MS Thesis Defense

Finding associations among SNPs for
prostate cancer using collaborative filtering

Rohit Kugaonkar

9:00am Wed. 18 July 2012, Room ITE 325b

Prostate cancer is the second leading cause of cancer related deaths among men. Because of the slow growing nature of prostate cancer, sometimes surgical treatment is not required for less aggressive cancers. Recent debates over prostate-specific antigen (PSA) screening have drawn new attention to prostate cancer. Due to the complicated nature of prostate cancer, studying the entire genome is essential to find genomic traits. Due to the high cost of studying all Single Nucleotide Polymorphisms (SNPs), it is essential to find tag SNPs which can represent other SNPs. Earlier methods to find tag SNPs using associations between SNPs either use SNP's location information or are based on data of very few SNP markers in each sample. Our study is based on 2300 samples with 550,000 SNPs each. We have not used SNP location information or any predefined standard cut-offs to find tag SNPs. Our approach is based on using collaborative filtering methods to find pair wise associations among SNPs and thus list top-N tag SNPs. We have found 25 tag SNPs which have highest similarities to other SNPs. In addition we found 16 more SNPs which have high correlation with the known high risk SNPs that are associated with prostate cancer. We used some of these newly found SNPs with 5 different classification algorithms and observed some improvement in prediction accuracy over using the original known high risk SNPs. The classifier can be used in a decision to perform further testing in case of a "yes" answer by the classifier.

Committee: Drs. Yelena Yesha (chair), Anupam Joshi, Aryya Gangopadhyay and Micheal Grasso.

Professor Marie desJarins (PI) along with co-principal investigators Professor Penny Rheingans and Dr. Susan Martin received a research award worth $200,000 from the National Science Foundation's program on Computing Education for 21st Century. Their 18 month research project  will gather data about the status of Computer Science education in Maryland high schools and build relationships among high school teachers, community college and university faculty, and state education administrators to facilitate and increase state-level support for lasting improvements to computing education.

Despite the overall success of the K-12 education system in Maryland, opportunities to study computer science vary tremendously among the 24 school systems and approximately 200 high schools in the state. This disparity can be attributed to several factors, including the lack of a state-mandated computer science high school graduation requirement, the fact that there is no state-required teacher certification in the discipline, the absence of a standardized computer science curriculum, and barriers to entry for girls and underrepresented minorities.

The effort has two immediate objectives: (1) performing an assessment of the current state of high school computer science in each of the 24 Maryland school systems and (2) increasing knowledge about national issues associated with computer science education among high school and state administrators in Maryland through state-wide summit meetings for teachers, administrators, and higher education faculty. The long term goal is to develop curriculum and teacher development programs that will improve the quality, breadth, and student diversity of computer science education in Maryland.

Ph.D. Dissertation Defense

Relay Node Placement for
Federating Segmented Wireless Sensor Networks

Fatih Senel

2:00pm Tuesday, 10 July 2012, ITE 325b

Recent years have witnessed a growing interest in the applications of Wireless Sensor Networks (WSNs). Most notable among these applications are those operating in hostile environments space exploration, border protection, combat field reconnaissance, and search and rescue. Due to the harsh surroundings, WSNs may suffer from a large scale damage that causes many nodes to fail simultaneously and the network to get partitioned into multiple disjoint segments and its services become very limited. In such a case, restoring the network connectivity is very important in order to avoid negative effects on the applications. Linking disjoint segments may not be feasible through coordinated repositioning of some set of nodes as the scope of the damage is so wide that cannot be determined. One of the viable solutions for federating damaged WSNs is to deploy additional resources, i.e. relay nodes, to form inter-segment multi-hop paths.

In this dissertation, we tackle technical challenges related to the federation of segmented WSNs. We present a set of effective techniques that for repairing the damaged WSN using the least number of relay nodes (RNs) as well as maintaining some desirable topology features such as robustness against failures, network coverage and balanced traffic load. The correctness and time-complexity of all proposed approaches are analyzed and their performance is validated through extensive simulation experiments.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Samuel Lomonaco, Tim Oates, Kemal Akkaya and Waleed Youssef

MS Thesis Defense

Integrating Domain Knowledge in Supervised Machine Learning
to Assess the Risk of Breast Cancer Using Genomic Data

Aniket Bochare

9:00am Friday 29 June 2012, ITE 325b

Breast cancer is the most common form of cancer in women. Breast cancer comprises 22.9% of the invasive cancers in women and 16% of all the female cancers. Currently, treatment decisions are based primarily on clinical parameters, with little use of genomic data. Our study takes into consideration the data of postmenopausal women of European descent and their single nucleotide polymorphism (SNP) information to assess the risk of developing breast cancer. We used various supervised machine learning and data mining techniques to generate a model for predicting risk of breast cancer using only genomic data.

In this research we propose an approach to select the nine best SNPs using various feature selection algorithms to improve binary classification accuracy and validate our results with the existing literature. The machine learning model generated without the domain knowledge yields poor prediction results. After the addition of the domain knowledge of the 11 SNPs into the original training set we performed classification using the best features obtained by feature selection techniques. The machine learning model generated using both the domain knowledge and the feature selection techniques performed much better compared to the naive approach of classification.

Committee: Drs. Yelena Yesha (chair), Anupam Joshi, Aryya Gangopadhyay and Micheal Grasso

Ph.D. Dissertation Defense

Fabrication and Operation of Integrated Distributed-Bragg-Reflector
Thermally Tunable Quantum Cascade Lasers

Liwei Cheng

10:00am Friday, 22 June 2012
Center for Advanced Studied in Photonics Research Conference Room

Quantum cascade lasers (QCLs) that emit in the mid-infrared (IR) range between 3 and 10 μm of the electromagnetic spectrum play an important role in optical gas sensing and molecular spectroscopic applications because several important environmental molecules such as CO, CO2, CH4, and NH3 are known to exhibit strong absorption lines in this mid-IR range. To differentiate such fine absorption features as narrow as a few angstroms, a single-mode QCL with an extremely narrow spectral linewidth, broadly tunable over the molecular absorption fingerprints and operating at sufficient optical power at room temperature, is highly desirable. We present, in this dissertation, two major studies on mid-IR QCLs, one being an improvement in device performance through a buried-heterostructure (BH) regrowth study, and the other being a realization of single-mode tunable QCLs integrated with distributed-Bragg-reflector (DBR) grating and thermal tuning mechanism.

Efficient heat dissipation in the QCL active region, which is crucial for high optical-power operation, can be effectively achieved using BH waveguides laterally embedded with InP grown by metal-organic chemical vapor disposition. We have experimentally studied the effects of the structural features of mesas, such as mesa orientation, geometry, sidewall-etched profile, and the length of the oxide overhang, on the BH regrowth. We find that the mesa oriented in the [01 1 ] direction with smoothly etched sidewalls produces a satisfactory planar growth profile and uniform lateral growth coverage and that a mesa-height–to–overhang-length ratio between 2.5 and 3.0 is effective in reducing anomalous growth in the vicinity of oxide edges. As a result, high-power QCLs capable of producing multi-hundred milliwatts at room temperature at ~4.6 μm and ~7.9 μm through reproducible BH regrowth results have been demonstrated.

We have also demonstrated single-mode tunable QCLs operating at ~7.9 μm with an internal DBR grating structure and thermal tuning scheme incorporated. A special flip-chip bonding configuration and device assembly utilizing two copper heatsinks—one for the gain section and the other for the DBR grating section—were devised and constructed to achieve separate temperature controls in both sections. A miniature thermoelectric (TE) cooler dedicated to the DBR grating section was implemented to control the DBR grating temperature while the gain section was kept at a different temperature to achieve single-wavelength tuning. Under ±1000 mA bias conditions, a quasi-single-wavelength tuning range of ~7.2 cm-1 was realized across the TE cooler temperature span, combined with an additional temperature contrast of 56 °C between the two heatsinks (gain/DBR = 10/66 °C) owing to the implementation of additional temperature-controlling elements. We have also developed a two-dimensional thermal model to investigate the thermal dynamics in the device, including the temperature distribution and thermal dependency of each section, and the thermal response time, which ultimately dictates the wavelength tuning speed. We find that a 250-μm passive section located between the gain and DBR grating section can significantly improve temperature uniformity in both the sections as it absorbs most temperature gradients. Further, a swift thermal response time of ~7 ms is simulated if the DBR grating section is directly bonded on the miniature TE cooler.

More importantly, we have realized a monolithic photonic integration platform, both thermally and electrically, for mid-IR QCLs. The QCLs fabricated in this dissertation possess two major functionalities. The gain section, an active component, is electrically pumped to provide optical gain and is kept at a temperature different from the DBR grating section, and the DBR grating section, a passive component, provides optical feedback for single-wavelength emission and subsequently tunes the emission wavelength through a local temperature variation. Such thermal and optoelectronic integration opens new perspectives for mid-IR QC technology.

Committee: Drs. Fow-Sen Choa, Anthony Johnson, Li Yan, Ryan Robucci, Terrance Worchesky and Jocob Khurgin

Multi-antenna Spectrum Sensing: From GLRTs to LMPITs

Dr. Javier Via
University of Cantabria, Spain

2:00pm Friday 22 June 2012, ITE 325b

Spectrum Sensing represents one of the critical aspects of the Cognitive Radio paradigm, where spectral monitors need to determine the presence or absence of primary users under very low SNR conditions. In this talk, we briefly revisit the main spectrum sensing techniques, with special emphasis in multi-antenna detectors, and we will see that the heuristic eigenvalue-based approaches can be outperformed by making use of some important results in the hypothesis testing literature. In particular, we will consider two related hypothesis testing problems, and derive the locally (under low SNR) best detectors among those preserving the problem invariances. Interestingly, this challenging task can be accomplished thanks to Wijsman's theorem, which allows us to obtain the optimal test statistic without the explicit knowledge of the maximal invariant distributions.

Javier Vía received his Telecommunication Engineer Degree and his Ph.D. in Electrical Engineering from the University of Cantabria, Spain in 2002 and 2007, respectively. In 2002 he joined the Department of Communications Engineering, University of Cantabria, Spain, where he is currently an Associate Professor. He has spent visiting periods at Stanford University, Hong Kong University of Science and Technology, and more recently at SUNY at Buffalo. Prof. Vía has actively participated in several European and Spanish research projects. His current research interests include hypothesis testing and spectrum sensing, quaternion signal processing, and financial engineering.

Host: Tulay Adali

MS Thesis Defense

An Operational Study of DNSSEC and its Practical
Application in Establishing a Secure PKI Framework

Colin Roby

4:00pm 19 June 2012, ITE 325b

With the recent completion of signing the DNS Root and various TLD (top level domain), DNSSEC is gradually progressing towards an internet-wide adoption. The extension of DNSSEC security measures addresses many of the security flaws plagued the underlying DNS architecture since its inception. Once widely deployed, DNSSEC will pave the way for extending security service to a wide range of applications. This study focuses on the practicability of current iteration of DNSSEC implementation. Through a virtual network configuration which mimics a typical corporate environment, we explore viable options to establish a secure PKI framework based on DNSSEC in spite of its current limitations. In this endeavour, we propose a simple yet effective method to combine a corporate existing LDAP based directory service with DNSSEC to form a PKI key exchange infrastructure – one which is intuitive to administer and easy to scale to any large corporate network. We demonstrate the advantage of such a PKI framework in one area of its application – the common use of email. Using a prototype email client application, we illustrate how such a framework can promote and facilitate a more secure email system in terms of authenticity, integrity and confidentiality.

Committee: Dr. Deepinder Sidhu (Chair), Dr. Chein-I Chang, Dr. Yun Peng

Our department is a member of the Computing Research Association, which started in 1972 as a organization of US PhD granting CS departments and has grown to more than 220 North American academic departments of computer science, computer engineering, and related fields; laboratories and centers in industry, government, and academia engaging in basic computing research; and affiliated professional societies. The CRA has become an important advocate in the US for research and education for all of the computing disciplines.

One of its useful services is publishing the free Computing Research News (CRN) five times a year. It has short articles on trends, programs, issues, events, and opportunities as well as advertisements for academic and R&D oriented positions. Reading it is a good way to stay informed about computing research and higher education and to find R&D-oriented jobs.

The CRA has announced that the CRN will no longer be published in hard copy and invites people to sign up to receive announcements of the availability of electronic versions. We encourage anyone interested in computing R&D or higher education to subscribe.

NIST will hold a Big Data Workshop 13-14 June 2012 in Gaithersburg to explore key national priority topics in support of the White House Big Data Initiative. The workshop is being held in collaboration with the NSF sponsored Center for Hybrid Multicore Productivity Research, a collaboration between UMBC, Georgia Tech and UCSD.

This first workshop will discuss examples from science, health, disaster management, security, and finance as well as topics in emerging technology areas, including analytics and architectures. Two issues of special interest are identifying the core technologies needed to collect, store, preserve, manage, analyze, and share big data that could be standardized and developing measurements to ensure the accuracy and robustness of big data methods.

The workshop format will be a mixture of sessions, panels, and posters. Session speakers and panel members are by invitation only but all interested parties are encouraged to submit extended abstracts and/or posters.

The workshop is being held at NIST’s Gaithersburg facility and is free, although online pre-registration is required. A preliminary agenda is available which is subject to change as the workshop date approaches.

Meet Yu Wang, a graduate student pursuing her Ph.D. in Computer Science. Originally from China, Yu came to UMBC in 2009 to study for her Master's in Computer Science. She's part of UMBC's VANGOGH research lab and the UMBC High Performance Computing Facility (HPCF). Once she graduates, Yu hopes to one day work in a research position at the R&D group of the Weta Digital visual effects studio in Wellington, New Zealand.

Click here to learn more about Yu and to hear what she has to say about UMBC.