The National Institute of Standards and Technology (NIST) offers paid Summer internships through their SURF program with opportunities at NIST facilities in Gaithersburg and Boulder.  Many of the participating research projects are looking for students majoring in Computer Science or Computer Engineering.

The eleven week program starts in the last week of May and provides a generous stipend and housing (if needed). It is open to United States citizens or permanent U.S. residents who attend U.S. colleges or universities. It is competitive, but in the past, about one in three applicants was accepted. Applications are due by February 1 and must be submitted by UMBC. If you are interested, read more about the application process and come to the NIST SURF Information Session from 3:00 to 4:00 on Friday, December 2 in Sondheim 108.

Dr. Joshi has been a faculty member of UMBC’s Computer Science and Electrical Engineering Department since 1998. His research focuses on wireless and mobile computing, trust, security, privacy issues in distributed systems, and analytics of social media. He is a core member of UMBC's Ebiquity research group.

Congratulations to Dr. Anupam Joshi, who was recently appointed as the Oros Family Professor of Technology. This five year endowed professorship will provide Dr. Joshi with nearly $33,000 to spend on enhancing education in the fields of Information Technology at UMBC.

The Oros Family Professorship in Technology–established by David Oros, a UMBC alumnus who graduated with a Math degree in 1985– was established to support the work of Computer Science faculty whose research is geared toward mobile computing and wireless technology. Dr. Zary Segall, a former professor of Computer Science and Electrical Engineering at UMBC previously served as the Oros Family Professor in Technology.

Apart from being a prestigious distinction, the award allows support by way of a generous donation. Broadly defined, the money will be used to support students with assistantships and fellowships, develop international collaborations, and buy equipment to keep labs up to date. But, the funds are also meant to enhance and extend Dr. Joshi’s own research at the intersection of healthcare IT and mobility.

“The funds enable me to merge these two existing and very strong research threads to pursue a new “Blue Sky” opportunity,” says Dr. Joshi.

For example, Dr. Joshi is interested in creating a mobile device that can be used to diagnose illness— something similar to the “Tricorder” used in Star Trek. What Joshi envisions is a small, wireless tool (think smartphone) that could do things like take sensor readings and measure vitals. Essentially, it could diagnose a patient who is thousands of miles away from a hospital. Joshi explains that the implications of a device like this are especially encouraging for people in remote areas and poorer populations, where access to modern healthcare is limited if not non-existent.  

Though Dr. Joshi acknowledges the incredible potential of the encroaching age of ubiquitous computing, he is nevertheless wary of the consequences. As a result, he hopes the award will help him look more closely at the implications of mobile and social computing on our privacy. This Spring, Dr. Joshi will teach a course on the topic: Security and Privacy in a Mobile Social World, which will look at cases like that of 13-year old Megan Meier, who committed suicide after being cyberbullied by a friend’s mother who was posing as a 16-year old boy on Myspace.

Masters Thesis Defense

Approximation of Nonintegral Frequency Moments

Brian Pilz

10:00am 30 November 2011, ITE325b

Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for i = 1,…,n. For any k, define the frequency moments F_k as F_k = \sum_{i=1}^n m_i^k. Alon, Matias, and Szegedy showed how to estimate F_k for integers k>0 with a one-pass algorithm using O(n^{1-1/k}log n) space for given length m, accuracy, and confidence. Here we extend those results to non-integral k obtaining bounds on the variance giving accuracy and confidence estimates, and giving quantitative results on the algorithm’s space requirements with particular interest to when k is near 1. We also give some performance statistics of the algorithm for these cases and consider an application to entropy estimation. This algorithm is known as a sketching algorithm. Sketching algorithms are probabilistic algorithms generally requiring sublinear space vs. a "classical" O(n) (linear) space requirement, and may have applications for anomaly detection of systems or networks.

Committee:

• Drs. Samuel Lomonaco
• Brooke Stephens
• Kostas Kalpakis (chair)
• Larry Wagoner

EE Graduate Seminar

Time-Varying Amplitude Compression Processing to
Preserve and Enhance Spectral Contrast in Speech Signals

Dr. Janet C. Rutledge
Dean, UMBC Graduate School
Vice-Provost for Graduate Education
Affiliate Associate Professor of Electrical Engineering

11:30-12:45 Friday, 18 November 2011, ITE 231

Multichannel amplitude compression processing is used to reduce the level variations of speech to fit the reduced dynamic ranges of listeners with sensorineural hearing loss. This processing, however, can result in smearing of temporal information, artifacts due to spectral discontinuities at fixed channel edges, and spectral flattening due to reduced peak-to-valley ratios. Presented here is an implementation of a time-varying compression processing algorithm based on a sinusoidal speech model. The algorithm operates on a time-varying, stimulus-dependent basis to adjust to the speech variations and the listeners hearing profile. The algorithm provides fast-acting compression with minimal artifact, has time-varying frequency channels, is computationally inexpensive and preserves the important spectral peaks in speech.

This method has been extended to provide real-time enhancement of spectral peaks and valleys. This work is also related to processing audio signals that will be transmitted over amplitude-limited noisy channels or for listeners in a noisy environment.

Dr. Janet Rutledge is Dean of the Graduate School and Affiliate Associate Professor in the CSEE Department at UMBC. She received the BS in electrical engineering from Rensselaer Polytechnic Institute and the MS and Ph.D. in electrical engineering from Georgia Tech. Prior to coming to UMBC in 2001, she was a faculty member at Northwestern University, and program director at the National Science Foundation.

Host: Prof. Joel M. Morris

EE Graduate Seminar

Time-Varying Amplitude Compression Processing to
Preserve and Enhance Spectral Contrast in Speech Signals

Dr. Janet C. Rutledge
Dean, UMBC Graduate School
Vice-Provost for Graduate Education
Affiliate Associate Professor of Electrical Engineering

11:30-12:45 Friday, 18 November 2011, ITE 231

Multichannel amplitude compression processing is used to reduce the level variations of speech to fit the reduced dynamic ranges of listeners with sensorineural hearing loss. This processing, however, can result in smearing of temporal information, artifacts due to spectral discontinuities at fixed channel edges, and spectral flattening due to reduced peak-to-valley ratios. Presented here is an implementation of a time-varying compression processing algorithm based on a sinusoidal speech model. The algorithm operates on a time-varying, stimulus-dependent basis to adjust to the speech variations and the listeners hearing profile. The algorithm provides fast-acting compression with minimal artifact, has time-varying frequency channels, is computationally inexpensive and preserves the important spectral peaks in speech.

This method has been extended to provide real-time enhancement of spectral peaks and valleys. This work is also related to processing audio signals that will be transmitted over amplitude-limited noisy channels or for listeners in a noisy environment.

Dr. Janet Rutledge is Dean of the Graduate School and Affiliate Associate Professor in the CSEE Department at UMBC. She received the BS in electrical engineering from Rensselaer Polytechnic Institute and the MS and Ph.D. in electrical engineering from Georgia Tech. Prior to coming to UMBC in 2001, she was a faculty member at Northwestern University, and program director at the National Science Foundation.

Host: Prof. Joel M. Morris

This Saturday, November 19, students enrolled in Introduction to Engineering Science (ENES 101) will put robotic vessels to the test during UMBC's first AROW competition. AROW–Academy Robotics on the Water–was developed by Captain Jonathan Russell, Lt. Cmdr. Brian Maggi and Stephen Grenier of the US Coast Guard Academy as an introductory engineering design experience. The competition requires students to design robotic vessels capable of performing simulated tasks akin to those of the US Coast Guard. Forty teams will compete to see how many tasks can be performed by their robotic vessels within a four minute time limit.

What: UMBC AROW Competition

When: Saturday, November 19th 9:30 a.m. to Noon, 1:30 p.m. to 4:00 p.m.

Where: Engineering/ Computer Science Building (ECS), 2nd Floor Atrium

Click here for more information.

This Saturday, November 19, students enrolled in Introduction to Engineering Science (ENES 101) will put robotic vessels to the test during UMBC's first AROW competition. AROW–Academy Robotics on the Water–was developed by Captain Jonathan Russell, Lt. Cmdr. Brian Maggi and Stephen Grenier of the US Coast Guard Academy as an introductory engineering design experience. The competition requires students to design robotic vessels capable of performing simulated tasks akin to those of the US Coast Guard. Forty teams will compete to see how many tasks can be performed by their robotic vessels within a four minute time limit.

What: UMBC AROW Competition

When: Saturday, November 19th 9:30 a.m. to Noon, 1:30 p.m. to 4:00 p.m.

Where: Engineering/ Computer Science Building (ECS), 2nd Floor Atrium

Click here for more information.

Ph.D. Dissertation Defense

Identifying and Isolating Text Classification Signals
from Domain and Genre Noise for Sentiment Analysis

Justin Martineau

1:30-4:00 Friday, 18 November 2011, ITE 325b, UMBC

Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This thesis provides methods to identify, characterize, and isolate the sentiment bearing terms to improve textual sentiment classification when there is little or no labeled data for the domain.

We introduce a new theoretical framework that explains the different sources of noise that affect term level sentiment bias. This noise comes from the genre the author communicates in and the domain or general topic that the author is writing about. To understand the affects of domain noise we defined sentimental domain independence and statistically described it in the multi-domain product review data set. This allowed us to design a Domain Independence Verification Algorithm (DIVA) to eliminate this noise and produce a domain-independent sentiment model using data drawn from a variety of different domains. This model is the most accurate method to classify documents in the 25 category product review data set.

Committee:

• Dr. Tim Finin (chair)
• Dr. Marie desJardins
• Dr. Akshay Java
• Dr. James Mayfield
• Dr. Tim Oates

Ph.D. Dissertation Defense

Identifying and Isolating Text Classification Signals
from Domain and Genre Noise for Sentiment Analysis

Justin Martineau

1:30-4:00 Friday, 18 November 2011, ITE 325b, UMBC

Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This thesis provides methods to identify, characterize, and isolate the sentiment bearing terms to improve textual sentiment classification when there is little or no labeled data for the domain.

We introduce a new theoretical framework that explains the different sources of noise that affect term level sentiment bias. This noise comes from the genre the author communicates in and the domain or general topic that the author is writing about. To understand the affects of domain noise we defined sentimental domain independence and statistically described it in the multi-domain product review data set. This allowed us to design a Domain Independence Verification Algorithm (DIVA) to eliminate this noise and produce a domain-independent sentiment model using data drawn from a variety of different domains. This model is the most accurate method to classify documents in the 25 category product review data set.

Committee:

• Dr. Tim Finin (chair)
• Dr. Marie desJardins
• Dr. Akshay Java
• Dr. James Mayfield
• Dr. Tim Oates

Congratulations to Dr. Yelena Yesha, professor of Computer Science and Electrical Engineering, for receiving this year's IBM CAS Faculty Fellow of the Year award. 

The award is a recognition of Dr. Yesha's positive impact on the goals and reputation of IBM, as well as her influence on IBM's Center for Advanced Studies (CAS) as an ambassador in promoting IBM in academia.