Calendar

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Qing Tian, Assistant Professor
Computational and Data Sciences
George Mason University

 Introduction to R for Computational and Data Science

Friday, February 23, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

ABSTRACT:  R is a programming language and free software environment for statistical computing and graphics. It has gained substantially increased popularity in recent years. In addition to classical statistical analysis functionalities, it includes a wide range of packages with capabilities of data mining and machine learning, text analysis, spatial statistics, and social network analysis etc. This seminar will focus mostly on a suite of R packages that are designed to facilitate data (including social networks) visualization. These visualization functions are useful for exploratory analysis of real world data as well as output data from simulations.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Johan Bos-Beijer
Chief Data Officer and Assistant Inspector General
Managed Business Services
U.S. Navy

The Government Data Landscape:  Practical Understanding and Perspectives for the Future

Monday, February 26, 4:30-5:45
Exploratory Hall, Room 3301

Mr. Bos-Beijer brings to our forum over 40 years of executive and senior management private and public-sector experience primarily in data, strategic execution, program management, and ever-expanding curiosity about current and future technical capabilities applied to challenges. He is known for sharing successful practitioner examples, formidable development and execution experience, and a unique approach to problems. His presentation and discussion with us will share his personal journey, how to understand the Federal Data Landscape, examples of skills applied to program success and innovation, as well as learning what he refers to as the ‘navigation and negotiation’ approaches to data science in the public sector. He will cover examples of pioneering efforts in government and bringing private sector and academic methods to start ups in government. Mr. Bos-Beijer has also offered additional time with us after the session to interact with students and faculty to further the discussion and networking.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Eduardo Lopez, Assistant Professor
Department of Computational and Data Sciences
George Mason University

A Network Theory of Inter-Firm Labor Flows

Monday, March 5, 4:30-5:45
Exploratory Hall, Room 3301

Abstract: Using detailed administrative microdata for two countries, we build a modeling framework that yields new explanations for the origin of firm sizes, the firm contributions to unemployment, and the job-to-job mobility of workers between firms. Firms are organized as nodes in networks where connections represent low mobility barriers for workers. These labor flow networks are determined empirically, and serve as the substrate in which workers transition between jobs. We show that highly skewed firm size distributions are a direct consequence of the connectivity of firms. Further, our model permits the reconceptualization of unemployment as a local phenomenon, induced by individual firms, leading to the notion of firm-specific unemployment, which is also highly skewed. In coupling the study of job mobility and firm dynamics the model provides a new analytical tool for industrial organization and may make it possible to synthesize more targeted policies managing job mobility.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

James Glasbrenner, Assistant Professor
Department of Computational and Data Sciences
George Mason University

Using data science and materials simulations to control the corkscrew magnetism of MnAu₂

Monday, March 19, 4:30-5:45
Exploratory Hall, Room 3301

Materials occupy a foundational role in our society, from the silicon-based chips in our smartphones to the metals used to manufacture automobiles and construct buildings. The sheer variety in materials properties enables this wide range of use, and studying the atoms that bond together to form solids reveals the microscopic origin behind these properties. Remarkably, many properties can be traced to the behavior of and interaction between electrons, and computational simulations such as density functional theory calculations are used to study the features and macroscopic effects of this electronic structure. This computational approach can be further enhanced through recent advances in data science, which provide powerful tools and methods for analyzing and modeling data and for handling and storing large datasets.

In this talk, I will: 1) introduce the basic concepts of computational materials science and density functional theory in an accessible manner, and 2) present calculations on the material MnAu₂ where I use density functional theory and modeling to analyze its magnetic properties. The MnAu₂ structure is layered and its magnetic ground state forms a noncollinear corkscrew that rotates approximately 50° between neighboring manganese layers. Using the results of my calculations, I will explain the electronic origin of this corkscrew state and how to control its angle using external pressure and chemical substitution. In addition to discussing the electron physics, I will place a particular emphasis on the connection between data science and how modeling was used to analyze and interpret the density functional theory calculations. This will include a new, critical reexamination of my model fitting procedure using cross-validation and feature selection techniques, which will formally test the underlying assumptions I made in the original study.

Computational Social Science Friday Seminar

Ryan Zelnio, Ph.D.
Chief Analytics Officer
Office of Naval Research

 The Creation of the Office of Naval Research’s Data & Analytics Lab

Friday, March 23, 3:00-4:30 p.m.
Center for Social Complexity Suite
Research Hall, 3^rd Floor

The Office of Naval Research (ONR) coordinates, executes, and promotes the science and technology programs of the United States Navy and Marine Corps. It administers the Naval Research Enterprise (NRE) investment portfolio of $2B annually in Naval relevant science and technologies (S&T) ranging from basic research to technology prototyping. This portfolio covers over 3000 grant and contract awards annually over a large variety of technologies.  In FY2017 alone, the basic and applied research portfolio (which is less than 50% of its budget) funded 4,411 scientific articles, 2,732 conference papers, 343 theses, 204 books & book chapters and 88 patents. However, while this portfolio is large, it is a drop in the bucket within the global research & development (R&D) enterprise.  In an attempt to understand this vast amount of data being produced both within the NRE and globally, ONR recently stood up the Data & Analytics Lab.  Its mission is to support strategic decision making at the Office of Naval Research with in-depth analysis of the NRE portfolio to enhance mission effectiveness for U.S. Naval Forces. This new lab is led by Mr. Matt Poe and includes Dr. Ryan Zelnio (2013 GMU SPP grad) serving as the Chief Analytics Officer and LCDR Nick Benes serving as the Chief Data Officer.  This lab seeks to harness ONR’s investments in social network analysis, machine learning, natural language processing, data visualization, supervised and unsupervised clustering, and many other data science tools to support decision processes across the NRE. Their talk will cover the range of challenges facing their lab as they stand up their effort and discuss the broader move within the federal government to better apply the tools of data science to understand the complexity of the R&D enterprise.  They will also discuss future partnering and internship opportunities.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Dr. Peer Kröger, Professor
Chair of Database Systems and Data Mining
Ludwig-Maximilians-University Munich

TBA

Monday, March 26, 4:30-5:45
Exploratory Hall, Room 3301

Details coming soon….

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

By Christopher Carroll and Jacquelyn Kazil

Christopher Carroll, Professor
Department of Economics
Johns Hopkins University

Title: Introduction to The Economics ARK (Algorithmic Repository and toolKit)

Abstract:
The Econ-ARK/HARK toolkit is a modular and extensible open source toolkit for solving, simulating, and estimating heterogeneous-agent (HA) models in economics and the social sciences. Although the value of models of this kind has been clear both to academics and to policymakers for a long time, the code for implementing such models has so far been handcrafted and idiosyncratic. As a result, it may take years of human capital development for a new researcher to become proficient enough in these methods to contribute to the literature. The seminar will describe how the Heterogeneous Agents Resources and toolKit (HARK) eases this burden by providing a robust framework in which canonical examples of such models are solved. The toolkit provides object-oriented tools for representing heterogeneous agents, solution methods for solving or characterizing their dynamic choice problems, and a framework for representing the environment in which agents interact. The aim of the toolkit is to become the go-to resource for heterogeneous agent modelers, by providing a well-designed, well-documented, and powerful platform in which they can develop their own work in a robust and replicable manner.

Bio:
I am a professor of economics at JHU and co-chair of the National Bureau of Economic Research’s working group on the Aggregate Implications of Microeconomic Consumption Behavior. Originally from Knoxville, Tennessee, I received my A.B. in Economics from Harvard University in 1986 and a Ph.D. from the Massachusetts Institute of Technology in 1990. After graduating from M.I.T., I worked at the Federal Reserve Board in Washington DC, where I prepared forecasts for consumer expenditure. I moved to Johns Hopkins University in 1995 and also spent 1997-98 working at the Council of Economic Advisors in Washington, where I analyzed Social Security reform proposals, tax and pension policy, and bankruptcy reform. Aside from my current work at Hopkins and the NBER, I am also an associate editor at the Review of Economics and Statistics,(ReStat) the Journal of Business and Economic Statistics, (JBES) and the Berkeley Electronic Journal of Macroeconomics (BEJM).

My research has primarily focused on consumption and saving behavior, with an emphasis on reconciling the empirical evidence from both microeconomic and macroeconomic sources with theoretical models. (In addition to articles in economic journals, I’ve authored Encyclopedia Britannica articles on consumption related topics.) My most recent research has focused on the dynamics of expectations formation, particularly on how expectations reflect households’ learning from each other and from experts. This focus flows from a career-long interest in consumer sentiment and its determinants.

Jacquelyn Kazil
CSS PhD Student
Title: Mesa, Agent-based modeling library in Python 3

Abstract:
Python has grown significantly in the scientific community, but there is no tool or reusable framework to do agent-based modeling (ABM) in Python. While there are well-established frameworks in other languages, the lack of one in the Python language is at odds with the growth of Python in the scientific community. As a result, we created an ABM framework called Mesa in Python 3 with sustained contributions. Mesa is built to be modular, so the backend server, the frontend visualization and tooling, the batch runner, and the data collector are each separate components that can be upgraded independently from each other. In addition to this, Mesa is extensible and meant to be decoupled from domain specific add-ons. This empowers the community to develop features and add-ons independent of the core Mesa library. In this talk, Jackie will set the stage for her Ph.D by providing an overview Mesa’s past, present, and proposed future, along with how that fits in the ABM ecosystem of other tooling.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Dr. Chang-Tien Lu, Professor
Department of Computer Science at Virginia Tech

Monday, April 2, 4:30-5:45
Exploratory Hall, Room 3301

Title: Spatiotemporal Event Forecasting in Social Media

Abstract:

Social media has become a popular data source as a surrogate for monitoring and detecting events. Analyzing social media (e.g., tweets) to reveal event information requires sophisticated techniques. Tweets are written in unstructured language and often contain typos, non-standard acronyms, and spam. In addition to the textual content, Twitter data form a heterogeneous information network where users, tweets, and hashtags have mutual relationships. These features pose technical challenges for designing event detection and forecasting methods. In this talk, I will present the design and implementation of EMBERS, a fully automated 24×7 forecasting system for significant societal events using open source data including tweets, blog posts, and news articles. I will describe the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion engine that supports trading off specific evaluation criteria. I will also demonstrate the superiority of EMBERS over base rate methods and its capability to forecast significant societal happenings.

Bio:

Chang-Tien Lu is a Professor of Computer Science and Associate Director of the Discovery Analytics Center at Virginia Tech. He received his Ph.D. from the University of Minnesota at Twin Cities in 2001. He served as Program Chair of the 18th IEEE International Conference on Tools with Artificial Intelligence in 2006, and General Chair of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems in 2009 and the International Symposium on Spatial and Temporal Databases in 2017. He also served as Secretary (2008-2011) and Vice Chair (2011-2014) of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL). His research interests include spatial databases, data mining, urban computing, and intelligent transportation systems. He has published over 120 articles in top rated journals and conference proceedings. His research has been supported by NSF, NIH, DoD, IARPA, VDOT, and DCDOT. He is an ACM Distinguished Scientist and Virginia Tech College of Engineering faculty fellow.

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Tom Pike, CSS PhD Student
George Mason University

Title: Operationalizing Agent Based Models: Introducing Mesa Packages

Friday, April 6, 3:00PM
Center for Social Complexity Suite
3rd Floor, Research Hall

Abstract:
        Packages of code are ubiquitous across Object Oriented Programming. Few, if anyone, writing an application programs everything from scratch, instead they rely on optimized packages. Yet, for Agent Based Models the emergent behavior observed in articles or even in class (e.g. CSS 600) is not integrating optimized agent cognition packages or opinion diffusion packages into their Netlogo, MASON, MESA or other ABM platform instead it is using the platform and then coding their ABM from scratch or “hacking” an existing” model. This begs the question, could ABMs also have optimized packages of various agent dynamics so modelers could more easily alter data sources, compare the effects of different agent cognitions or conduct any other myriad of combinations to develop more complex models?

        A repository of optimized packages compatible with existing ABM platforms would help catalyze different uses of ABMs. First, it would make it easier to integrate ABMs into policy discussions and policy development as individuals could more easily bring in complex agent behavior to their specific problem. In one sense, this approach creates technical bridge between practitioners and researchers as observed in machine learning and artificial intelligence. Why not the same for the social sciences and policy development? Second, it would help researchers who could now compare different combinations of accepted theories against real world data. Or, allow researchers to focus on one aspect of a complex system as they no longer must code the rest of the system dynamics themselves.

        In support of this objective, this presentation will introduce Mesa Packages a distributed repository designed to help share packages for Mesa and discuss one package based on coalition game theory to provide a specific example of how Mesa Packages can work.

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS 

Dr. Hanan Samet, Professor
Department of Computer Science at University of Maryland

Monday, April 9, 4:30-5:45
Exploratory Hall, Room 3301
Title: Reading News with Maps by Exploiting Spatial Synonyms

Abstract:

NewsStand is an example application of a general framework to enable people to search for information using a map query interface, where the information results from monitoring the output of over 10,000 RSS news sources and is available for retrieval within minutes of publication.  The advantage of doing so is that a map, coupled with an ability to vary the zoom level at which it is viewed, provides an inherent granularity to the search process that facilitates an approximate search. This distinguishes it from today’s prevalent keyword-based conventional search methods that provide a very limited facility for approximate searches and which are realized primarily by permitting a match via use of a subset of the keywords.  However, it is often the case that users do not have a firm grasp of which keyword to use, and thus would welcome the search to also take synonyms into account.  For queries to spatially-referenced data, the map query interface is a step in this direction as the act of pointing at a location (e.g., by the appropriate positioning of a pointing device) and making the interpretation of the precision of this positioning specification dependent on the zoom level is equivalent to permitting the use of spatial synonyms (i.e., letting spatial proximity play a role rather than only seeking an exact match of a query string). Of course, this is all predicated on the use of a textual specification of locations rather than a geometric one, which means that one must deal with the potential for ambiguity.

The issues that arise in the design of a system like NewsStand, including the identification of words that correspond to geographic locations, are discussed, and examples are provided of its utility. More details can be found in the video at http://vimeo.com/106352925 which accompanies the “cover article” of the October 2014 issue of the Communications of the ACM about NewsStand at http://tinyurl.com/newsstand-cacm or a cached version at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf

Bio:

Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies.  He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems, computer graphics, computer vision, image processing, games, robotics, and search.  He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof-carrying code. He is the author of the recent book “Foundations of Multidimensional and Metric Data Structures” (http://www.cs.umd.edu/~hjs/multidimensional-book-flyer.pdf) published by Morgan-Kaufmann, an imprint of Elsevier, in 2006, an award winner in the 2006 best book in Computer and Information Science competition of the Professional and Scholarly Publishers (PSP) Group of the American Publishers Association (AAP), and of the first two books on spatial data structures “Design and Analysis of Spatial Data Structures”, and “Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS”, both published by Addison-Wesley in 1990.  He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and Systems (TSAS), the founding chair of ACM SIGSPATIAL, a recipient of a Science Foundation of Ireland (SFI) Walton Visitor Award at the Centre for Geocomputation at the National University of Ireland at Maynooth (NUIM), 2009 UCGIS Research Award, 2010 CMPS Board of Visitors Award at the University of Maryland, 2011 ACM Paris Kanellakis  Theory and Practice Award, 2014 IEEE Computer Society Wallace McDowell Award, and a Fellow of the ACM, IEEE, AAAS, IAPR (International Association for Pattern Recognition), and UCGIS (University Consortium for Geographic Science).  He received best paper awards in the 2007 Computers & Graphics Journal, the 2008 ACM SIGMOD and SIGSPATIAL ACMGIS Conferences, the 2012 SIGSPATIAL MobiGIS Workshop, and the 2013 SIGSPATIAL GIR Workshop, as well as a best demo paper award at the 2011 and 2016 SIGSPATIAL ACMGIS Conferences.  His paper at the 2009 IEEE International Conference on Data Engineering (ICDE) was selected as one of the best papers for publication in the IEEE Transactions on Knowledge and Data Engineering.  He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and was an ACM Distinguished Speaker for the term 2008-2015.