# 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.

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)

**TOWARDS RELIABLE SPATIAL AND SPATIO-TEMPORAL PATTERN ANALYSIS**

Matthias Renz

Department of Computational and Data Sciences

George Mason University

Fairfax, VA

Current technology trends, such as smart phones, mobile devices, stationary sensors and satellites, coupled with a new user mentality of voluntarily sharing information generates a huge volume of geo-spatial and geo-temporal data that might be useful for many applications. Indeed, the increasing volume of geo-spatial data from heterogeneous sources is an example of Big Data. In this talk I will present effective and efficient solutions to problems related to reliable spatial pattern analysis and mining of uncertain spatial and spatio-temporal data. These techniques can substantially improve the quality of decision making, minimize risk, and unearth valuable insights from data that would otherwise remain hidden. Use of uncertain data presents a two-fold challenge: 1) identifying potential solutions and assigning a probability to each solution such that the user is confident about the results, and 2) enabling fast computations such that the user obtains results in a reasonable time, even for large data sets.

April 25, 2016, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND

THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)

A Retrospective. 45 Years of Computational Atomic and Molecular Physics: What Have We Learned

Barry Schneider

Applied and Computational Mathematics Division

Information Technology Laboratory

National Institute of Standards and Technology

Gaithersburg, MD

Atomic and molecular physics was an early beneficiary of the development of electronic computation. Luckily, most of the interactions between electrons and nuclei are well understood and it appears that all that remains is to “turn the crank”. In retrospect, this was not that simple. These are complex many-body systems and in order to overcome the exponential scaling of these computations with the number of particles, clever algorithms and efficient codes needed to be developed. In this talk I will describe a number of the important developments that have taken place over the past four decades and how they have impacted our qualitative and quantitative understanding of scattering processes and the interaction of radiation with matter.

September 12, 2016, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)

**Understanding and controlling the magnetic exchange of novel materials**

James Glasbrenner

Department of Computational and Data Sciences, and

Computational Materials Science Center

George Mason University

Fairfax, VA

September 19, 2016, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

The behavior of electrons in materials underpins key materials properties, which means that computing and understanding the electronic structure of various materials systems is of vital importance. These computations are, nowadays, often performed using density functional theory (DFT), a first-principles methodology that is an important tool in the computational materials scientist’s toolbox. One of the materials properties that is accessible via DFT is magnetism, and DFT can be used to study the magnetic interaction between electrons (called the exchange interaction) in a material. This involves constructing models such as the Heisenberg model and mapping DFT calculations onto it, which allows one to understand how tuning different features impacts important parameters such as the critical temperature and the stability of magnetic ground state. This approach to studying magnetic materials is of particular appeal in the spin electronics field, where the encoding and processing information using the magnetic states of electrons is of central importance. In this talk I will: 1) introduce the basic concepts of computational materials science using DFT in an accessible manner, and 2) present calculations on two different materials where I used DFT in conjunction with modeling to analyze the magnetic interactions. The first presented material will be the dilute magnetic semiconductor (Ba, K)(Zn, Mn)2As2, which exhibits ferromagnetism when a small amount of manganese and potassium are substituted into the material, and where changing the relative quantity of potassium influences the strength of the magnetic interactions. The second presented material is MnAu2, a magnetic metal that has a cork-screw noncollinear magnetic ground state which can be tuned in intriguing ways using pressure and chemical substitution. Using modeling in combination with DFT, I will show how we are able to understand the nature of the microscopic magnetic interactions in each material and that the microscopic mechanisms driving the the magnetic interactions in both compounds is the same. These results can then be used to resolve several experimental questions, one of which had gone unaddressed for several decades.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)

Local Structure Analysis in Simulated Materials via Voronoi Topology

Emanuel A. Lazar

Laboratory for Research on the Structure of Matter

School of Engineering and Applied Science

University of Pennsylvania

Philadelphia, PA

Describing how atoms are arranged in real and simulated materials is a very natural problem that arises in numerous computational materials science applications. However, aside from perfect crystals, insightful yet tractable descriptions of local arrangements of atoms can be tricky to develop. We consider several conventional order-parameter methods for describing local structure and highlight their theoretical and practical limitations. We then introduce a topological approach more naturally suited for structure analysis and highlight its versatility and robustness. In particular, the Voronoi tessellation method can aid in the study of materials at high temperatures, close to melting, without uncontrolled modification of raw data. Applications to the study of grain boundary evolution and melting will be briefly presented.

September 26, 2016, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (CSI 898-Sec 001)

**Embedding Context in Intelligent User Interfaces Using Data Analytics**

Dieter Pfoser, Associate Professor

Department of Geography and Geoinformatics

George Mason University

Fairfax, VA

October 24, 2016, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Making sense of user-generated Web content such as social media data, blogs, or even Wikipedia entries poses interesting research challenge considering its lack of structure, amount, and associated noise. This work introduces a range of online content summaries for such unstructured data. Besides the typical spatial, temporal and thematic summaries, we introduce two additional views. Geoevents are emerging events that are limited in spatial scope hashtags and that are detected automatically by comparing their spatial distribution to a global topic search.

Links-of-Interests (LOIs) are connection summaries between geographic locations, people and concepts. These content summaries are available as part of the functionality of a Web-based tool that allows for the interactive visualization, querying and exploration of such unstructured data. This talk will discuss research results and demo the capabilities of the visualization prototype.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES

(CSI 898-Sec 001)

**Scientific Data Mining and Its Applications in Tropical Cyclone Research**

Ruixin Yang, Associate Professor

Department of Geography and Geoinformatics

George Mason University

Fairfax, VA

October 31, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

In this talk I will give an overview of of scientific data mining (SDM) followed by a few examples of SDM applications to the analysis of tropical cyclones (TCs), focusing on their intensity changes. Because rapidly intensifying (RI) tropical cyclones are the major error sources in TC intensity forecasting, association rules facilitate the RI process by mining for sets of conditions that have strong interactions with rapidly intensifying TCs. The technique of association rules explores associations among multiple conditions in a simple manner identifying a predictor set with fewer factors but improved RI probabilities. Furthermore, in searching the “optimal” RI condition combinations, a peculiar condition combination was identified that gives a very high RI probability. Such combination can be considered as a sufficient condition for RI that almost guarantees that a RI will take place. Applications of classification techniques to the intensity forecasting will also be discussed. Several drawbacks and future directions for SDM with the TC intensity change problem will be discussed at the end of the talk.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES

(CSI 898-Sec 001)

**Robust Estimation of Value-at-Risk for Quantitative Risk Management: Applications to Climatology, Insurance, Accidents and Financial Analysis**

Sabyasachi Guharay

U.S. Department of the Treasury

Washington, D.C.

November 7, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Establishing robust quantitative metrics which allow decision makers to determine the amount of risk in a system with extreme loss events is a problem of interest in many scientific fields. One of the fundamental metrics which is universally accepted in all fields of risk management is the quantity known as Value-at-Risk (VaR). A subfield of risk management, modern Operational Risk Management (ORM), closely investigates methodologies on robustly estimating VaR, “Robust Estimation of VaR.” Currently, academic researchers and industry practitioners are actively looking at ways to make this estimate more statistically robust and accurate with minimal assumption requirements.

In this talk I will present two new quantitative approaches for estimating VaR that are agnostic regarding the relationship between frequency and severity: (1) Data Partition of Frequency and Severity (DPFS) using K-means to estimate VaR; (2) Distribution based partitioning (DBP) of frequency and severity using copulas. Verification is conducted on five simulated scenario datasets while validation is conducted on five publicly available datasets from four different domains: –US Financial Indices data of Standard & Poor’s 500 and Dow Jones Industrial Average; –Chemical Loss spills as tracked by the US National Coast Guard; –Australian automobile accidents; –US hurricane data. It is observed that previous VaR calculations inaccurately estimate the VaR for 80% of the cases in simulated data and 60% of the cases in real-world data studies while new methodologies attains accurate VaR estimates which are within the 95% confidence interval bounds for both simulated and real-world data.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES

(CSI 898-Sec 001)

**Using Coarse-Grained Models to Access Expanded Length and Time Scales for Nanoscience Applications**

K. Michael Salerno, Jr.

Center for Computational Materials Science

Naval Research Laboratory

Washington D.C.

November 14, 4:30 pm

Exploratory Hall, Room 3301

Fairfax Campus

Polymers and other soft materials have an important role as a coating for nanoscale building blocks like metallic nanoparticles (NPs) and nanorods. This coating mediates interactions between these building blocks and their environment. Atomistic molecular dynamics (MD) simulations are ideal for examining the role of chemistry and atomic interactions at the sub-nanometer scale, for example in the interactions between a NP and a solvent, or between pairs of NPs. Unfortunately, atomistic MD simulations are limited to lengths of order 50 nm and times of order 50 ns. The time scale limitation precludes modeling nanoscale self-assembly, and limits dynamic simulations to extremely high rates of deformation or thermalforcing. These simulations are also limited to sizes that represent a small number of NPs, making it impossible to model large assembled structures.

Faced with these limitations, we have developed coarse-grained (CG) models of polyethylene, a simple polymer used to coat NPs and nanorods. These models have enabled simulations of bulk polymer melts that overcome the limits of atomistic MD by providing a computational speedup of greater than 104 while retaining fundamental details at the sub-nanometer scale. These details produce the viscoelastic properties and semi-crystalline behavior that are intrinsic to polyethylene and that are missed by generic CG models. When applied to a NP coating the CG models capture the coating morphology, indicating the value of using these CG models in nanoscale applications.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER

AND THE DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES (

CSI 898-Sec 001)

**Topological Quantification of Microstructures**

Tom Wanner

Department of Mathematical Sciences

George Mason University

Fairfax, VA

November 21, 2016, 4:30 p.m.

Exploratory Hall, Room 3301

Fairfax Campus

Many applied processes generate complex microstructures or patterns which are hard to quantify due to the lack of any underlying regular structure. These patterns may evolve with time or include some element of stochasticity. The resulting variations in the detail structure frequently force one to concentrate on rougher geometric features. From a mathematical point of view, several notions from algebraic topology suggest themselves as natural quantification tools in such a setting. In this talk I will describe some of these tools, in particular homology and persistent homology, and how they can be efficiently computed using open source software. I will also present some applications motivated by materials science problems.

Refreshments will be served at 4:15 PM.

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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm

__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

**Daniel Pulido
**Bachelor of Science, Boston University, 1998

Master of Science, Worcester Polytechnic Institute, 2003

**Self-Similar Spin Images for Point Cloud Matching
**

**Friday, October 13, 2017, 10:00 a.m.**

Research Hall, Room 162

All are invited to attend.

__Committee__Anthony Stefanidis, Dissertation Director

Estela Blaisten-Barojas

Arie Croitoru

Juan Cebral

The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. Simultaneously, the growth of other forms of geospatial data (e.g., crowdsourced Web 2.0 data) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. However, combining data from disparate sources requires overcoming numerous technical challenges in order to generate products that mitigate their respective disadvantages and combine their advantages.

Therefore, this dissertation addresses the problem of fusing two point clouds from potentially different sources by considering two specific problems: scale matching and feature matching. To address the problem of feature matching we develop a novel feature descriptor referred to as “Self-Similar Spin Images” which combine the concept of local self-similarity with the descriptive power of Spin Images. To address the problem of scale matching we develop a novel scale detection metric referred to as “Self-Similar Keyscale” which analyzes the self-similarity of two point clouds to identify a characteristic scale to match them. Finally, we develop a novel change detection method as a sample use case of the developed matching techniques.