Author: William Kennedy

The Research Colloquium on Computational Social Science/Data Sciences speaker for Friday, October 11, 2019, will be Neil Johnson, Professor of Physics, George Washington University. Dr. Johnson’s talk entitled “Slaying the Online Hydra of Hate, Distrust and Anti-Science” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall.  The talk will be followed by a Q&A session along with light refreshments. 

This session will be live-streamed on the YouTube channel:  https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

 For a list of upcoming and previous seminars, please visit:  https://cos.gmu.edu/cds/calendar/

 Abstract:  Hate and distrust are thriving on the Internet [1]. In addition to the political world, medicine and science are also under attack. Social media platforms such as Facebook have access to state-of-the-art software tools, yet appear unable to keep it under control. In this talk, I discuss why this might be using a simple theory of the functional properties of networks-of-networks. I also show how the ’sociophysics’ of heterogeneous objects offers a fresh understanding of such systems as interacting gels in a multi-dimensional space. I then discuss how this analysis is connected to the unexpected surge in online pro-ISIS support that arose several years ago [2].
[1] N.F. Johnson et al., Hidden resilience and adaptive dynamics of the global online hate ecology, Nature, Sept. 12, 2019

[2] N.F. Johnson et al., New online ecology of adversarial aggregates: ISIS and beyond, Science 352, 1459 (2016)

Bio:  Neil Johnson is a professor of physics at George Washington University and heads up a new initiative in Complexity and Data Science which combines cross-disciplinary fundamental research with data science to attack complex real-world problems. He is a core member of GW’s new Knight Foundation-funded Institute for Data, Democracy and Politics. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures through to the online world of collective behavior on social media. He is a Fellow of the American Physical Society (APS) and is the recipient of the 2018 Burton Award from the APS. He received his BA/MA from St. John’s College, Cambridge, University of Cambridge and his Ph.D. as a Kennedy Scholar from Harvard University. He was a Research Fellow at the University of Cambridge, and later a Professor of Physics at the University of Oxford until 2007, having joined the faculty in 1992. Following a period as Professor of Physics at the University of Miami, he was appointed Professor of Physics at George Washington University in 2018. He presented the Royal Institution Christmas Lectures “Arrows of Time” on BBC TV in 1999. He co-founded and co-directed CABDyN (Complex Agent-Based Dynamical Systems) which is Oxford University’s interdisciplinary research center in Complexity Science, and an Oxford University interdisciplinary research center in financial complexity (OCCF).

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Research Colloquium on Computational Social Science/Data Sciences speakers for Friday, October 04, 2019, will be Amira Al-Khulaidy and Valentin Vergara, CSS PhD students. Their talk, entitled “Corruption and the effects of influence within social networks: An agent-based model of the “Lava Jato” scandal,” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

This session will not be video-streamed.

For announcements regarding future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

We hope to see you on Friday, October 04, 2019 at 3PM.

Abstract: 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.

Bios:
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.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Research Colloquium on Computational Social Science/Data Sciences speaker for Friday, September 27, 2019, will be Anamaria Berea, Visiting Research Assistant Professor, Complex Adaptive Systems Lab, University of Central Florida. Dr. Berea’s talk entitled “Exploring what is universally possible for life with a wealth of computational methods” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

This session will be live-streamed on the YouTube channel: https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA
For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/
For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

We hope to see you on Friday, September 27, 2019 at 3:00.

Abstract: In this talk I will discuss the advances made at NASA/SETI Frontier Development Lab AI Research Accelerator with respect to understanding the coevolution of biospheres and atmospheres, from cellular metabolisms to planetary environments. The current challenge presented by the sample of 1 when we consider the evidence of life and living systems in the Universe is overcome by a series of computer simulations and simulated data that give enough potential combinations of stable biospheres to be explored by machine learning algorithms. While we are only scratching the surface now with respect to the promise and perils of constructing well informed simulations and feeding them into more advanced algorithms, this approach will hopefully help identify possible life on observable exoplanets identified in the Kepler and TESS data. Methodologically, we explored Monte Carlo simulations, agent-based models, machine learning and dockerization in a complex data architecture supported by Google, in order to simulate more than 250,000 stable atmospheres, 500 types of synthetic genomes (metabolic networks) and more than 37,700,000 chemical reactions.

Bio: Anamaria Berea has dual PhDs in economics (2010) and computational social science (2012). Her research is focused on the emergence of communication in biological and social networks, by applying theories and methods from economics, complex systems and information theory to understand fundamental aspects of communication from cells to societies. Dr. Berea was one of the data scientists part of the NASA/SETI Frontier Development Lab (2017) in the heliophysics team and a data science mentor for the astrobiology team in the following year. She is the author of the book “Emergence of Communication in Socio-Biological Networks”, Springer, 2018. She is currently a Blue Marble Space Institute of Science research scientist and a Visiting Research Assistant Professor, Complex Adaptive Systems Lab, University of Central Florida.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

Dr. Vadim Sokolov, Assistant Professor, Department of Systems Engineering and Operations Research, will be the presenter at Friday’s
Research Colloquium on Computational Social Science/Data Science. His topic will be “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.

Abstract:
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.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Research Colloquium on Computational Social Science/Data Science speaker for Friday, September 13, will be Kim McEligot, Ph.D. Candidate, Department of Systems Engineering and Operations Research at George Mason University. Kim’s talk entitled “Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy” (abstract below) will begin at 3:00 in the Center for Social Complexity Suite (373-381) located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

This session will be live-streamed on the YouTube channel: https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA
For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

Abstract: Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.

Bio: Kim McEligot is a PhD candidate in the Department of Systems Engineering and Operations Research at George Mason University. His research interests include federation of computational fluid dynamics coastal flood modeling with geo-spatial agent-based modeling for individual and community level flood mitigation policy analysis. He holds an M.S. in Systems Engineering from Johns Hopkins University, and an M.A. in National Security and Military Affairs from the U.S. Naval War College. His email address is [email protected].

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

Welcome to the first seminar of the fall semester.

The Research Colloquium on Computational Social Science/Data Sciences speakers for Friday, September 06, 2019, will be Ben Intoy, PhD and Dan Baeder from Deloitte Consulting LLP. Their talk, entitled “Massive-Scale Models of Urban Infrastructure and Populations,” will begin at 3:00pm in the Center for Social Complexity Suite (373-381) located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

We hope to see you on Friday, September 06, 2019 at 3PM.

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. After completing his PhD, Ben 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, he took a position at 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 focuses 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.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Colloquium on Computational Social Science/Data Sciences Research speaker for Friday, May 03, 2019, will be Gary Bogle, who recently defended his CSS doctoral dissertation. Gary’s talk entitled “Polity Cycling in Great Zimbabwe via Agent-Based Modeling: The Effects of Timing and Magnitude of External Factors,” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/
For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

NOTE: this is the last seminar for the spring semester; see you in the fall semester!

Abstract: This research explores polity cycling at the site of Great Zimbabwe. It rests on laying out the possibilities that may explain what is seen in the archaeological record in terms of modeling what external factors, operating at specific times and magnitudes. What can cause a rapid rise and decline in the polity? This is explored in terms of attachment that individuals feel towards the small groups of which they are a part of, and the change in this attachment in response to their own resources and the history of success that the group enjoys in conducting collective action. The model presented in this research is based on the Canonical Theory of politogenesis. It is implemented using an agent-based model as this type of model excels at generating macro-level behavior from micro-level decisions.

The input parameters to the model presented here are the collective action frequency (CAF) and environmental effect multiplier. The results show that a prehistoric polity can be modeled to demonstrate a sharp rise and fall in community groups and that the rise and fall emerges from the individual decision-making.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Colloquium on Computational Social Science/Data Science Research speaker for Friday, April 19 will be Francesca Tavazza, Ph.D., National Institute of Standards and Technology, Materials Science and Engineering Division. Dr. Tavazza’s talk entitled “The JARVIS project: Accelerating discovery of materials and validation of models using classical, quantum and machine-learning methods” (abstract below) will begin at 3:00 in the Center for Social Complexity Suite located on the third floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

Please visit https://cos.gmu.edu/cds/calendar/to see list of upcoming seminar speakers.

Abstract: Identifying new materials for technological applications is the goal of the Material Genome Initiative (MGI). As a response, NIST started the JARVIS project, a combination of atomistic databases at the classical and quantum level, and machine learning models. JARVIS-DFT is a collection of physical properties computed using Density Functional theory (DFT) for about 30000 materials. For each material, we determined its heat of formation, conventional and improved DFT bandgaps, dielectric function, elastic, phonon, electronic and transport properties. Statistical analysis of such properties allows to identify novel trends as well as new materials with desirable properties. JARVIS-FF is a database of classically computed properties, designed to facilitate the user in choosing the right classical force field (FF) for their investigation. It uses the LAMMPS code to compute the same property, for the same material, with as many force fields as available (more than 25000 classical force-field). We focused on quantities like relaxed structures, elastic properties, surface energies, vacancy formation energies and phonon vibrations. JARVIS-FF contains these calculations for more than 3000 materials, so that a direct comparison between FF is easily achieved. Lastly, using all the properties in JARVIS-DFT as a training set, and novel descriptors inspired by FF-fitting, we developed machine learning (ML) models for all the properties studied in JARVIS-DFT. This allows to make on the fly predictions, and, therefore, to use ML to pre-screen materials.

Dr. Tavazza’s Short Bio:
Undergraduate degree in Physics in Milan, Italy, 1993 (Universita’ Statale di Milano, Milano, Italy)
Master in Material Science in Milan, Italy, 1996 (Universita’ Statale di Milano, Milano, Italy). Dissertation topic: Tight-binding modeling of Cobalt and Iron Silicides, including fitting of the tight-binding parameters.
PhD in Physics at The University of Georgia, GA, USA in 2003 (PhD. Advisor: Prof. Davis Landau). Dissertation topic: Classical Monte Carlo simulations of Si and Si-Ge compounds under various conditions.
PostDoc at NIST starting in 2003, focusing on Density Functional theory (DFT) modeling of mechanical properties in metals.
Brief hiatus working at the Army Research Laboratory in 2008 for a short time, otherwise at NIST ever since I got there as a postdoc.
Currently: running an atomistic modeling group (both classical and DFT modeling) focused on the investigation of specific, technological relevant materials (TaS2, TaSe2, Bi2MnSe4, for instance) as well as on compiling databases of material properties. My group extensively uses artificial intelligence (AI) tools to accelerate material discovery as well as to build novel force fields (physics-inspired, neuron network-based fitting of Si, Ge, SiGe, AlNi potentials).

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.

The Colloquium on Computational Social Science/Data Sciences Research speaker for Friday, April 12, 2019, will be Melanie Swartz, Computational Social Science PhD Candidate, Department of Computational and Data Sciences. Melanie’s talk entitled “Emoji Use in Social Media During Events” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

Abstract: Emoji in social media add more information than just a pictograph to accompany words or convey emotion. Emoji use related to communication about collective social events can provide additional insight about our collective identity and social interactions. Melanie will be presenting preliminary results and welcomes your feedback of her analysis on emoji use in social media for a number of events ranging from national, religious, protests, marches, celestial events, global scheduled events such as International Women’s Day, and more. We look forward to seeing you in person for an engaging discussion on Friday as this event will not be recorded or live streamed.

• *The programs and services offered by George Mason University are open to all who seek them. George Mason does not discriminate on the basis of race, color, religion, ethnic national origin (including shared ancestry and/or ethnic characteristics), sex, disability, military status (including veteran status), sexual orientation, gender identity, gender expression, age, marital status, pregnancy status, genetic information, or any other characteristic protected by law. After an initial review of its policies and practices, the university affirms its commitment to meet all federal mandates as articulated in federal law, as well as recent executive orders and federal agency directives.