Learn about graduate programs in CSEE

UMBC's Computer Science and Electrical Engineering (CSEE) Department has research active graduate progams in Computer Engineering, Electrical Engineering and Computer Science. Currently, our department has over 250 graduates students who hail from all parts of the globe. To learn more about our graduate programs, take a look at our new graduate program brochures:

Computer Engineering Graduate Program Brochure

Electrical Engineering Graduate Program Brochure

Computer Science Graduate Program Brochure

Henry Sienkiewicz on Cloud Computing in the Government


UMBC CSEE Colloquium

Cloud Computing

Henry J. Sienkiewicz

Chief Information Officer
Defense Information Systems Agency

11:30-12:30 Friday, 16 September 2011
Room 231, ITE Building

Mr. Henry Sienkiewicz will discuss the opportunities and challenges for using cloud computing in government agencies.

Henry J. Sienkiewicz is the Chief Information Officer for the Defense Information Systems Agency. As the DISA CIO he is responsible for developing, maintaining, and facilitating the implementation of the Agency's information technology (IT) architecture, enabling DISA to accomplish its critical combat support missions. As CIO, he ensures that agency IT and information assurance programs and policies are fully coordinated, integrated, and effectively implemented and are aligned with the Agency's strategy. Mr. Sienkiewicz joined DISA in 2008 as the Technical Program Director for DISA Computing Services before moving to the CIO position. He is a founding member of George Washington University's technology transfer council, retired from the US Army Reserves, and has been involved in many academic and entrepreneurial pursuits throughout his extensive IT career.

Hosts: Professor Yelena Yesha and Joel Morris

directionsupcoming talks

Undergraduate Researcher Profile: Ugonna Ohiri






Ugonna Ohiri is a first year Senior majoring in Computer Engineering. A URA scholar, Ugonna's research deals with standoff chemical detection. To learn more about Ugonna's research pursuits, read his research profile.  

UMBC named NVIDIA CUDA Teaching Center

UMBC has been named an NVIDIA CUDA Teaching Center following the submission of a proposal by Dr. Marc Olano, professor, and Dr. Shujia Zhou, research associate professor of the Computer Science and Electrical Engineering Department. The NVIDIA CUDA Teaching Center Program will provide UMBC with enough high-end GPUs to upgrade the UMBC GAIM (Games, Animation and Interactive Media) Lab, as well as a Tesla GPU-based computing processor.

Dr. Olano was familiar with NVIDIA’s grant programs through previous equipment grants, and last February, he spoke with David Luebke, Director of Research at NVIDIA, about the CUDA Teaching Center Program. His decision to submit a proposal weighed heavily upon the increasing interest in GPU computing around the UMBC community.

“It’s an important skill for game programming,” says Dr. Olano, who is the director of the Computer Science program’s Game Development Track. He adds that UMBC’s Multicore Computational Center (MC2) and High Performance Computing Facility (HPCF) are also moving toward nodes with GPU computing capability and could benefit from the upgrade.  

UMBC is now one of thirty-six NVIDIA CUDA Teaching Center within the U.S., joining schools such as Florida A&M University, Hood College, Purdue University and UCLA. Apart from the generous equipment donation, UMBC’s distinction as a NVIDIA CUDA Teaching Center provides the university with recognition on NVIDIA’s website, access to teaching materials, and the opportunity to receive discounts on some NVIDIA equipment purchases.

Dr. Olano predicts that the newly-enhanced GAIM lab will be usable by the beginning of the Spring semester. The new equipment will enhance game development and parallel programming classes in upcoming semesters, such as CMSC 483: Parallel and Distributed Processing, which will be taught by Dr. Shujia Zhou in the upgraded lab this Spring. 

A letter from CSEE Department chair, Dr. Gary Carter

The Computer Science and Electrical Engineering department made significant accomplishments during the 2010-2011 school year. Our undergraduate enrollment grew to 886 students and our graduate enrollment grew to 271 students. Our department produced a total of 16 Ph.D’s during that time period. Our research productivity in terms of research expenditures has reached the level of our departmental budget at nearly $6 Million. In addition, Professor Hillol Kargupta was elevated to the level of IEEE fellow, and Professor Marie desJardins was promoted to full Professor.

Last year also marked a number of changes within the department. I became the Chair in October of last year after Professor Anupam Joshi ably served the department as interim chair, following Professor Charles Nicholas stepping down from the position.

We have also gained three new Computer Engineering faculty members: Assistant Professors Tinoosh Mohsenin, Chintan Patel, and Gymama Slaughter. Most recently, Shawn Lupoli joined the department as a lecturer in Computer Science.

Last year was also a time of transition. We had a number of faculty members retire: Sue Evans, senior lecturer in Computer Science, Professor John Pinkston, and Professor Zary Segall. We will miss their contributions to the department. In order to compensate for these losses, I am pleased to report that we have been authorized to search for a new Computer Science Assistant Professor.

Please look at our new website which contains a wealth of information for students, faculty, staff, visitors, and prospective students. You can visit it at www.csee.umbc.edu


UMBC students present research at the Mid-Atlantic Student Colloquium on Speech, Language and Learning

Six CSEE graduate students will present their research First Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one-day event to be held at the Johns Hopkins University in Baltimore on Friday, 23 September 2011. Its goal is to bring together students taking computational approaches to speech, language, and learning, so that they can introduce their research to the local student community, give and receive feedback, and engage each other in collaborative discussion. The students and the titles of their presentations are:

  • Niyati Chhaya, Joint Inference for Extracting Text Descriptors from Triage Images of Mass Disaster
  • Lushan Han, GoRelations: An Intuitive Query System for DBpedia
  • Niels Kasch, Concept Modeling for Scripts
  • Justin Martineau, DIVA: Domain Independence Verification Algorithm for Sentiment Analysis
  • Varish Mulwad, Automatically Generating Linked Data from Tables
  • Jennifer Sleeman, A Streaming Approach to Linking FOAF Instances
  • Xianshu Zhu, Finding Story Chains in Newswire Articles

Attendance is open to all and free but space is limited, so online registration is requested by September 16. The program runs from 10:00am to 5:00pm and will include oral presentations, poster sessions, and breakout sessions.

POSTPONED: talk: Nonlinear Optical Signal Processing in Optical Fibers and Waveguides

CSEE Graduate Seminar

Nonlinear Optical Signal Processing in
Optical Fibers and Waveguides

Dr. Gary M. Carter
Professor of Electrical Engineering
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

1-2pm Friday, 16 September, 2011, ITE 227

postponed until later in the Fall

Advances in optical fiber and semiconductor technology have progressed to the degree that nonlinear optical signal processing can be demonstrated at extraordinarily high data rates. This talk will review some of the work of Dr. Carter's research group in photonic crystal fibers, silicon nano wires, and AlGaAs optical waveguides.

Hosts: Profs. Joel M. Morris and Yelena Yesha

Upcoming CSEE talks

Talk: Genetic information for chronic disease prediction

Genetic information for chronic disease prediction

Michael A. Grasso, MD, PhD
University of Maryland School of Medicine

1:00pm Friday 23 September 2011, 227 ITE

Type 2 diabetes and coronary artery disease are commonly occurring polygenic-multifactorial diseases, which are responsible for significant morbidity and mortality. The identification of people at risk for these conditions has historically been based on clinical factors alone. However, this resulted in prediction algorithms that are linked to symptomatic states, which have limited accuracy in asymptomatic individuals. Advances in genetics have raised the hope that genetic testing may aid in disease prediction, treatment, and prevention. Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We present an overview of genetic issues involved in polygenic-multifactorial diseases, and summarize ongoing efforts use this information for disease prediction.

Michael Grasso is an Assistant Professor of Internal Medicine and Emergency Medicine at the University of Maryland School of Medicine, and an Assistant Research Professor of Computer Science at the University of Maryland Baltimore County. He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, and the William Beaumont Medical Research Honor Society. He completed a residency at the University of Maryland School of Medicine, and currently works in the Department of Emergency Medicine at the University of Maryland Medical Center. He has been awarded more than $1,200,000 in grant funding from the National Institutes of Health, the National Bureau of Standards and Technology, and the Department of Defense, and has authored more than 35 scholarly papers and abstracts. His research interests include clinical decision support systems, clinical data mining, clinical image processing, personalized medicine, software engineering, database engineering, and human factors. He is also a semi-professional trumpet player and is interested in the specific medical needs of performing artists, especially instrumental musicians.

Host: Yelena Yesha


Camilla Hyman joins CSEE staff

Ms. Camilla Hyman recently joined the Computer Science and Electrical Engineering Staff as the Business Services Specialist for the department. Ms. Hyman has replaced Ms. Donna Meyers, who retired at the end of Spring 2011.

This summer, Camilla Hyman joined the Computer Science and Electrical Engineering Department as Business Services Specialist–a role that had been left vacant by Ms. Donna Meyers, who retired at the end of Spring 2011. Ms. Hyman, who has lived in Maryland her entire life, grew up just down the road from UMBC in Catonsville, MD and now lives in Baltimore City. Apart from being an expert at all things payroll, Ms. Hyman has served as a Girl Scout leader for the past seventeen years.

Before coming to UMBC, Ms. Hyman spent nine years working for the Goddard Earth Sciences and Technology (GEST) and Joint Center for Earth Sciences (JCET) departments at UMBC. Prior to that, she worked for the Division of Behavioral and Developmental Pediatrics and Adolescent Medicine departments at the University of Maryland, Baltimore (UMB). With less than a month of CSEE office life under her belt, Ms. Hyman already says that she finds the position enjoyable. The best part about her job, she says, is learning about different cultures from the Graduate Students that she interacts with every day. 

talk: Analysis of Brain Network Connectivity in fMRI Data using Spatial Dependence

EE Graduate Seminar

Analysis of Brain Network Connectivity
in fMRI Data using Spatial Dependence

Sai Ma
EE PhD Candidate, CSEE Dept, UMBC

11:30-12:45 Friday 9 September 2011, ITE 231

Due to low invasiveness and high spatial resolution, functional magnetic resonance imaging (fMRI) has become popular in neuroimaging field to determine where activity occurs in brain as a result of performing cognitive tasks or merely being at rest.  One of the most active areas in current fMRI research involves exploring functional connectivity, i.e., statistical interactions, among distributed neural units. Understanding connectivity elucidates how functional systems process information in brain. More interestingly, disorganized connectivity has shown to be related to various kinds of mental disorder.

Data-driven methods, especially independent component analysis (ICA), have been successfully applied to fMRI data analysis and provided an opportunity to study brain functional connectivity on a network, hence multivariate scale. However, independence is a strong assumption which is not necessarily nor typically satisfied in real applications. For this reason, dependent component analysis (DCA) has emerged to generalize ICA by grouping components into independent subsets while within subset dependence is allowed.

Based on ICA and motivated by DCA, we aim to develop effective and efficient analysis schemes to extract, characterize, and quantify network connectivity pattern in fMRI data. We define functional network connectivity as spatial dependence among ICA-derived components, instead of second-order temporal correlation between time courses, to capture high-order statistics. According to this definition, we present our work on the study of network connectivity by several data-driven methods, including ICA, DCA, hierarchical clustering, hypothesis testing, and graph theoretical analysis.

seminar Host: Prof. Joel M. Morris

1 120 121 122 123 124 138