intrinsic inaccessibility and inequity, has created difficulties with traffic, mobility, safety, health, pollution, and misinformation among many others. The physical and digital growth of cities outpaces the effort to address the aforementioned issues.
The growing popularity of online social networks (OSN) and World Wide Web (WWW) has remarkably expedited the information dissemination among individuals and groups. Digital data is the lifeblood of modern cities. Today, it’s being captured in large quantities at unprecedented rates via ubiquitous devices and sensors. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms that benefit from the availability of such data. That has turned the discussion from how the massive amounts of data are collected to how knowledge can be extracted from them.
Smart cities become smart not only because they automate routine functions serving the citizens, buildings, and traffic systems but also because they enable monitoring, understanding, analyzing and planning the city to improve the efficiency, equity, and quality of life for its citizens in real time. With physical and digital problems on one hand and big data on the other, smart cities strive to juxtapose them to find inexpensive solutions. How the digital data should be processed to help solve problems in cities remains one of the major areas of research and development in recent years and the focus of this talk
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Science and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University
Swabir Silayi
Bachelor of Science, Fatih University, 2009
Master of Science, Fatih University, 2011
Master of Science, Old Dominion University, 2013
ELECTRONIC STRUCTURE AND DYNAMICS ANALYSIS OF NOBLE METALS BY A TIGHT-BINDING PARAMETRIZATION
Wednesday, December 04, 2019, 3:00 p.m.
Exploratory Hall, Room 3301
All are invited to attend.
Committee
Dr. Estela Blaisten – Committee Chair
Dr. Dimitrios A. Papaconstantopoulos
Dr. James Glasbrenner
Dr. Eduardo Lopez
Theoretical studies of the properties of materials are important as they serve to narrow the focus of what are normally time consuming and costly experimental searches. In modeling these materials, first-principles density functional methods have been proven to quite effective. They have the drawback of being computationally expensive and, to mitigate this, faster approaches have been developed such as the tight-binding model.
We have used the Naval Research Lab (NRL) tight-binding (TB) method to study the electronic and mechanical properties of the noble metals. The tight-binding Hamiltonians are determined from a fit that has a non-orthogonal basis and reproduces the electronic structure and total energy values of first-principles linearized augmented plane wave calculations. In order to perform molecular dynamics simulations, we developed new TB parameters that work well at smaller interatomic distances. We analyze fcc, bcc and sc periodic structures and we demonstrate that the TB parameters are transferable and robust for calculating additional dynamical properties which they had not been fitted to.
To do this, we calculated phonon frequencies and density of states at finite temperature and performed simulations to determine the coefficients of thermal expansion and the atomic mean squared displacement. The energies for vacancy formation were also calculated as were the binding energies for fcc-based, bcc-based and icosahedral clusters of different sizes. The results compared very well with experimental observations and independent first-principles density functional calculations.
Extending from the single element systems, we develop parameter sets for the Cu-Ag and Ag-Au noble metal binary alloys as well. These parameters were fit to the structures 2, 10, 12 − 3,3, with the and representing the different combinations of , and in addition to the fcc , and .
As an output of this extension to the binary systems, the following quantities were reproduced in good agreement with available experimental and theoretical values: elastic constants, densities of electronic states as well as the total energies of additional crystal structures that were not included in the original first-principles database. We also used this TB parametrization for the alloy systems to successfully perform molecular dynamics simulations and determined the energies for vacancy formation, temperature dependence of the coefficient of thermal expansion, the mean squared displacement and phonon spectra. In addition we show that these TB parameters work for determining binding energies and bond lengths of Cu-Ag fcc-like clusters.
