Department of Computer Science and Electrical Engineering

We are now offering the UMBC Cybersecurity MPS program at Shady Grove in Montgomery County, MD.

The Cybersecurity Master’s in Professional Studies degree provides students the essential knowledge required to serve in leadership and operational roles throughout the industry. Through the program, students will learn how to analyze cybersecurity risks and assess available countermeasures. The program will expose students to practical managerial and operational considerations needed to conduct cybersecurity activities for large organizations.

PhD Dissertation Defense

Analysis of brain network connectivity
using spatial information

Sai Ma

1:00pm Thursday, 18 April 2013, ITE 325b

In current functional magnetic resonance imaging (fMRI) research, one of the most active areas involves exploring statistical dependencies among brain regions, known as functional connectivity analysis. Data-driven methods, especially independent component analysis (ICA), have been successfully applied to fMRI data to extract distributed brain networks and offer an opportunity to investigate functional connectivity on a network level, thus at a multivariate level. However, the independence assumption in ICA is neither necessarily nor typically satisfied in real applications and an extension is desirable. Furthermore, most current ICA-based studies focus on the use of temporal information and second-order statistics for functional connectivity analysis. Taking spatial information and higher-order statistics in fMRI data into account is expected to lead to better understanding of the overall brain network connectivity in healthy controls and also in patients with mental disorders, such as schizophrenia.

We develop a dependent component analysis (DCA) framework to generalize the ICA-based connectivity analysis methods by grouping components into maximally independent clusters. First, we define functional network connectivity as the statistical dependence among spatial components, instead of the typically used temporal correlation. Based on this definition, we use a hypothesis test to automatically generate functional connectivity structure for a large number of brain networks. After that, we separate dependent components within a given cluster using prior information, such as sparsity and experimental paradigm information, to achieve a better decomposition. We also combine this DCA-based clustering analysis with graph-theoretical analysis to discover significant group differences in topological properties of functional connectivity structure. To extend the methodologies currently available for functional connectivity, we propose an independent vector analysis (IVA) based scheme to extract and analyze dynamic functional connectivity.

The methods we develop offer advantages for effective and efficient examination of not only static, but also dynamic functional connectivity among different brain networks. We identify significant differences in functional connectivity structure between healthy controls and patients with schizophrenia, which may prove useful to serve as potential biomarkers for diagnosis. We also find task-induced modulations in functional connectivity when comparing different active states in the brain. Furthermore, we observe temporal variability in functional connectivity structure and physiologically meaningful group differences in dynamic connectivity among several brain networks. Our methods can provide insights to understanding of functional characteristics of the brain network organization in healthy individuals and patients with schizophrenia.

Committee: Dr. Adali (Chair), Dr. Morris, Dr. Rutledge, Dr. LaBerge, Dr. Phlypo, Dr. Calhoun, and Dr. Westlake

PhD Dissertation Defense

Data-driven group analysis of complex-valued fMRI data

Pedro A. Rodriguez

11:00am Tuesday, 16 April 2013, ITE 346, UMBC

Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity of the analysis both for data-driven techniques such as independent component analysis (ICA) and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge for successful study of the fMRI data. In addition, complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis and visualization of the results. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The developed methods become key components of a framework that allows the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data.

In this dissertation, we first introduce the methods developed as part of the fully complex framework for ICA of fMRI data. We introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy voxels—3D pixels—in the fMRI complex images so they can be used in individual and group studies. We also introduce a phase correction scheme that can be either applied sub-sequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. Finally, we present two visualization methods that are used to augment the sensitivity and specificity in the detection of activated voxels. We show the benefits of using the developed methods on actual complex-valued fMRI data.

In the remainder of the dissertation, we focus on developing constrained ICA (C-ICA) algorithms for complex-valued fMRI data. C-ICA uses prior information, hence providing a balance between model-based and data-driven approaches such as ICA to improve the source estimation performance and robustness to noise. C-ICA algorithms have been used to improve the estimation performance in real-valued fMRI data, but—to our knowledge—have not been applied to complex-valued fMRI data. We develop the first C-ICA algorithm that uses complex-valued references to constrain either the sources or the mixing coefficients. The designed algorithm is not restricted to having a unitary demixing matrix, which is a major assumption in existing C-ICA algorithms. We show, on both simulated and actual fMRI data, how the performance of ICA improves by using prior information about the fMRI paradigm.

Committee: Dr. Adali (Chair), Dr. Morris, Dr. Rutledge, Dr. Laberge, Dr. Phlypo, and Dr. Calhoun

The CSEE Department will hold a "town hall" meeting for undergraduate COmputer Science (CMSC) majors, minors and other interested students in ITE 456 from 12:00 to 2:00 on Thursday, April 18.

This is an opportunity to interact with your department chair, Professor Gary Carter, the CMSC undergraduate program director Professor Marc Olano and other faculty members. During the meeting you will hear about recent developments in the department and CMSC program, and have opportunities to express opinions, raise issues, make suggestions, ask questions and discuss how to make the CMSC program better. There will also be pizza and drinks.

If you plan on attending, please send an email message to so we can be sure to order enough food. If you have any questions or topics that you would like to raise in advance, send them to . We look forward to a lively and useful event where the communication flows both ways.

PhD Dissertation Defense

Independent Vector Analysis:
Theory, Algorithms, and Applications

Matthew Anderson

1:45pm Wednesday, 17 April 2013, ITE 325B

The field of blind source separation (BSS) is a well studied discipline within the signal processing community due to its applicability to a variety of problems when the data observation model is poorly known or difficult to model. For example, in the study of the human brain with functional magnetic resonance imaging (fMRI), a neuroimaging sensor, BSS algorithms are able to provide medical researchers and practitioners with a decomposition of a three-dimensional ‘movie’ of the brain that is amenable to analysis. BSS algorithms achieve this decomposition with only a few justifiable assumptions; this is contrary to methods based on the general linear model, which require prespecified models of the expected or desired response to achieve analysis of fMRI data.

Most BSS algorithms consider just a single dataset, but it also desirable to have methods that can analyze multiple subjects or data collections in fMRI jointly, so as to provide insights beyond that achieved with individual analysis of single datasets. Several frameworks for using BSS on multiple datasets jointly have been proposed. The subject of this dissertation is the study of one of these frameworks, which has been termed independent vector analysis (IVA). IVA is a recent extension of the classical independent component analysis (ICA) model to BSS of multiple datasets and it has been the subject of significant research interest. In this dissertation, we provide a formulation of IVA that accounts for sources which possess properties such as a) following Gaussian or non-Gaussian distributions; b) samples are independently and identically distributed (iid) or are dependent; and c) having either linear or nonlinear dependence of sources between datasets. The proposed IVA formulation utilizes the likelihood to define the objective function. This formulation admits to theoretical analysis. In particular, we provide the identification conditions, i.e., we determine when the sources can be ‘blindly’ recovered by IVA, and give a lower bound on the source separation performance.

Several algorithms exist for achieving IVA. We provide several new approaches to developing IVA algorithms and apply these approaches using a Gaussian distribution source model and a more general Kotz distribution model. The former, in addition to leading to efficient IVA algorithms, serves as the distribution model that directly connects canonical correlation analysis (CCA) and ICA.  

Committee: Dr. Tulay Adali (Chair), Dr. Joel Morris, Dr. Aninyda Roy, Dr. Ronald Phlypo, and Dr. Mike Novey

Dissertation Defense

Digital Forensics for
Infrastructure-as-a-Service Cloud Computing

Josiah Dykstra

10:00am Tuesday, 16 April 2013, ITE 325b

We identify important issues in the application of digital forensics to Infrastructure-as-a-Service cloud computing and develop new practical forensic tools and techniques to facilitate forensic exams of the cloud. When investigating suspected cases involving cloud computing, forensic examiners have been poorly equipped to deal with the technical and legal challenges. Because data in the cloud are remote, distributed, and elastic, these challenges include understanding the cloud environment, acquiring and analyzing data remotely, and applying the law to a new domain. Today digital forensics for cloud computing is challenging at best, but can be performed in a manner consistent with federal law using the tools and techniques we developed.

The first problem is understanding how and why criminal and civil actions in and against cloud computing are unique and difficult to prosecute. We analyze a digital forensic investigation of crime in the cloud, and present two hypothetical case studies that illustrate the unique challenges of acquisition, chain of custody, trust, and forensic integrity. Understanding these issues introduces legal challenges which are also important for federal, state, and local law enforcement who will soon be called upon to conduct cloud investigations.

The second problem is the lack of practical technical tools to conduct cloud forensics. We examine the capabilities for forensics today, evaluate the use of existing tools including EnCase and FTK, and discuss why these tools are incapable of trustworthy cloud acquisition. We design consumer-driven forensic capabilities for OpenStack, including new features for acquiring trustworthy firewall logs, API logs, and disk images.

The third problem is a deficit of legal instruments for seizing cloud-based electronically-stored information. We analyze the application of existing policies and laws to the new domain of cloud computing by analyzing case law and legal opinions about digital evidence discovery, and suggest modifications that would enhance cloud the prosecution of cloud-based crimes. We offer guidance about how to author a search warrant for cloud data, and what pertinent data to request.

This dissertation enhances our understanding of technical, trust, and legal issues needed to investigate cloud-based crimes and offers new tools and techniques to facilitate such investigations.

Committee: Dr. Alan T. Sherman (Chair), Dr. Charles Nicholas, Dr. Richard Forno, Dr. Simson Garfinkel (Naval Postgraduate School), Mr. Donald Flynn, JD (Department of Defense Cyber Crime Center)

The elections for both the IEEE Undergraduate Student Branch and IEEE Graduate Student Branch Executive Boards have been scheduled for Friday, April 19th, 4.00pm to 5.00pm, room TBA. This is our regular meeting time. Below you will find more information on the election process, including the procedure to run for an Officer position.

IEEE Undergraduate Student Branch Officer positions include the following.

• Chair: Will be responsible for leading the IEEE Undergraduate Student Branch overall. S/he will also be responsible for representing the IEEE USB in General Body meetings and Executive Board meetings, and delegating responsibilities for various task. Events and activities must be approved by the Chair before sent to committees and funds must be approved before being forwarded to the Treasurer.
• Vice-Chair: Will be responsible for assisting the Chair in leading the IEEE USB and helping to run the branch smoothly. If the Chair is not present, the VP will take upon her/his responsibilities.
• Secretary: Will be responsible for taking minutes at meetings, keeping track of undergraduate student attendance and reporting the activities to the branch. S/he will be the point person for external relationships.
• Treasurer: Will manage the accounts and funds for the IEEE USB. S/he’ll be responsible for attending the SGA treasurer training session, and working with the Executive Board to generate a budget plan.
• Member At Large: Will support the other executive board members by facilitating their responsibilities as well as managing member recruitment/retention, managing fundraising activities in cooperation with the Treasurer and developing and managing projects.

IEEE Graduate Student Branch Officer positions include the following.

• Chair: Will be responsible for leading the IEEE Graduate Student Branch overall. S/he will also be responsible for representing the GSO in the GSA Senate meetings and external events. Funds must be approved by the Chair before being forwarded to the Treasurer.
• Vice-Chair: Will be responsible for assisting the Chair in leading the IEEE GSB and helping her/him to run the branch smoothly.
• General Secretary: Will be responsible for reporting the activities to the IEEE SB GSA. S/he will be the point person for external relationships.
• Treasurer: Will manage the accounts and funds for the IEEE GSB. S/he’ll be responsible for the annual budget report along with the payments.

To run for one of the above positions you must be a grad/undergrad student in good academic status, be subscribed to our mailing list, and send me () a small description (no more than 100 words) about you and why should other members vote for you, no later than next Thursday, April 11th, by 12.00pm (noon). You can only run for one position.

These descriptions will be compiled and sent out to the entire mailing list no later than the following Friday, April 12th. Each candidate will be given two minutes during that Friday's meeting to make a brief speech.

The election process will be supervised by our advisor, Dr. Choa.

IMPORTANT

• if you win, you MUST be an official IEEE member or become one within a week after the election date.
• to vote, you must present your UMBC ID and the email that you used to subscribe to our mailing list.

Please come and vote on the 19th to ensure that your voice is heard, and consider running for one of the positions. If you have any concerns or questions about the election process, please let Jorge Teixeira () know ASAP.

On behalf of the UMBC IEEE GSB and UMBC IEEE USB Executive Boards,

UMBC IEEE Student Branch, Chair
Jorge Teixeira

Here is UMBC President Freeman Hrabowski's talk at the 2013 TED conference. He is, as usual, very inspirational. We are truly lucky to have him leading our university.

"If a student has a sense of self, it’s amazing how their dreams and values can make all the difference in the world."

Look for his mention of computer science and women in IT near the end.

UMBC Graduate student Puneet Sharma talks about his research on developing a multilayer framework to catch data exfiltration, 10:30 Monday April 8 in toom ITE325b at UMBC. Here is the abstract.

Data exfiltration is the unauthorized leakage of confidential data from a particular system. It is nothing but a very specific form of intrusion which is particularly hard to catch due to the most common cause; an insider entity responsible for the leak. That entity could be a real person employed in the organization, or even a malicious hardware piece bought from an unreliable third party. Catching such intrusions therefore, can be extremely difficult. What is proposed is a framework with a multitude of parameters to be constantly monitored on a system. These parameters would cover the entire stack of the computer architecture starting from the hardware up till the application layer. A more spread out and comprehensive monitoring framework should ensure that designing an attack becomes extremely difficult since the intruder must now devote significantly more time and effort to bypass the multiple checks and avoid raising alarms.

CSEE Professor Anthony Johnson has been appointed by the National Research Council to the National Academies’ Committee on Atomic, Molecular, and Optical Sciences. The CAMOS committee’s goals include providing active stewardship of the agenda laid out in the National Academies study Controlling the Quantum World and interacting with and advising U.S. federal agencies on science and technology issues involving the atomic, molecular, and optical sciences.

Dr. Johnson is the director of UMBC’s Center for Advanced Studies in Photonics Research. His research is in the area of ultrafast optics and optoelectronics- the ultrafast photophysics and nonlinear optical properties of bulk, nanoclustered, and quantum well semiconductor structures, untrashort pulse propagation in fibers and high-speed lightwave systems.