With the start of the fall semester, the Department welcomes its newest faculty member, Dr. Michael Eagle, who earned his PhD in Computer Science from North Carolina State University. Dr. Eagle’s main area of research is focused on deriving insight about human behavior using data-driven methods, and using that insight to create new and better systems. His specific focus is on complex interactive environments, such as video games and intelligent tutoring systems.
For the Fall 2018, Dr. Eagle will be teaching CDS 303 -Scientific Data Mining.
Recently, Billy Lamberti, CSI PhD student, attended the 2018 Joint Statistical Meetings held in Vancouver, BC where he sat in on a workshop that provided additional tools for graduate students to teach Statistics and Data Science courses. The workshop was funded by the American Statistical Association and NSF and required the participants to apply and be accepted. Billy also chaired a session for the Statistical Imagining group on Statistical Analysis of Complex-Valued MRI. He presented his own Stratified Over Representative k- folds Cross-Validation algorithm later during the week and attended other sessions on topics including statistical image analysis, analyzing fMRI data, and statistical analysis techniques.
To support his trip, he applied for and received Graduate Student Travel Funds. The Graduate Student Travel Fund (GSTF) provides financial assistance for graduate students to present their work at academic conferences. The program is administered by the Office of the Provost.
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
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.
Robert Weigel, Dissertation Director/Chair
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.
Suchismita Goswami, Computational Sciences and Informatics PhD Candidate, will be presenting a paper she co-authored with Dr. Edward Wegman. The paper entitled Network Neighborhood Analysis for Detecting Anomalies Using Scan Statistics of Time Series of Graphs will be presented at the 4th International Conference on Computational Social Science(IC2S2 2018), Northwestern University’s Kellogg School of Management,12-15 July, 2018.