Calendar

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

Michael Eichler
Strategic Planning Advisor
Metro Office of Planning
WMATA

Metrorail and Metrobus, Data Sources and Information Needs

Monday, February 12,  4:30-5:45
Exploratory Hall, Room 3301

Abstract:  Modernization of nearly all the technology that underlies the provision of rail and bus transit service over the past 30 years has resulted in a vast amount of data that until recently has been more or less neglected.  Meanwhile, challenges that face rail and bus transit systems continue to mount, from maintaining a state of good repair to capturing and keeping riders in the age of Uber/Lyft and bike share.  The key to providing safe, convenient, affordable, and reliable transit service into the next century lies in the hands of data scientists and policy analysts.  This talk will review the different data-generating technologies and the types of data they create, followed by an exploration of the pressing issues faced by transit agencies and the questions begging for answers. 

Bio:    Michael currently serves as Strategic Planning Advisor at WMATA in the Office of Planning’s Applied Planning Intelligence unit, where he focuses on transforming data into information to help inform policy and planning decisions.  He currently focuses on fare policy, crowding, GTFS data and online tools, and customer-focused performance metrics.  Before joining WMATA in 2010, he worked for Oracle Corporation, an IT start-up, and the Metropolitan Washington Council of Governments.   He holds a BS in Systems Analysis and Engineering from The George Washington University, and masters in City and Regional Planning and Transportation Engineering from UC Berkeley. 

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS

 Sean Mallon, Associate Vice President
Entrepreneurship and Innovation
George Mason University
and
Eric Koefoot
Founder and CEO of PublicRelay

The Journey and Stories of a Data Science Entrepreneur

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

This session will feature conversation between Sean Mallon, Mason’s AVP for Entrepreneurship and Innovation, and Eric Koefoot, founder and CEO of PublicRelay, a venture-backed data analytics and media intelligence startup based in McLean, VA. During the discussion we will explore a wide range of topics, ranging from what inspired the initial business idea, to customer discovery, to product development challenges, to fundraising, to customer acquisition strategies, and much more. This will be a highly interactive seminar and participants are encouraged to come with questions and personal experiences to share.

Sean Mallon, Associate Vice President, Entrepreneurship and Innovation, Office of the Provost. Photo by Ron Aira/Creative Services/George Mason University

Sean Mallon Bio: Sean Mallon is Mason’s Associate Vice President for Entrepreneurship and Innovation. Before joining Mason in 2016, Sean spent many years as an entrepreneur and early-stage technology investor. Sean hold a Bachelor’s in History from Princeton and an MBA from the Wharton School of the University of Pennsylvania.

Eric Koefoot Bio: Formerly the CEO of U.S. News Ventures, CEO at Five Star Alliance, CFO and later VP Global Sales at Washington Post Digital, Eric is the founder and CEO of PublicRelay and brings substantial media experience and understanding. Eric holds a Bachelor’s in Engineering from MIT and an MBA from the Sloan School at MIT.

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.

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….

COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS 

Olga Papaemmanouil, Associate Professor
Department of Computer Science at Brandeis University

Data Management Expert Discussion Seminar:
Learning-based Cost Management for Cloud Databases

Monday, April 16, 4:30-5:45
Exploratory Hall, Room 3301

Cloud computing has become one of the most active areas of computer science research, in large part because it allows computing to behave like a general utility that is always available on demand. While existing cloud infrastructures and services reduce significantly the application development time, significant effort is still required by cloud data management applications to manage their monetary cost, for often this cost depends on  a number of decisions including but not limited to performance goals, resource provisioning and workload allocation. These tasks depend on the application-specific workload characteristics and performance objectives and today their implementation burden is left on application developers.  

We argue for a substantial shift away from human-crafted solutions and towards leveraging machine learning algorithms to address the above challenges.   These algorithms can be trained on application-specific properties and customized performance goals to automatically learn how to provision resources as well as schedule the execution of incoming query workloads with low cost. Towards this vision, we have developed WiSeDB, a  learning-based cost  management service for cloud-deployed data management applications. In this talk, I will discuss how WiSeDB leverages  (a) supervised learning to automatically learn cost-effective models for guiding query placement, scheduling, and resource provisioning decisions for batch processing, and (b) reinforcement learning  to offer low cost online processing solutions, while being adaptive to resource availability and decoupled from notoriously inaccurate performance prediction models.

Speaker Bio:  Dr. Papaemmanouil is an Associate Professor in the Department of Computer Science at Brandeis University. Her research interest lies in the area of data management with a recent focus on cloud databases, data exploration, query optimization and query performance prediction. She received her undergraduate degree in Computer Science and Informatics at the University of Patras, Greece in 1999. In 2001, she received her Sc.M. in Information Systems at the University of Economics and Business, Athens, Greece. She then joined the Computer Science Department at Brown University, where she completed her Ph.D in Computer Science at Brown University in 2008. She is the recipient of an NSF Career Award (2013) and a Paris Kanellakis Fellowship from Brown University (2002)

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Robert Axtell, Professor
Computational Social Science Program,
Department of Computational and Data Sciences
College of Science
and
Department of Economics
College of Humanities and Social Sciences
George Mason University

Are Cities Agglomerations of People or of Firms? Data and a Model

Friday, September 28, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Business firms are not uniformly distributed over space. In every country there are large swaths of land on which there are very few or no firms, coexisting with relatively small areas on which large numbers of businesses are located—these are the cities. Since the dawn of civilization the earliest cities have husbanded a variety of business activities. Indeed, often the raison d’etre for the growth of villages into towns and then into cities was the presence of weekly markets and fairs facilitating the exchange of goods. City theorists of today tend to see cities as amalgams of people, housing, jobs, transportation, specialized skills, congestion, patents, pollution, and so on, with the role of firms demoted to merely providing jobs and wages. Reciprocally, very little of the conventional theory of the firm is grounded in the fact that most firms are located in space, generally, and in cities, specifically. Consider the well-known facts that both firm and city sizes are approximately Zipf distributed. Is it merely a coincidence that the same extreme size distribution approximately describes firm and cities? Or is it the case that skew firm sizes create skew city sizes? Perhaps it is the other way round, that skew cities permit skew firms to arise? Or is it something more intertwined and complex, the coevolution of firm and city sizes, some kind of dialectical interplay of people working in companies doing business in cities? If firm sizes were not heavy-tailed, but followed an exponential distribution instead, say, could giant cities still exist? Or if cities were not so varied in size, as they were not, apparently, in feudal times, would firm sizes be significantly attenuated? In this talk I develop the empirical foundations of this puzzle, one that has been little emphasized in the extant literatures on firms and cities, probably because these are, for the most part, distinct literatures. I then go on to describe a model of individual people (agents) who arrange themselves into both firms and cities in approximate agreement with U.S. data.

Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences

Gonzalo Castañeda
Visiting Scholar, Interdisciplinary Center for Economic Science
George Mason University/Centro de Investigación y Docencia Económica (CIDE), México

 How do governments determine policy priorities?
Studying development strategies through spillover networks

Friday, October 5, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract: Determining policy priorities is a challenging task for any government because there may be, for example, a multiple objectives to be simultaneously attained, a multidimensional policy space to be explored, inefficiencies in the implementation of public policies, interdependencies between policy issues, etc. Altogether, these factors generate a complex landscape that governments need to navigate in order to reach their goals. To address this problem, we develop a framework to model the evolution of development indicators as a political economy game on a network. Our approach accounts for the –recently documented–network of interactions between policy issues, as well as the well-known political economy problem arising from budget assignment. This allows us to infer not only policy priorities, but also the effective use of resources in each policy issue. Using development indicators data from more than 100 countries over 11 years, we show that the country-specific context is a central determinant of the effectiveness of policy priorities. In addition, our model explains well-known aggregate facts about the relationship between corruption and development. Finally, this framework provides a new analytic tool to generate bespoke advice on development strategies.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Maciej Latek, Chief Technology Officer, trovero.io./
Ph.D. in Computational Social Science 2011
George Mason University

Industrializing multi-agent simulations:
The case of social media marketing, advertising and influence campaigns

Friday, October 12, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  System engineering approaches required to transition multi-agent simulations out of science into decision support share features with AI, machine learning and application development, but also present unique challenges. In this talk, I will use trovero as an example to illustrate how some of these challenges can be addressed.

As platform to help advertisers and marketers plan and implement campaigns on the social media, trovero is comprised of social network simulations for optimization and automation and network population synthesis used to preserve people’s privacy while maintaining a robust picture of social media communities. Social network simulations forecast campaign outcomes and pick the right campaigns for given KPIs. Simulation is the only viable way to reliably forecast campaign outcomes: Big data methods fail to forecast campaign outcomes, because they are fundamentally unfit for social network data. Network population synthesis enables working with aggregate data without relying on data sharing agreements with social media platforms that are ever more reluctant to share user data with third parties after GDPR and the Cambridge Analytica debacle.

I will outline how these two approaches complement one another, what computational and data infrastructure is required to support them and how workflows and interactions with social media platforms are organized.

Computational Social Science Research Colloquium /
Colloquium in Computational and Data Sciences

Ken Kahn, Senior Researcher
Computing Services
University of Oxford

Agent-based Modelling for Everyone

Friday, October 19, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor Research Hall

All are welcome to attend.

Abstract: Agent-based models (ABMs) can be made accessible to a wide audience. A wonderful example is the Parable of the Polygons (https://ncase.me/polygons/) based upon Schelling’s segregation model. The challenge isn’t simply to provide an interactive simulation to the general public but to convey how the model works and what assumptions underlie it. The speaker has been involved in three efforts to do more than make the models but understandable but also to enable people without computer programming experience to get a hands-on understanding of the process of modelling. One project attempted to model the 1918 Pandemic in a modular fashion so learners could understand and modify the model. Another was the Epidemic Game Maker which was created for a Royal Society science exhibition. Finally a generic browser-based system for creating ABMs by composing and customising pre-built “micro-behaviours” will be described. All of these systems will be demonstrated.