Department of Computer Science and Electrical Engineering

The Computer Science and Electrical Engineering Department wishes to extend its congratulations to Dr. Tim Oates for his promotion from associate professor to full professor.

In 2001, after receiving his Ph.D. in Computer Science from the University of Massachusetts Amherst, Dr. Oates began teaching at UMBC. His course repertoire includes Introduction to Machine Learning, Discrete Structures, Data Structures, and the ever-popular Robotics.

As the director of UMBC’s Cognition, Robotics, and Learning (CoRal) Lab, his research centers on machine learning. The vision of the lab is to “understand how artificial systems can acquire grounded knowledge from sensori-motor interaction with their environment that enables cognitive activities like natural language communication and planning,” says the lab’s website. More about his research interests can be found in his research profile.  

In addition to his academic work, Dr. Oates contributed to the department last year as chair of the ABET Assessment committee. He is also the advisor for UMBC’s student chapter of the Association for Computing Machinery (ACM), the world’s largest educational and scientific computing society.

The Sixth UMBC Digital Entertainment Conference (DEC) will be held this Saturday, April 28 from 10:00am to 4:00pm in LH1 in the Biological Sciences building.

Every year since 2007 the students ofn the UMBC Game Developer's Club has organized the conference and invited speakers from the videogames industry to come in and discuss important topics in the games industry. DEC 2012 is sponsored by Zynga, the studio that developed Farmville and many other Facebook games.  One of the strenghts of the UMBC program in Graphics, Animation and Interactive Media (GAIM) is its strong ties to game development studios in the Maryland, DC and Northern Virginia area.

The 2012 DEC is open to anyone, and features an all-star lineup of speakers from Firaxis Games, Zynga East, Pure Bang, and Mythic Entertainment. Whether you are a high school student, go to UMBC or another university, or are already working in a different industry, you are sure find interesting information about how the games industry works, how some current developers got started, and what they do. If you are a game developer, you are sure to find high school students, UMBC students and students from other universities who are interested in jobs in the games industry.

Here is the schedule.

10:00am – Barry Caudill, Director of Gameplay Development at Firaxis
11:00am – Tim Train, Studio Manager at Zynga East
12:00pm – Lunch Break
1:00pm – Eric Jordan, Programmer at Firaxis
2:00pm – Ben Walsh, CEO of Pure Bang Games
3:00pm – Brian Johnson, Director of Online Operations at Mythic Entertainment

photo courtesy www.umbc.edu/gsa

More than two-dozen Computer Science and Electrical Engineering graduate students are slated to present at the Graduate Student Association's annual Graduate Research Conference (GRC). The GRC will be held this Friday, April 27 and features a keynote presentation from Dr. Michael I. Nishimura, a UMBC alumnus who now works at Loyola University Chicago as a professor in the Department of Surgery, the Associate Director of the Oncology institute, the Associate Director of the Cancer Center Translations Research, and as Program Director of Immunologic Therapeutics.

This year, the CSEE presence at the GRC is even stronger than it was last year, when three CSEE students were awarded for outstanding presentations. The honorees were Varish Mulwad who received an award for an outstanding oral presentation on his dissertation research entitled "Generating Knowledge from Tables," Kavita Krishnaswamy who received an award for an outstanding oral presentation on her thesis research entitled "Path planning a roboticarm efficiently," and Akshya Iyengar who received an award for an outstanding poster presentation for her thesis research entitled "Estimating Temporal Boundaries of Events using Social Media Data".

Take a look at the list below, which includes the 27 CSEE graduate students who will present at the GRC this year.

Check out some photos from last year's conference.

Click here for a printable list of CSEE presenters.

Oral Presentations
Location: Sondheim 203

“Noise Reduction in AIRS Infared Earth Observing Radiance Grids using the Obscov Algorithm”
David Chapman, Milton Halem, Phuong Nguyen, and Jeff Avery
Time: 9:00 a.m.

“Heart Disease Prediction Model: A Data Mining Approach”
Soma Das
Time: 9:15 a.m.

“Measuring the Pulse Duration of Ultrafast Lasers”
Jared Dixon
Time: 9:30 a.m.

“Using Deceptive Packets to Increase Base-Station Anonymity in Wireless Sensor Network”
Yousef Ebrahimi and Mohamed Younis
Time: 9:45 a.m.

“SmartRate: A Rating Interpretation Mechanism for Agents in Smart Grid Markets”
Yasaman Haghpanah
Time: 10:00 a.m.

“Visualizing Changes to Directory Structure to Support Digital Forensics”
Timothy Leschke
Time: 10:15 a.m.

“Extracting Semantic Linked Data from Tables”
Varish Mulwad
Time: 10:30 a.m.

“Automatic Identification of Prescription Drugs Using Shape, Imprint, and Color”
Adrian Rosebrock
Time: 11:00 a.m.

“An Optical Sensor for a Ceramic Water Filtration System for the Detection of E.Coli Using a Microfluidic Chip”
Serina Woods
Time: 11:15 a.m.

Poster Presentations
Location: Albin O. Kuhn Library: 7^th Floor

“Witness-based Saboteur Detection in Multi-agent Systems”
Petr Babkin
Time: 11:00 a.m.

“Entity Linking and Disambiguation for Smartphone Platforms”
Anurag Korde
Time: 11:00 a.m.

“Third-order Quadratically Converging, Quasi-Newton Optimization”
Rory Mulvaney
Time: 11:00 a.m.

“Supervised Learning Techniques for Predicting Risk of Breast Cancer using Genetic Information”
Aniket Bochare
Time: 11:12 a.m.

“Prostate Cancer Prognosis using Genomic Data”
Rohit Kugaonkar
Time: 11:12 a.m.

“Using Supervised Techniques for Classification of Conventional Data Items”
Nikhil Puranik
Time: 11:12 a.m.

“Surface Emitting Quantum Cascade Laser Array”
Xing Chen, Liwei Cheng, Dingkai Guo, and Fow-Sen Choa
Time: 11:24 a.m.

“Learning Sensitive to Multiple Sources of Costs”
Zachary Kurtz
Time: 11:24 a.m.

“Exploring Hidden Markov Model for Semantic Activity Prediction”
Amey Sane
Time: 11: 24 a.m.

“Calculating Representatives of Geographic Sites across the World”
Ashwinkumar Ganesan
Time: 11:36 a.m.

“Older Adults Interactions with a Touch Table Top Display Space”
Galina Madjaroff
Time: 11:36 a.m.

“Rendering of Smoke and Fire in a 3D Volume with Multiple Scattering”
Taekyu Shin
Time: 11:36 a.m.

“Chronic Disease Prediction: An Experimental Analysis Using the K-nearest Neighbor Algorithm”
Matthew Gately
Time: 11:48 a.m.

“Situation Aware Intrusion Detection Model”
Sumit S. More
Time: 11:48 a.m.

“Unsupervised Coreference Resolution for FOAF Instances”
Jennifer Sleeman
Time: 11:48 a.m.

“Towards an Intuitive Query System for DBpedia”
Lushan Han
Time: 12:00 p.m.

“Link Prediction Using Frequent Subgraphs”
Maksym Morawski
Time: 12:00 p.m.

“Modeling Motheye Antireflective Structures for Increased Coupling through As2S3 Optical Fibers”
Robert J. Weiblen
Time: 12:00 p.m.

MS Thesis Defense

Distributed Model Consensus for Models of Locally
Biased Measurements in Wireless Sensor Networks

Jacob Thompson

4:00pm Wednesday, 25 April 2012, ITE 325B, UMBC

Wireless sensor networks (WSNs) consist of interconnected microsensors, each of which collects measurements from its local environment, which are often used in monitoring and control applications. These applications make inferences about the global and local states of the deployment environment. However, due to the limited communication and energy resources at the sensors, gathering all the raw data at a central fusion/control point is impractical.

Hence, it is essential to have distributed learning and inference in WSNs, such as learning a consensus model from the locally learned models. Consensus is challenging due to the limited resources of the sensors and the inherent bias of the individual sensor models learned from their local sensing environments. Two leading approaches for this problem are the approach by Zheng et al which uses loopy belief propagation on a certain graphical model based on the WSN topology and the local models, and the approach by Xiao et al which relies on gossip averaging of the parameters of the local models.

We focus on multivariate linear regression models, such as Bayesian, Ridge, and Lasso regression models. We analyze and extend the loopy Gaussian belief propagation (GaBP) approach to model consensus, and compare its performance to the gossip averaging approach. We experimentally find that GaBP tends to converge much faster than gossip averaging, but to a less accurate estimated consensus model (especially in the presence of multiple cycles in its corresponding graphical model). We also find that gossip averaging along paths in the WSN, tends to provide much faster convergence to more accurate estimated consensus models as compared to GaBP.

Committee: Professors Kostas Kalpakis (chair), Tim Oates and Yun Peng

Analyzing Social Media Data
Morgan Madeira
Junior, Computer Science

Social media has increasingly become an outlet for expression for a large part of our society. Literature suggests that analyzing data from these sites can lead to improvements in areas such as health-care and search-ad targeting. Users of these sites often associate with many other users described as “friends,” even if they do not have a strong connection, or what would be described as friendship in daily life. It is valuable to determine the strength of relationships between users and to identify communities within social networks. These communities represent people with similar characteristics, which are used by applications to solve many real-world problems. For instance, it is useful to identify groups that are interested in a specific movie genre. Information about these groups can be used to target movie advertisements towards the people most interested in that genre. These types of problems have similar characteristics to identifying close friends. We have created a system to collect and analyze the data about user characteristics, while being respectful of privacy concerns. The system is composed of a front end Facebook application and a back end machine-learning based tool. The front end component gathers data about a user and their friends. The back end uses the collected data and machine-learning techniques to determine relationships between users.

To learn more about the project, check out an interview with Morgan.

Catch Morgan's poster presentation at URCAD this Wednesday, April 25 in the University Center Ballroom from 10:00 a.m. to 12:30 p.m.

Multiclass Datasets, Their Predictions, and Their Visualization
Wallace Brown and Alexander Morrow with Kevin Winner
Senior, Computer Science
Sophomore, Computer Science

Many datasets contain a wealth of information. For example, a person may be described by their race, age, gender, income, marital status, nationality, level of education, etc. By analyzing this data, we can form educated and accurate predictions about individuals. We can, for instance, determine that a person with a particular race, age, nationality, and income is likely to be a college undergraduate. Our goal is to develop ways to visualize these predictions and the uncertainty associated with the predictions. Displaying data in a scatterplot is a standard means of describing two-dimensional information. However, displaying high-dimensional data (i.e., data that includes many attributes, such as age, race, and income) is significantly more challenging.  We present a means of visualizing high-dimensional data sets and the predictive models derived from the data, using existing dimension reduction techniques and novel glyph-based displays.

To learn more about the project, check out an interview with Wallace and Alexander.

Catch Wallace and Alexander's presentation at URCAD this Wednesday, April 25 in the University Center 312 from 1:15 to 1:30 p.m.

MS Thesis Defense

Towards Implementation of a Pattern Recognition System based on
a Working Theory of the Learning Rule for Dendritic Integration

Shamit Patel

4:00pm Monday 23 April 2012, ITE 346, UMBC

My goal is to develop a working theory of the learning rule for dendritic integration, and to then implement a pattern recognition system based on that learning algorithm so that the algorithm can be evaluated for its generalization ability. In this regard, this thesis presents an implementation of Jeff Hawkins and Dileep George's Hierarchical Temporal Memory (HTM) pattern recognition system that's based on an existing theory of the learning rule for dendritic integration – spike-timing-dependent synaptic plasticity (STDP). The integration of this learning rule is the novel contribution of this thesis. I found that the STDP HTM system achieved much higher probabilistic classification accuracy and better generalization ability than the non-STDP HTM system. Probabilistic classification accuracy is a way of measuring classification accuracy in which a testing pattern is classified correctly if its label appears in the group of labels output by the top-level node of the HTM network.

Committee: Professors Tim Oates (Chair), Yun Peng and Tim Finin

Ph.D. Dissertation Defense

DiffServ Assured Forwarded and Robust
Header Compression: Performance Analysis

Houcheng Lee

12:30pm Monday, 23 April 2012, ITE 201b

Performance analysis of network architecture and protocols can be done by using modeling and simulation or using emulation techniques. Modeling and simulation can be used to obtain qualitative results about a network protocol through building models of protocols under drastic abstractions. One major concern of the modeling approach is the fidelity of the results from a model simulation.

This dissertation presents detailed performance analyses of two major Internet standards using emulation techniques. The standards investigated are Differentiated Services (DiffServ) architecture and Robust Compression (ROHC) standard.

The DiffServ architecture was proposed to support quality of services (QoS) implementation in TCP/IP networks. In the first part of dissertation, a new and scalable approach to performance analysis of DiffServ architecture, based on fluid flow modeling approximation to TCP/UDP based traffic flows, is used to emulate millions of competing TCP and UDP flows across networks of various sizes, complexities and link speeds. The emulation involved real-world network constraints, including traffic prioritizations, limited buffers in routers, link congestion, active queue management (AQM) and packet drop policies on congested links. The results provided first quantitative understanding of the interactions of traffic with different drop precedencies in AF1 class traffic in a congested network.

The Robust Header Compression (ROHC) is proposed to implement efficient IP header compression on bandwidth constrained links in TCP/IP networks. In the second part of dissertation, an emulation tool, called CORE, developed at NRL is used to analyze the performance of ROHC. ROHC compresses packet headers at a sender side, and decompresses them at the receiver side to increase efficiency of a communication link. The results provided quantitative understanding of compression gains from using ROHC standard for compressing IPv4/UDP and IPv6/UDP traffic on links.

Committee:

• Dr. Deepinder Sidhu
• Dr. Yelena Yesha
• Dr. Konstantinos Kalpakis
• Dr. Ted M. Foster
• Dr. Edward Zieglar