The UMBC Biological Sciences Department will host two talks on bioinformatics on Wednesday, April 27.

Bioinformatics: Illustrations from 20 years at NCB*I, Dr. Jim Ostell, NCBI, NIH, 11:00am Wednesday 27 April, BS-004

Jim Ostell is one of the founders of NCBI, he will give a general audience talk, ideal for students and faculty from Biology, CS, Chemistry, Statistics and Math that would like to learn what Bioinformatics is about and the history of one of the main bioinformatic center in the world.

Network and state space models: science and science fiction approaches to cell fate predictions, John Quackenbush, Harvard, 12:00pm Wednesday 27 April BS-004

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. ‘Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited, in large part because the high-dimensional they produce cannot be fully constrained by the limited number of measurements and in part because the data themselves represent only a small part of the complete story. To circumvent these limitations, we have developed methods that combine ‘omic data with other sources of information in an effort to leverage, more completely, the compendium of information that we have been able to amass.Here we will present a number of approaches we have developed, with an emphasis on the how those methods have provided into the role that particular cellular pathways play in driving differentiation, and the role that variation in gene expression patterns influences the development of disease states. Looking forward, we will examine more abstract state-space models that may have potential to lead us to a more general predictive, theoretical biology.

MS Defense

Privacy Preservation in Context-Aware Systems

Pramod Jagtap

1:00pm Wednesday, 27 April 2011, ITE 210

Recent years have seen a confluence of two major trends – the increase of mobile devices such as smart phones as the primary access point to networked information and the rise of social media platforms that connect people. Their convergence supports the emergence of a new class of context-aware geosocial networking applications. While existing systems focus mostly on location, our work centers on models for representing and reasoning about a more inclusive and higher-level notion of context, including the user’s location and surroundings, the presence of other people and devices, feeds from social networking systems they use, and the inferred activities in which they are engaged. A key element of our work is the use of collaborative information sharing where devices share and integrate knowledge about their context. This introduces the need for privacy and security mechanisms. We present a framework to provide users with appropriate levels of privacy to protect the personal information their mobile devices are collecting including the inferences that can be drawn from the information. We use Semantic Web technologies to specify high-level, declarative policies that describe user’s information sharing preferences. We have built a prototype system that aggregates information from a variety of sensors on the phone, online sources, and sources internal to the campus intranet, and infers the dynamic user context. We show how our policy framework can be effectively used to devise better privacy control mechanisms to control information flow between users in such dynamic mobile systems.

Presentation

Thesis Committee:

• Dr. Anupam Joshi (chair)
• Dr. Tim Finin
• Dr. Yelena Yesha
• Dr. Laura Zavala

MS Defense

A Framework for GPU 3D Model Reconstruction
Using Structure-from-Motion

Yu Wang

10:00am Thursday, 28 April 2011, ITE 352

A framework for three-dimensional (3D) model reconstruction is described in this thesis. The primary application is scanning forest canopies and assisting scientific applications such as fire hazard evaluation and vegetation biomass estimation, using photos taken from a radio-controlled helicopter, although the methods apply to any series of photographs taken along a path. The approach is based on the fact that the photos are taken in a continuous path, thus taking advantage of the adjacency between images. The major contributions of this project are 1) introduce a linear time complexity algorithm that reduces number of image pairs to match for datasets obtained in a continuous path. 2) present an optimized high performance framework for GPU 3D reconstruction using structure-from-motion.

Thesis Committee:

• Dr. Marc Olano, Chair
• Dr. Penny Rheingans
• Dr. Erle Ellis

MS Thesis Defense

eXtensible Dynamic Form (XDF) for Supplier Discovery

Yan Kang

1:00PM Tuesday, 26 April 2011, ITE 346

Discovery of suppliers (supplier discovery) is essential for building a flexible network of suppliers in a supply chain. The first step for supplier discovery is to collect manufacturing capabilities of suppliers and requirements of customers. In traditional e-marketplaces, online form interfaces are typically used to collect the requirements and capabilities. However, those forms are mostly lack of flexibility to capture a variety of requirements and capabilities in a structured way. In this thesis, we propose new innovative form architecture called eXtensible Dynamic Form (XDF) to facilitate data collection process of supplier discovery.

This architecture provides several key innovations including: 1) architecture for users (suppliers or customers) to create new structure of form for their own contents; 2) an intelligent search engine facilitating users to reuse the existing form components 3) hierarchical representation of the requirements and capabilities as XML instances. Experimental results demonstrate that the proposed architecture is valuable for facilitating the supplier discovery process.

Thesis Committee:

• Dr. Charles Nicholas
• Dr. Yun Peng (Chair)
• Dr. Yelena Yesha

MS Thesis Defense

Recovering from Soft Node Failures in
Wireless Sensor Networks using Neural Networks

Shivvasangari Subramani

9:00am Tuesday, 26 April 2011, ITE 346

In the past few years, wireless sensor networks (WSNs) have become important in different applications because of their robustness in hostile environments. WSNs need to perform in a timely manner in the face of interference, attacks, accidents, and failures. Being a battery operated system, there is a trade-off between performance and energy utilization. In this thesis we focus on WSN accuracy and consider ways to improve the performance of WSNs when sensors become damaged, resulting in poor input signal quality. When all other components of the sensor like the processor, memory, and battery are working, our proposed solution is to learn to undo the damage in a node by training on neighbors sensor values.

Thesis Committee:

• Dr. Anupam Joshi
• Dr. Tim Oates (chair)
• Dr. Mohamed Younis

MS Thesis Defense

A Graph-Theoretic Approach to
Collusion Detection in Multi-Agent Systems

Peter Hamilton

9:00am Thursday, 28 April 28 2011, ITE 325B

The study of trust and cooperation is a major component of multi-agent systems research. Such work often focuses on how best to estimate the reliability of a specific agent, or how to create strategies and protocols that engender the most cooperation from the most agents. However, when cooperation is not a desired aspect of a multi-agent system, these actions define collusive behavior, which can have a significant impact on the dynamics of the system.

This thesis defines a generic, graph-theoretic approach to collusion detection known as CODING. This approach detects group-based collusion, targeting two basic collusion mechanisms that rely on large numbers of colluding agents for success. CODING analyzes and classifies agent interactions from the system and constructs a series of interaction graphs from this data. These graphs are processed for structures that correspond to collusion mechanisms; the agents composing these structures are reported as colluders. CODING is applied to a game theory domain, in which it must detect agents adhering to group strategies in round-robin tournaments composed of single-player strategies.

Thesis Committee:

• Dr. Marie desJardins (chair)
• Dr. Tim Finin
• Dr. Tim Oates