The NIST National Cybersecurity Center for Excellence (NCCoE) is seeking full- and part-time paid interns from UMBC graduate students studying cybersecurity at the Universities at Shady Grove (USG). The program is part of NCCoE’s ongoing efforts to build and sustain academic partnerships in the Montgomery County region.

The NCCoE internship will identify and immerse students in practical cybersecurity experiences at the NCCoE in Rockville, MD. NCCoE’s Cybersecurity Graduate Researchers will work in a state-of-the-art facility with expert cybersecurity practitioners from government and academia, along with engineers from some of the largest and most influential IT and cybersecurity companies in the world, including Intel, Microsoft, Symantec, HP, Cisco, Splunk, Palo Alto Networks, and Hytrust.

During their internships, NCCoE’s Graduate Cybersecurity Researchers may assist NCCoE staff and contractors in areas such as:

the design and building of cybersecurity reference designs to demonstrate platform capabilities that address one or more challenges identified by industry.

mentoring undergraduate cybersecurity researchers and helping build teams to work on research projects.

working with NCCoE industry partners and collaborators to identify relevant commercially available technologies that can serve as a component of these reference designs.

supporting the NCCoE lab infrastructure, including the provisioning of hardware, creation and management of both virtual and physical equipment, and the installation and configuration of cybersecurity tools and components.

These NCCoE internships are open to UMBC cybersecurity students enrolled at the USG campus who are US citizens. The deadline for Summer 2014 consideration is Monday, May 5, however internships are available during the 2014-15 academic year as well.

For more information and/or to apply, please contact the USG Career & Internship Services Center at 301-738-6338 or Sorry, you need javascript to view this email address.

ACTIVE Center Open House

4:00-5:00pm Monday, 28 April 2014 in ENGR 231

The ACTIVE Center (Engineering 231) is a new classroom that was created by the CSEE Department with support from the Hrabowski Fund for Innovation, BAE Systems, and Northrup Grumman. The ACTIVE Center is designed to facilitate active student learning and laptop-based laboratory activities, and features movable furniture and whiteboards, a smart projector, and flat-panel displays around the room. We are also developing and documenting a “virtual environment” for the classroom, by creating “design patterns” for how computing technology can be used in this type of space to facilitate student learning.

The classroom came online in February 2014, and four pilot courses are currently being offered in the space. On Monday, April 28, we will hold an open house and presentation in the ACTIVE Center to show the campus community how an intentionally designed classroom space can increase student engagement and improve learning outcomes. The open house will include a presentation by Dr. Marie desJardins, a Hrabowski Academic Innovation Fellow and lead PI for the ACTIVE Center project, and a Q&A session with instructors and students who are currently teaching and learning in the ACTIVE Center. We will share best practices for developing and using similar teaching spaces, and will present the current policy for requesting to use the space for future classes.

Please note that no food or drink (other than covered containers of water) are permitted in the ACTIVE Center, but light refreshments will be provided in the hallway following the presentation.

Capacity is limited, so please RSVP. For more information, contact Marie desJardins (Sorry, you need javascript to view this email address. ).

Dissertation Defense

Feature Extraction and Fusion for Supervised and Semi-supervised
Classification: Application to fMRI and LTM Data

Wei Du

2:00pm Thursday, 24 April 2014, ITE 325B

Extracting powerful features from high dimensional noisy data promises to significantly improve the effectiveness of further analysis, especially of classification. Since there is no single feature selection and extraction method or classifier that works best on all given problems, developing effective and efficient feature selection and extraction methods and classifiers for specific applications has became one of the most active areas in the machine learning field. The aim of this dissertation is to develop novel data-driven methods for extracting and selecting the most distinguishing features for performing classification using functional magnetic resonance imaging (fMRI) and laser tread mapping (LTM) tire data.

FMRI data have the potential to characterize and classify various brain disorders including schizophrenia. However, the high dimensionality and unknown nature of fMRI data present numerous challenges to accurate analysis and interpretation. Independent component analysis (ICA), as a data-driven method, has proven very useful for fMRI analysis in extracting spatial components as multivariate features used in classification, and more recently, for the analysis of fMRI data in its native complex-valued form. In this dissertation, we first present a novel framework to extract powerful features from components estimated by ICA, allowing us to remove the redundancy and retain the most discriminative activation patterns from multivariate ICA features. We apply the proposed three-phase feature extraction framework to two real-valued fMRI data sets, and achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Second, due to the iterative nature of ICA algorithms, typically independent components (ICs) are not estimated consistently during different ICA runs, and hence it is not clear which result to use further. We present a statistical framework that utilizes an objective criterion to select the best of multiple ICA runs such that the multivariate ICA features from the best run can be used for further analysis and inference. Using the proposed framework, we study the performance of a novel complex ICA algorithm for fMRI analysis, entropy rate bound minimization, which takes all three types of diversity into account, including non-Gaussianity, sample dependence and noncircularity that are present in the complex-valued fMRI data. We show that CERBM leads to significant improvement in ICs that provide higher classification accuracy, and thus is a promising ICA algorithm for the analysis of complex-valued fMRI data.

Classification using LTM data is another problem we address where we first study the use of highly multivariate solutions such as ICA and then note the advantages using lower-level features for classification. In this case, an important problem is the selection of best set of features for the best classification performance. Additionally, there are a large amount of unlabeled tire data that are easy to collect but only a few of them can be easily labeled by expert. In this dissertation, we propose a novel mutual information (MI) based approach to achieve feature splits for co-training, a practical and powerful data-driven method in semi-supervised learning. Inspired by the idea of dependent component analysis, the proposed MI-based approach presents feature splits that are maximally independent between- or within- subsets, and thus selects and fuses features more effectively than other feature split methods. Experimental results on both simulated study and LTM tire data indicate that co-training with MI-based feature splits yields significantly higher accuracy than supervised classification.

Committee: Profs. Tulay Adali (Chair), Joel Morris, Janet Rutledge, Charles E. Laberge, Vince D. Calhoun (University of New Mexico and the Mind Research Network), and Dr. Matthew Anderson (Northrop Grumman Corp.)

Self-Assessments

What career is best for you?

Are you struggling with picking or major, undecided, or not sure of what you want to do with your major

If you answered yes to these questions, then check out how the Career Services Center can help.

Dependable Strengths is a two-day workshop to help you assess your strengths and identify what you enjoy, what you do well, and events or experiences you are proud of. It motivates you to succeed. By learning more about yourself, you will discover new opportunities. There will be guided group discussions.

Register now for the summer Dependable Strengths Workshop (Friday and Saturday, June 6-7, 2014)                 http://www.careers.umbc.edu/skills/ds.php

Focus 2 is an Internet based self-assessment tool designed to help you explore career options best for you based on you interests, work values, personality, skills, and educational and leisure time preferences. After you take Focus, please schedule an appointment with a career specialist. The specialist will help you clarify your career path and get you headed in the right direction.

http://www.careers.umbc.edu/skills/selfexp.php#focus

Compass Woofound is our newest self-assessment. This is a visual personality assessment in which you select if a picture is you or not you. This assessment provides information about your personality, how you interact with other personalities, career fields best for you, and what majors and classes would be best for you.  

https://umbc.woofound.com/

The Hatcher Group - Baltimore, Maryland

POSITION TYPE: Internship Opportunities

POSTING DATE: April 21, 2014

Maxim Healthcare Services - Columbia, Maryland

POSITION TYPE: Internship Opportunities

POSTING DATE: April 16, 2014

Maxim Healthcare Services - Columbia, Maryland

POSITION TYPE: Internship Opportunities

POSTING DATE: April 14, 2014

OmniTi Computer Consulting - Fulton, Maryland

POSITION TYPE: Internship Opportunities

POSTING DATE: April 14, 2014