Calendar

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

Karl Battams
Bachelor of Science – Astrophysics, University College London, 2002
Master of Science – Computational Sciences, George Mason University, 2008

Reduction and Synopses of Multi-Scale Time Series with Applications to Massive Solar Data

Monday, July 30, 2018, 11:00 a.m.
Exploratory Hall, Room 3301

All are invited to attend.

Committee
Robert Weigel, Dissertation Director/Chair
Jie Jhang
Robert Meier
Huzefa Rangwala

In this dissertation, we explore new methodologies and techniques applicable to aspects of Big Solar Data to enable new analyses of temporally long, or volumetrically large, solar physics imaging data sets. Specifically, we consider observations returned by two space-based solar physics missions – the Solar Dynamics Observatory (SDO) and the Solar and Heliospheric Observatory (SOHO) – the former operating for over 7-years to date, returning around 1.5 terabytes of data daily, and the latter having been operational for more than 22-years to date. Despite ongoing improvements in desktop computing performance and storage capabilities, temporally and volumetrically massive datasets in the solar physics community continue to be challenging to manipulate and analyze. While historically popular, but more simplistic, analysis methods continue to provide new insights, the results from those studies are often driven by improved observations rather than the computational methods themselves. To fully exploit the increasingly high volumes of observations returned by current and future missions, computational methods must be developed that enable reduction, synopsis and parameterization of observations to reduce the data volume while retaining the physical meaning of those data.

In the first part of this study we consider time series of 4 – 12 hours in length extracted from the high spatial and temporal resolution data recorded by the Atmospheric Imaging Assembly (AIA) instrument on the NASA Solar Dynamics Observatory (SDO). We present a new methodology that enables the reduction and parameterization of full spatial and temporal resolution SDO/AIA data sets into unique components of a model that accurately describes the power spectra of these observations. Specifically, we compute the power spectra of pixel-level time series extracted from derotated AIA image sequences in several wavelength channels of the AIA instrument, and fit one of two models to their power spectra as a function of frequency. This enables us to visualize and study the spatial dependence of the individual model parameters in each AIA channel. We find that the power spectra are well-described by at least one of these models for all pixel locations, with unique model parameterizations corresponding directly to visible solar features. Computational efficiency of all aspects of this code is provided by a flexible Python-based Message Passing Interface (MPI) framework that enables distribution of all workloads across all available processing cores. Key scientific results include clear identification of numerous quasi-periodic 3- and 5-minute oscillations throughout the solar corona; identification and new characterizations of the known ~4.0-minute chromospheric oscillation, including a previously unidentified solar-cycle driven trend in these oscillations; identification of “Coronal Bullseyes”, that present radially decaying periodicities over sunspots and sporadic foot-point regions, and of features we label “Penumbral Periodic Voids”, that appear as annular regions surrounding sunspots in the chromosphere, bordered by 3- and 5-minute oscillations but exhibiting no periodic features.

The second part of this study considers the entire mission archive returned by the Large Angle Spectrometric Coronagraph (LASCO) C2 instrument, operating for more than 20-years on the joint ESA/NASA Solar and Heliospheric Observatory (SOHO) mission. We present a technique that enables the reduction of this entire data set to a fully calibrated, spatially-located time series known as the LASCO Coronal Brightness Index (CBI). We compare these time series to a number concurrent solar activity indices via correlation analyses to indicate relationships between these indices and coronal brightness both globally across the entire corona, and locally over small spatial scales within the corona, demonstrating that the LASCO observations can be reliably used to derive proxies for a number of geophysical indices. Furthermore, via analysis of these time series in the frequency domain, we highlight the effects of long-time scale variability in long solar time series, considering sources of both solar origin (e.g., solar rotation, solar cycle) and of instrumental/operation origin (e.g., spacecraft rolls, stray light contamination), and demonstrate the impact of filtering of temporally long time series to reduce the impacts of these uncertain variables in the signals. Primary findings of this include identification of a strong correlation between coronal brightness and both Total and Spectral Solar Irradiance leading to the development of a LASCO-based proxy of solar irradiance, as well as identification of significant correlations with several other geophysical indices, with plausible driving mechanisms demonstrated via a developed correlation mapping technique. We also determine a number of new results regarding LASCO data processing and instrumental stray light that important to the calibration of the data and have important impacts on the long-term stability of the data.

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

Kieran Marray, Laidlaw Scholar 
St. Catherine’s College
University of Oxford

FORTEC: Forecasting the Development of Artificial Intelligence up to 2050 Using Agent-Based Modeling

Friday, August 31, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Kieran is a Laidlaw Scholar from St Catherine’s College, University of Oxford. He has been visiting the Center for Social Complexity over the summer to do research in complexity economics supervised by Professor Rob Axtell.

Due to a welcome reception for new and returning CDS student, there will be no colloquium on Friday, September 7. The next one will be held on Friday, September 14.  Speaker and topic to be announced later.

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

William Kennedy, PhD, Captain, USN (Ret.)
Research Assistant Professor
Center for Social Complexity
Computational and Data  Sciences
College of Science

Characterizing the Reaction of the Population of NYC to a Nuclear WMD

Friday, September 14, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  This talk will again review the status of our multi-year project to characterize the reaction of the population of a US megacity to a nuclear WMD event 2 years into the project. Our approach has been to develop an agent-based model of the New York City area, with agents representing each of the 23 million people, and establish a baseline of normal behaviors before exploring the population’s reactions to small (5-10Kt) nuclear weapon explosions. We have the modeled the environment, agents, and their interactions, but there have been some challenges in the last year. I’ll review our status, successes, and challenges as well as near term plans.

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

Michael Eagle, Asst. Professor
Department of Computational and Data Sciences
George Mason University

Understanding Behavior in Interactive Environments: Deriving Meaningful Insights from Data 

Friday, September 21, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Advanced learning technologies are transforming education as we know it. These systems provide a wealth of data about student behavior. However, extracting meaning from such datasets is a challenge for researchers; and often impossible for the instructors. Understanding learner behavior is critical to finding, extracting, and acting on insight found in educational data. It is equally important to have strong evaluative methodologies to explore the effectiveness of new interventions, and pinpoint when, where, and precisely what students are learning.

This talk covers the ways I have combined human modeling, qualitative, quantitative (statistical and machine learning) methods enable researchers to make sense of behavior and to produce data-driven personalization. I will have a focus on modeling of humans in interactive problem-solving environments, such as intelligent tutoring systems, online courses, and educational video games. Combining results from experimental design, machine learning, and cognitive models results in large improvements to existing learning systems, as well as powerful insights for instructors and researchers on how students behave and learn in interactive environments.

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.

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

Brant Horio
Director, Data Science at LMI/CSS PhD Student

The Pedagogy of Zombies: A Case Study of Agent-Based Modeling Competitions for Introducing Complexity, Simulation, and its Real-World Applications

Friday, October 26, 3:00 p.m.
Center for Social Complexity, 3rd Floor Research Hall

All are welcome to attend.

Abstract:  Complexity is pervasive in our daily lives and while academic programs exist to explore, interpret, experiment with, and apply these concepts to better understand the mechanics of our social world, the field is yet to be widely recognized in the mainstream consciousness. Are there engaging instructional methods and tools that can leverage a lower barrier to entry and indoctrinate new scholars into the science of complexity? In this Halloween-themed talk, I present a use case of a simulation modeling competition (and its evolution over several years) that provided preprogrammed agent-based models of a zombie apocalypse. Participants were challenged to explore and formalize human agent behaviors that leveraged their environment and other human agent-agent interactions to hide, evade, and otherwise prevent a grisly human extinction. I will describe the successes and challenges of this experience and a selection of the most creative solutions. I then go on to describe how this competition concept was extended to contemporary challenges that highlighted for participants potential real-world use cases that included combating the zika virus, and fisheries enforcement by the US Coast Guard. I hope for this talk to initiate dialog for how we might continue similar efforts to more easily introduce and propagate the complexity perspective.