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.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Oscar Olmedo, PhD
Data Scientist
CACI International, Inc.
Calibration for Probabilistic Classification
Monday, August 28, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: This talk will be a review of calibration methods for classifiers that make probabilistic predictions on a scale 0 to 1. It is known that certain classification methods, such as Naïve Bayes or Random Forest make biased predictions that to not match the true posterior probabilities. By calibrating the predictions made by classifiers the true probability of the predicted class can be determined. This type of calibration can be crucial for real-world decision making problems in medicine, business, marketing, and finance. In this talk I will focus on applications in marketing.
Part two: Marketing yourself for future careers outside of academia
It is known that the number of jobs in academia is not rising as fast as the number of PhD’s graduating. Currently a new career option is available to these PhDs, the Data Scientist. But how does one make the transition out of academia to this hot new field? I will discuss strategies for marketing yourself as well as tools necessary to be successful in your transition.
Dr. Oscar Olmedo is an alumnus of George Mason University who studied physics in undergrad (2004), Computational Sciences and Informatics Masters (2007), and Computational Sciences and Informatics PhD (2011) with a concentration in solar physics under Dr. Jie Zhang. After graduating in 2011, Dr. Olmedo went on to NRL as an NRC fellow for two years, and briefly worked at NASA Goddard for a few months in 2013 before moving to Syntasa, a startup focusing on ecommerce/marketing analytics. In 2015, he moved to CACI to work on cyber security research as a DARPA contractor.
A copy of Dr. Olmedo’s presentation is found here: OLMEDO_PRESENTATION_8.28.17
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
William G. Kennedy, Research Assistant Professor
Center for Social Complexity
Characterizing the reaction of the population of NYC to a nuclear WMD
Friday, September 1, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
ABSTRACT: This talk will review the status of our multi-year project to characterize the reaction of the population of a US megacity to a nuclear WMD event. Our approach is to develop an agent-based model of the New York City area, with agents representing each of the 20-25 million people, and establish a baseline of normal behaviors before exploring the population’s reactions to small (5-10Kt) nuclear weapon explosions. In our first year, we explored understanding a large population’s reaction to a nuclear WMD event with four major activities: (1) reviewing existing social theories and reports of disaster behavior, (2) collecting data and modeling the infrastructure of a mega-city and surrounding region, (3) generating synthetic population, and (4) developing an agent-based model of all the individuals in the region. The review of social science theories and data on individual/group behavior during disasters led to the publication of a case study (the Flint River drinking water crisis) and preparation of two review papers. For the New York City mega-city and surrounding area, we collected spatial, demographic, and workforce data from several sources and devised methods and algorithms to make the data useful for our simulation. Using Python, we processed road data and created one connected network forming the transportation layer of the model. Using demographic data and our own heuristics, again in Python, we synthesized individuals, their households, their associated schools and workplaces and finally their social networks. Other datasets were utilized so that children attend nearby schools or daycare constrained with actual capacities and people are employed in workplaces located nearby matching workforce data. Finally, we began modeling individuals’ movement in three counties, two rural counties and one in the heart of Manhattan. I will start with a discussion of the effects of a nuclear WMD event and then discuss the details our work and our future plans.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Claudio Cioffi-Revilla, Professor
Computational Social Science
Department of Computational and Data Sciences
Director, Center for Social Complexity
George Mason University
Computational Modeling of Terrorism
Monday, September 11, 4:30-5:45
Exploratory Hall, Room 3301
Computational social scientists have investigated terrorism for decades, but only recently has the field advanced to creating the first testable formal theories. This talk will review some important background and present recent advances in agent-based modeling of terrorism, based on radicalization theory and research. Enduring challenges will also be covered, as opportunities for research projects, theses, and dissertations.
Dr. Cioffi-Revilla is a Professor of Computational Social Science, founding and former Chair of the Department of Computational Social Science, and founding and current Director of the Mason Center for Social Complexity at George Mason University. He holds two doctoral degrees in Political Science and International Relations and his areas of special interest include quantitative, mathematical, and simulation models applied to complex human and social systems.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Matthew Oldham, PhD Student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
The Quest for Living Beta: Investigating the Implication of Shareholder Networks
Friday, September 15, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
The behavior of financial markets has, and continues, to frustrate investors and academics. With the advent of new approaches, including complex systems and network analysis, the search for an explanation as to how and why markets behavior as they do has greatly expanded, and moved away from the tradition neoclassical approaches that have been beholden to the Efficient Market Hypothesis.
The complex system approach utilizes a number of a concepts in an attempt to understand stock market returns including; imitation, herding, self-organized co-operativity, and positive feedbacks, with many of these features captured by network analysis. In addition, with the meteoric rises of network science has come the realization that the behavior of a system can vary greatly depending on the network structure (the topology) of a system, thus providing further impetus for the use of network analysis in terms of financial markets.
My presentation will detail my recent research of the US Institutional shareholder networks over the period of 2007-10, a period which includes the beginning of the Global Financial Crisis. The research utilized an extensive dataset provided from the Thomson Reuters 13f database, to undertake a temporal analysis of the networks formed between US institutional investors and the stocks in the S&P 500. The analysis makes use of both projected and bipartite networks and uncovers numerous insights regarding relationships between the market in general, stocks and their shareholders. In addition, I will illustrate how the findings can be used in conjunction with an agent-based model to uncover the workings of the stock market.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Jim Simpson, PhD
Cybersecurity and Data Science Consultant
Ages and Lifetimes of U.S. Firms: Why Businesses Should NOT be Treated Like People
Monday, September 18, 4:30-5:45
Exploratory Hall, Room 3301
Abstract: An overview of Deep Learning with quick introductions to techniques such as Word Embeddings, Autoencoders, Transfer Learning, Attention Models, and Generative Adversarial Networks.
Dr. Simpson is a seasoned researcher in the areas of machine learning, deep learning, and cybersecurity. He has served as Principal Investigator and Data Scientist on several DARPA programs with a focus on machine learning applications to data fusion, prediction, and unsupervised anomaly detection problems. He holds a Ph.D. in Electrical Engineering from North Carolina State University where he developed novel receivers and algorithms for undersea communications.
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Andrew Crooks, Associate Professor
Computational Social Science
Department of Computational and Data Sciences
George Mason University
ABM for Simulating Spatial Systems: How are we doing?
Friday, September 22, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places?
In this presentation, I will review the current state of art of modeling geographical systems. I will highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Lauren Deason, PhD
Lead Data Scientist
Punch Cyber Analytics Group
Monday, September 25, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: This presentation will detail novel analytic methods for processing time series data at scale using techniques drawn from digital signal processing and document matching. These methods can be applied to detect coordinated and automated cyber activity, to match patterns present in time series data, and to fuse together disparate datasets.
Dr. Deason holds a BS in Applied Mathematics from the University of Virginia, a MA in Mathematics with an emphasis in Real Analysis and Probability Theory from UC Berkeley, and a PhD in Economics from the University of Maryland, College Park. Dr. Deason has 10 years of experience in mathematical modeling and data science, spanning employment as a Professor of Mathematics, an Economist, and a Data Scientist. Dr. Deason’s past experience includes developing dynamic stochastic models within a game theoretic framework to explore the effects of trade policy uncertainty as well as estimating empirical models to explain various phenomena. More recently, Dr. Deason has developed multiple algorithms for detecting and classifying periodic and coordinated behavior in a variety of contexts on large data sets as part of DARPA’s Network Defense Program.
Computational Social Science
Robert Axtell, PhD
Computational Social Science Program, Department of Computational and Data Sciences,
College of Science
Department of Economics, College of Humanities and Social Sciences
Krasnow Institute for Advanced Study
George Mason University
Co-Director
Computational Public Policy Lab
Krasnow Institute for Advanced Study and Schar School of Policy and Government
External Professor, Santa Fe Institute (santafe.edu)
External Faculty, Northwestern Institute on Complex Systems (nico.northwestern.edu)
Scientific Advisory Council, Waterloo Institute for Complexity and Innovation (wici.ca)
Getting Younger by Growing Older: U.S. Firms Gain Longevity as they Age
Friday, September 29, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
Abstract: Using data on the entire population of American firms I will first show that the distribution of firm ages is approximately stationary, with small ‘defects’ arising at the start of last decade’s Financial Crisis now propagating through the distribution. From these data I will derive the distribution of U.S. firm lifetimes and demonstrate that it has a specific structure that conforms to economists’ intuitions about new (and small) firms having higher failure probabilities than older (and larger) firms. I will then demonstrate that the large body of statistical theory on ‘survival analysis’ is directly applicable to firms. Specifically, I will focus on firm hazard functions and empirically show that for the U.S. this is a power law over a wide range of ages, ostensibly a new finding, declining with age. This permits computation of the expected remaining lifetime of firms as a function of their age, an INCREASING function, implying that American firms gain longevity as they get older, a very non-biological type of aging. Conditioning on firm size produces further results. Specifically, using the Cox ‘proportional hazards’ specification, the reduction in failure probability associated with larger size is quantified. At the end I will demonstrate that an ABM of firm dynamics can be calibrated to reproduce all of these features of U.S. firms.
Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.
His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.) His research has been supported by American philanthropies (e.g., John D. and Catherine T. MacArthur Foundation, Institute for New Economic Thinking) and government organizations (e.g., National Science Foundation, Department of Defense, Small Business Administration, Office of Naval Research, Environmental Protection Agency). Stories about his research have appeared in major magazines (e.g., Economist, Atlantic Monthly, Scientific American, New Yorker, Discover, Wired, New Scientist, Technology Review, Forbes, Harvard Business Review, Science News, Chronicle of Higher Education, Byte, Le Temps Strategique) and newspapers (e.g., Wall St. Journal, Washington Post, Los Angeles Times, Boston Globe, Detroit Free Press, Financial Times). He is co-author of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press) with J.M. Epstein, widely cited as an example of how to apply modern computing to the analysis of social and economic phenomena.
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Kirk Borne, PhD
Principal Data Scientist and an Executive Advisor
Booz Allen Hamilton
Monday, October 2, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: Smart data are essential when faced with massive-scale data collections. “Smart” refers to data that are tagged or indexed with meaning-filled metadata that carry information about the semantic meaning of the data, its applications, use cases, content, context, and more. Such meta-tags enable efficient and effective discovery, description, and delivery of the right data at the right time, both to humans and to automatic processes.
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/
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Xiaoyi Yuan, PhD Student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University
Quantifying the Social Debates of Anti-Vaccination on Twitter
Friday, October 6, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
Measles is one of the leading causes of death among young children. In many developed countries with high measles, mumps, and rubella (MMR) vaccine coverage, measles outbreaks still happen each year. Social media has been one of the dominant information sources to gain vaccination knowledge and thus has also been the focus of the “anti-vaccine movement”. This talk is about two of my recent research projects on this topic. The first one will be introduced briefly, which is an agent-based model demonstrating how a small amount of online anti-vaccine sentiment could have the power of increasing the probability of measles outbreaks significantly. This research inspired me to investigate details of communicative pattern of “anti-vacciners” by analyzing a large twitter dataset (660892 tweets) after the California Disneyland measles outbreak in 2015. This second research has two main parts: first, in order to identify “anti-vacciners”, I used supervised learning to label each tweet as either positive, neutral, or negative opinion towards vaccination. The linear support vector machine model shows good performance on this dataset with an accuracy score of 72% on test data. Second, Louvain’s method for community detection of the retweet network shows the common pattern of social media communities; i.e., overall fragmented but with a few large communities. By investigating the opinion distribution in big communities, however, I discovered that they are highly overlapped, especially within “anti-vacciners”, meaning that they have more frequent communication within their own opinion group than with others. What’s useful for health communication strategies is to look further into the brokers–those who stand between two or more communities. At the end of the talk, I will address details of analyzing the brokerage as well.
