Calendar

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences
Melanie Swartz
Melanie Schwartz, CSS PhD Candidate
Department of Computational and Data Sciences
George Mason University
Emoji Use in Social Media during events
Friday, April 12, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
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. This friday Melanie Swartz 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.

It’s that time of year!! Our semi-annual Faculty/Staff v. Students Soccer Game is coming up this Saturday, April 13th. We hope to see you there! We’ll bring the food and equipment, you just need to bring yourself…and friends, family, or pets if you like. All are welcome! Let’s have some fun!

We Are All in It Together: Training the Next Generation of Model Thinkers

We need to train the next generation of scientists, modelers and analysts to be technically competent and socially aware multidisciplinary collaborators. Model thinking and Modeling and Simulation can play an even more significant role in society if we expand our horizons beyond socio-technical problems and tackle broader challenges impacting the human condition. In this talk, we discuss multidisciplinary collaboration, inclusion and social awareness as three pillars that can form the basis for maintaining and increasing the relevance of our field in a world where technological change is outpacing our ability to manage and predict its impact on key aspects of life and society. We present tools, models and practical examples to illustrate the importance of each pillar and provide a framework for what we hope will become an intense and fruitful debate within the scientific community.

About Dr. Saiko Diallo

Chief Scientist, Research Associate Professor, Virginia Modeling, Analysis and Simulation Center (VMASC), Old Dominion University

Dr. Diallo has studied the concepts of interoperability of simulations and composability of models for over fifteen years. He is VMASC’s lead researcher in Human Simulation and Simulated Empathy where he focuses on applying Modeling and Simulation to study how people connect with one another and artificial beings, and how they experience their environment and creations. He currently has a grant to conduct research into modeling religion, culture and civilizations. He is also involved in developing cloud based simulation engines and User Interfaces in order to promote the use of simulation outside of the traditional engineering fields.

Dr. Diallo graduated with a M.S. in Engineering in 2006 and a Ph.D. in Modeling and Simulation in 2010 both from Old Dominion University. He is the President-Elect and a member of the Board of Directors for the Society for Modeling and Simulation International (SCS). Dr. Diallo has over one hundred publications in peer-reviewed conferences, journals and books.

Publications

https://scholar.google.com/citations?hl=en&user=cO_Fy9sAAAAJ&view_op=list_works

To facilitate open communication with all College of Science faculty, Dean Agouris is continuing to offer open door sessions periodically throughout the academic year.

Ideal topics for these faculty walk-in appointments include:

• new faculty introductions
• ideas for growth
• sharing research, teaching, or service activities
• multidisciplinary collaboration opportunities

Dean Agouris will be holding additional open door sessions on the following dates in Exploratory Hall 3200:

• October 24, 2018
• November 14, 2018
• November 27, 2018
• December 12, 2018
• January 15, 2019
• February 12, 2019
• February 27, 2019
• April 16, 2019
• March 27, 2019
• May 8, 2019
• May 21, 2019

Open door sessions will also be held at 3006 IABR on the SciTech Campus on the following dates from 10:30-12:30pm:

• October 9, 2018
• January 30, 2019
• February 19, 2019

Call ahead notification is not required. Just check in with Teri Fede in Suite 3200 Exploratory Hall (Fairfax) or at  IABR 3006 (SciTech).  If a queue forms during these periods, she will allocate time accordingly for those requesting it.

 By: Dr. Lora Peppard, Associate Professor of Nursing, Project Director for 3 federally funded behavioral health and integration grants, George Mason University

Brought to you by the College of Science at George Mason University. Special thanks to C. Daniel and Juliann Clemente for their support.

About the Galileo’s Science Café Series

Hear about the latest findings surrounding hot topics in science and medicine that affect our everyday lives and the decisions that we make. Bring your family and friends for a free, casual, interactive science discussion. Learn from the experts and speak with them personally.

Sponsored by the College of Science at George Mason University.

View the remaining dates

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences
Francesca Tavaza, PhD
National Institute of Standards and Technology Materials Science and Engineering Division
The JARVIS project: Accelerating discovery of materials and validation of models
using classical, quantum and machine-learning methods
Friday, April 19, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
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) fortheir 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.
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).

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
Thomas P. Boggs
Master of Science, George Mason University, 2002
Master of Science, Virginia Tech, 1994
Bachelor of Arts, Virginia Tech, 1992
Bachelor of Science, Virginia Tech, 1991
Probabilistic Topic Modeling for Hyperspectral Image Classification
Monday, April 22, 2019, 3:00 p.m.
Exploratory Hall, Room 3301
All are invited to attend.
Committee
Jason Kinser, Chair
Igor Griva
Ronald Resmini
Robert Weigel
Probabilistic Topic Models are a family of mathematical models used primarily to identify latent topics in large collections of text documents. This research adapts the topic modeling approach to the unsupervised classification of hyperspectral images. By considering image pixels similarly to text documents and quantizing data for each spectral band to develop a spectral feature vocabulary, it is demonstrated that by using Latent Dirichlet Allocation with a hyperspectral image corpus, learned topics can be used to produce unsupervised classification results that often match ground truth better than the commonly used k-means algorithm. The
topic modeling approach developed is demonstrated to easily extend to classification of image regions by aggregating spectral features over spatial windows. The region-based document models are shown to account for the spectral covariance and heterogeneity of ground-cover classes, resulting in similarity to land use ground truth that increases monotonically with window size.
Multiresolution wavelet decompositions of pixel reflectance spectra are used to develop a novel feature vocabulary that more naturally aligns with material absorption and reflectance features, further improving classification results. The wavelet-based document modeling approach is evaluated against synthetic image data, a small AVIRIS image with 16 ground truth classes, and finally on practical-sized, overlapping AVIRIS and Hyperion images to demonstrate the utility of the models. Multiple wavelet bases and numbers of quantization levels are considered and for the data sets evaluated, it is determined that using the Haar wavelet with 10 quantization levels yields the best performance, while also producing easily interpretable topics. It is demonstrated that by omitting low-level wavelet coefficients, vocabulary size and model inference time can be significantly reduced without loss of accuracy.
The wavelet-based approach is extended by replacing quantization levels with simple thresholds for positive and negative wavelet coefficients, reducing the vocabulary size to two times the number of wavelet coefficients. The thresholded wavelet model provides accuracy comparable to the quantized wavelet model, while having significantly shorter inference time and supporting easily interpretable visualization of topics in the wavelet domain. By establishing appropriate model hyperparameters and omitting low-level wavelet
coefficients, the thresholded wavelet model provides better unsupervised classification results than previously developed quantized band models, has shorter model parameter estimation time, and has an average document word count smaller by a factor of 5 and a vocabulary smaller by a factor of 10

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Matthew Oldham
Bachelor of Economics with Honours, University of Tasmania, 1995
Master of Arts in Interdisciplinary Studies, George Mason University, 2016
The Utilization of Computational Social Science
for the Benefit of Finance
Wednesday, April 24, 2019, 9:00 a.m.
Exploratory Hall, Room 3301
All are invited to attend.
Committee
Robert Axtell, Chair
Andrew Crooks
Edward Lopez
Richard Bookstaber
The ability to identify the mechanisms responsible for the behavioral characteristics of financial markets has remained an elusive pursuit. Further, the precise behavioral characteristics of financial markets remains a point of contention. Some practitioners proclaim that markets are efficient and the return profile of financial assets follow a Gaussian distributed random walk, while others suggest that markets are not efficient, with returns tending to be heavily skewed and markets record instances of extreme outlying events at a rate more than what the efficient school prescribes. A feasible explanation for why financial markets behave as they do is that they are a complex adaptive system (CAS), an approach where investors and firms are considered heterogeneous interacting agents (HIA), which contrasts against the single representative agent approach utilized in the efficient market (neoclassical economic) paradigm.
Firstly, this dissertation provides an overview of the basis of the efficient market framework (EMF) before presenting the need to pursue alternative methods. The principal alternative discussed is the utilization of Computational Social Science (CSS) tools to consider financial markets as a CAS. The primary impetus for the approach is the statistical imprint of a CAS – power-law distributions – are found in asset returns and various other economic variable related to financial markets, including the distributions of shareholders and firm size. Of the various CSS tools, the remainder of the dissertation presents two agent-based models aimed at addressing a variety of research, yet with a common theme of quantifying the effects of agents placing an increased focus on short-term factors, a phenomenon known as “short-termism.”
The first model considers the effects of investors forming an information network with each other in an agent-based artificial stock market. In turn, agents try and improve their investment performance by adjusting their connections; a process that involves cutting ties with those agents who provide poor quality information and connecting to the betterperforming investors. The crucial elements in the model are the timeframe over which the agents consider their performance; the interval between rewiring their connections; and
their tendency to follow the advice of their connections over other information sources. Through varying the effect of these elements meaningful insights into the dynamics driving the behavior of the financial markets, with the presence of even a small proportion of
short-term investors being responsible for a material increase in market volatility. A similar record occurred after reducing the interval between when investors adjust their information network.
An ambition research agenda underlies the implementation of the second model. The foundation for the model stems from the growing concern that the management of publicly listed firms is becoming preoccupied with the share price of their firm, thereby placing an
increased, and non-optimal, focus on their short-term earnings. To address this issue required the expansion of the existing agent-based artificial stock market approach to include many firms who have their earnings endogenously influenced by the market. To achieve the required expansion, the model has firms maintain growth expectations which they adjust after factoring in their most recent performance against those expectations and the movement in their firm’s share price. Firms also must allocate their limited resources between growing sales or margins. In terms of the investors, the model considers various investment styles, with individual styles and combinations responsible for generating greater volatility in the market and more extreme adjustments by management. Before undertaking the extensions, an extensive set of data relating to the size, growth, and performance of globally listed firms was collected and assessed. Consistent with previous research, the distributions, apart from growth, were heavily skewed. The growth distributions were found to be somewhat consistent with Laplace distributions, which is the existing growth distribution benchmark.

The College of Science announces a research colloquium specifically for undergraduate research students. Participating students will present a poster at the 2019 Undergraduate Research Colloquium, which is open to the university community.

The purpose of this event is to showcase the variety of undergraduate research projects in the College to other students and to faculty in four categories Life Sciences, Physical Sciences, Earth Sciences and STEM Learning & Education. If you are a student engaged in undergraduate research at any level in any academic unit within the College of Science you are encouraged to participate. This year’s event will feature a student poster sessions and awards.

This event is FREE of charge for all Mason students, staff, and faculty. Refreshments will be served and monetary awards are available.

Colloquium Details

Location: Center for the Arts Lobby
Date: Thursday April 25, 2019
Time: 1-4pm

Schedule:
1-2pm Poster Session A
2-3pm Poster Session B
3-3:30pm  Networking reception and Student’s Choice Award
3:30-4pm   Dean’s Address and Poster Awards

Deadlines

March 30, 2019: Abstract submission deadline (250 words or less). Submit HERE.
April 14, 2019: Poster submission deadline. Use required template and send electronic document to stemcos@gmu.edu

RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES
Ross Schuchard
Computational Social Science PhD Candidate
Department of Computational and Data Sciences
Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks
Friday, April 26, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall
All are welcome to attend.
Abstract:
The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the conversational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes.