Congratulations to our Computer Science and Electrical Engineering undergraduate students who have recently been selected as Undergraduate Research Award (URA)Scholars. The URA provides undergraduates with up to $1,500 to fund their original research projects. The work is done in conjunction with a faculty mentor, and is then presented at UMBC's  Undergraduate Research and Creative Achievement Day (URCAD) in April. This year, these students and their faculty advisors will be recognized at URCAD on Wednesday, April 25 at noon in the University Center, Room 312.

David Eisen
Computer Engineering, Senior
Faculty Advisor: Fow-Sen Choa

David’s research focuses on the study of cultured neuron inhibition and excitation via optical stimuli from mid-IR (MIR) quantum cascade lasers (QCL).

“It was very exciting news to hear my URA proposal was accepted. This is my first research experience and proposal, so I believe this award will give me the confidence to pursue other opportunities in the future. The URA will give me a hands-on learning experience in the field of photonics research by working alongside Professor Choa as well as graduate students. Not only does this look good on a resume, but it gives me a chance to really get a taste for what real world challenges and rewards await after graduation.”

Julian Field
Computer Engineering, Junior
Faculty Advisor: Tinoosh Mohsenin

Julian will be doing research in UMBC's Energy Efficient and High Performance Computing Lab. His task will be the development and mapping of a many-core processor for the purpose of performing ultrasound imaging applications. Many cores on the processor will improve performance and reduce the running time of the processes. The processor will be designed using the Verilog hardware description language (HDL) and will be implemented by programming the design into a Field-Programmable Gate Array (FPGA). Using an FPGA not only reduces the cost of the design, but it also speeds up implementation.

"I felt very honored to receive the award, and I believe that it is going to help me in several ways. It allows me to get paid for doing research in a field that fascinates me and in which I hope to work in the future. Besides the obvious financial benefit, the award adds weight to my research when I list it on my resume, and this should be of great value when I apply to graduate school and for Computer Engineering jobs and internships in the future."

Thomas A Hervey
Information Systems/ Geography, Sophomore
Aneep Bindra
Computer Science, Sophomore
Zachary Hullihen
Computer Science, Junior
Faculty Advisor: Marc Olano 

"Lights, Camera, Motion, Action: The Dance Application of Microsoft's Kinect and Intelligent Stage Lighting."

"Receiving this award has given me a stronger incentive to work my hardest and apply myself beyond [the] academic realm. This award will be beneficial because it will provide necessary funds for unusual but necessary research tools. I look forward to strengthening my presentation skills at URCAD next year as well as refining my organizational skills through planned documentation and procedures during the actual experimentation. The award overall certainly creates a sense of confidence within the research community. I find it likely that students that are not active in research will hear about the award and benefits through their friends' experiences, and current URA scholars will continue research through new projects." -Thomas A. Hervey

Alex Morrow
Computer Science, Sophomore
Faculty Advisor: Marie desJardins

"Quantitative Evaluation of Dimension Reduction for Visualization and Analysis."

Alex’s research branches off of his current research on visualization and analysis techniques to facilitate user understanding of the uncertainty associated with statistical models that have been learned from data. Alex will analyze and evaluate dimension reduction methods. Using a set of quantitative metrics compiled by his research advisors Dr. Marie desJardins and Dr. Penny Rheingans, Alex will compare the performance of several dimension reduction techniques in a number of different application domains.

Adam Page
Computer Engineering / Math
Faculty Advisor: Dr. Tinoosh Mohsenin

Adam’s research involves designing an energy efficient and high performance error correction Low Density Parity Check Decoder circuit for IEEE 802.11ad standard, the next generation of Wireless Local Arena Network (WLAN). The design will include an efficient architecture for the decoder using Verilog coding and VLSI implementation.

“I am both honored and excited to receive this award. I know the URA will give me the opportunity to apply knowledge I have gained from all of my courses into a real world application.”

Don’t forget to meet our URA scholars at URCAD next Wednesday, April 25 at noon in the University Center, Room 312.

Masters Thesis Defense

Heart Disease Prediction: A Data Mining Approach

Soma Das

2:00pm Monday, 23 April 2012, ITE 201B

Data mining is a field of computer science that combines statistical analysis and machine learning to detect hard-to-discern patterns from large amounts of data. It employs different algorithms to learn different patterns from training or experience and apply it to classify, predict or identify patterns. The healthcare environment is very information rich. There is a wealth of clinical data available within the healthcare systems. Also due to recent advancement of genomic research vast amount of genetic data are also available. Effective analysis tools are needed to discover hidden relationships and trends in these data. These tools are necessary to correctly diagnose people at risk of disease based on the derived knowledge from the data.

We used data mining techniques to evaluate the interaction between traditional risk factors and gene variants such as Single Nucleotide Polymorphisms (SNPs) towards Coronary Heart Disease (CHD) susceptibility in a prospective study of older population aged 65 and older. In our thesis we asked two questions whether we can predict CHD at birth or adding genetic information to traditional risk factors predict CHD better than traditional risk factors alone.We also analyzed two popular machine learning algorithms to determine the most efficient method on medical datasets mining. The evaluation is based on a set of performance metrics. We also applied a clustering method to identify different subgroups present in the selected datasets.

We chose eight traditional risk factors of CHD and 23 SNPs that had previously been reported to be associated with CHD. We then tested the association of these SNPs with CHD in cardiovascular Health Study (CHS). Based on previous studies, we pre specified a risk allele for each of 23 SNPs. We assigned coding values for homozygote, heterozygote, and the no risk homozygote SNPs and then combined these with traditional risk factors for each individual before feeding it to machine learning algorithms. We evaluated different classification algorithms using 10 fold cross validation test.

Receiver Operating Characteristic Curves (ROC) were plotted separately based on traditional risk factors alone and traditional risk factors plus SNPs. The increase in the Area Under Curve (AUC) was statistically significant for Whites and suggestive of improved CHD prediction for African American. We also found out that using only SNPs predicts CHD a little bit better than random guessing for only whites. The results gained from analysis suggest Naïve Bayes to be the best classifier for the given domain.

This study demonstrates the concept of using multiple SNPs as independent risk factors and indicates that it can improve prediction of incident CHD. Adding SNPs to traditional risk factors did not improve the prediction model dramatically as we expected but it was statistically significant.

Committee:

• Dr. Michael Grasso (co-chair)
• Dr. Anupam Joshi
• Dr. Yelena Yesha

EE Graduate Seminar

From Proton to Image: A Signal Processing Aspect of MRI

Albert Kir
PhD (EE) Student, CSEE Dept/UMBC

11:30am-12:45pm Friday, 20 April 2012, ITE 237

Magnetic Resonance Imaging (MRI) is routinely used in clinical setting for its great diagnosis and prognosis ability, and is a heavily studied research area across multiple disciplines. MRI has its tie with signal and imaging processing community since it stemmed from the study of nuclear magnetic resonance (NMR). The technique of Fourier imaging makes MRI possible through manipulation of the NMR signals. The issue of imaging speed has always been at the heart of functional MRI (fMRI) and interventional imaging, where a high image frame rate is required or preferable. In the past decade, partly owing to the advance in imaging hardware, a wide range of image reconstruction algorithms have been developed to accelerate the image acquisition process. There has been SENSE, SMASH, GRAPPA, and many of their variations in the parallel imaging category from the early days to the current K-T techniques based on compressive sensing (CS). In this talk, the basic imaging principle for MRI will first be presented, and then a discussion of the first parallel imaging technique, SENSE, will be given. Lastly, the use of K-T FOCUSS on fMRI will be demonstrated.

Albert Kir received the BS degree in Computer Engineering n 2005 and the MSEE degree in 2009 from UMBC. He is currently a PhD (EE) student at UMBC. Mr. Kirs current research interest includes optimization of rapid imaging techniques for structural and functional images for MRI.

Host: Prof. Joel M. Morris

• BNC Solutions LLC -- Administrative Assistant -- i.d. 9250886
• FiberPlex Technologies, LLC -- Administrative Support -- i.d. 9250881
• Pine Forest Camps -- Camp Counselors -- i.d. 9250925
• ATC Financial -- Client Coordinator (Temp to Perm) -- i.d. 9250851
• The American Physical Society -- File Clerk -- i.d. 9250808
• Baltimore City Community College -- Residential Coordinators -- i.d. 9250848
• Ripley's Believe It or Not! -- Sales Associate -- i.d. 9250878

• UMBC Office of Student Judicial Programs -- Graduate Student Assistant -- i.d. 9250916

Photo courtesy www.retrieverweekly.com

Left: Computer Engineering Alumnus Delali Dzirasa speaks with a student during the Raymond V. Haysbert, Sr. Entrepreneurship Lecture back in March.

The Retriever Weekly talked to UMBC alumnus Delali Dzirasa (CE, '04) about what it takes to be a successful entrepreneur. Dzirasa, the founder of Fearless Solutions, LLC–a team of software engineers and web developers that help clients solve problems through Data Visualization, Web Application Development, Software Engineering, and Rapid Prototyping–knows from experience. Here's what he had to say:

" Dzirasa of Fearless Solutions told stories of how he used to run a barbershop when he lived on the UMBC campus to make money.

"I am a serial starter of things," he said. "My advice for you is to believe in yourself… Never give up and hold onto your vision." "

Check out the full article HERE.