Seminars and Events
Current

Elsewhere

SANG Seminar Series

GRAND Seminar Series

Test-out Exam: INFS 501

Tuesday, January 17, 2012
2:00 PM
Eng 4201
Registration is required.

Abstract

Please email your intent to csadmin@cs.gmu.edu. Include your name, G number and the exams you wish to register for. A photo ID must be presented on the day of the exam. Each exam will be one hour in length.

It is important to note that you will be permitted to take each exam one time only. Failure to pass the exam will mean that you MUST take the foundation classes before enrolling in any core curriculum course.

Test-out Exam: INFS 515

Tuesday, January 17, 2012
3:30 PM
Eng 4201
Registration is required.

Abstract

Test-out Exam: INFS 519

Wednesday, January 18, 2012
2:00 PM
Eng 4201
Registration is required

Abstract

Test-out Exam: SWE 510

Wednesday, January 18, 2012
3:30 PM
Eng 4201
Registration is required

Abstract

GTA: Orientation

Thursday, January 19, 2012
11:00AM - 12:00PM
Eng 4201
This event is mandatory for all CS department graduate teaching assistants

SWE Seminar: Atomic Section Analysis Tool (AtSAT)

Friday, January 20, 2012
12:00 pm
Eng 4201
Lima Beauvais, Sr. SWE Pal-Tech Inc.

Abstract

Testing the presentation layer of web applications requires novel methodologies. In general analyzing, modeling, and testing web applications and their three main layers creates challenges. However the testing techniques used for traditional software can be applied to the data computation and data representation layers. This talk discusses the Atomic Section Analysis Tool (AtSAT), which helps to mechanize the process of testing the presentation layer of web applications. AtSAT is based on the proposed framework of Offutt and Wu (2009) and automates seven of the nine steps; reducing the time to apply the methodology and minimizing human errors.

Speaker's Bio

Lima Beauvais earned an MS degree in Instructional Technology at Bloomsburg University, PA in June 2001. He is currently a candidate for the Engineer Degree at GMU, Fairfax, with a concentration on software testing. He worked as a Senior Multimedia Developer at PerformTech, Inc. from June 2001 to November 2007, developing computer-based and web-based courseware. He has been working as a Senior Software Engineer at Pal-Tech, Inc. since November 2007, developing web applications and training packages. He taught seminar classes on multimedia development at the Art Institute of Washington in Arlington, VA and Sanford Brown college in McLean, VA. He is a member of the Corporate Advisory Council (CAC) of the Institute of Interactive Technology at Bloomsburg University.

SWE Seminar: A Tour of the Piazza Discussion Forums

Tuesday, January 24, 2012
12:00 pm
Research I, Rm. 163
Piazza Team

Abstract

Members of the Piazza team are visiting GMU on Tuesday, January 24 for a lunchtime seminar, with lunch provided. They will spend some time demonstrating the site, sharing best practices, and answering any questions.

Piazza is a free online gathering place where students can ask, answer, and explore 24/7, under the guidance of their instructors. Students as well as instructors can answer questions, fueling a healthy, collaborative discussion. Instructors can go into deeper detail on complex topics, and spot areas where students are struggling.

In SWE 432, we found that Piazza streamlined the teaching experience. All those hours spent responding to individual emails can now be put to better use. You will never have to answer the same question twice. Better yet, a student might answer it for you. On top of that, you always have complete editorial control over your class.

Most bulletin boards are organized top-down with the instructor creating and controlling all topics and threads. Piazza allows bottom-up organization by students, leading to a richer, more interactive, more collaborative, and more free learning experience. This leads to more participation from students and more learning by students.

You can read more about Piazza in this article from the New York Times: http://www.nytimes.com/2011/07/04/technology/04piazza.htm. Or you can see demos and sign up at http://www.piazza.com.

CS Dept Colloquium: Searching in Sequences of Documents and in Biological Sequences

Thursday, February 23, 2012
11:00 AM
Eng 4201
Dimitris Gunopulos

Abstract

We consider the problem of searching in two domains where the ordering is important, namely biological sequence data, and data from live, time-stamped data collections (such as blogs). As the number and size of such data collections increase, the problem of efficiently indexing and searching such data becomes more important. We present novel approaches for subsequence matching and for keyword search and event identification in document sequences.

Speaker's Bio

Dimitrios Gunopulos got his PhD from Princeton University in 1995. He has held positions as a Postoctoral Fellow at the Max-Planck-Institut for Informatics, Research Associate at the IBM Almaden Research Center, Visiting Researcher at the University of Helsinki, Assistant, Associate, and Full Professor at the Department of Computer Science and Engineering in the University of California Riverside, and Associate Professor in the Department of Informatics and Telecommunications, University of Athens. His research is in the areas of Data Mining, Knowledge Discovery in Databases, Databases, Sensor Networks, Peer-to-Peer systems, and Algorithms. He has co-authored over a hundred journal and conference papers that have been widely cited and a book. He has supervised 10 Ph.D. theses and 19 MS. His research has been supported by NSF (including an NSF CAREER award), the DoD, the Institute of Museum and Library Services, the Tobacco Related Disease Research Program, the European Commission, AT&T and Nokia. He has served as a General co-Chair in IEEE ICDM 2010, as a PC co-Chair in ECML/PKDD 2011, IEEE ICDM 2008, ACM SIGKDD 2006, SSDBM 2003, and DMKD 2000, and as an associate Editor at KAIS, at IEEE TKDE, at IEEE TPDS, and at ACM TKDD.

Host: Carlotta Domeniconi (carlotta@cs.gmu.edu)

Seminar: Probabilistic Hashing Methods for Fitting Massive Logistic Regressions and SVM with Billions of Variables

Friday, February 24, 2012
11:00AM - 12:00PM
Johnson Center, 3rd Fl, Room B
Ping Li, Department of Statistical Science, Cornell University

Abstract

In modern applications, many statistics tasks such as classification using logistic regression or SVM often encounter extremely high-dimensional massive datasets. In the context of search, certain industry applications have used datasets in 264 dimensions, which are larger than the square of billion. This talk will introduce a recent probabilistic hashing technique called b-bit minwise hashing (Research Highlights in Comm. of ACM 2011), which has been used for efficiently computing set similarities in massive data. Most recently (NIPS 2011), we realized that b-bit minwise hashing can be seamlessly integrated with statistical learning algorithms such as logistic regression or SVM to solve extremely large-scale prediction problems. Interestingly, for binary data, b-bit miwise hashing is substantially much more accurate than other popular methods such as random projections. Experimental results on 200GB data (in billion dimensions) will also be presented.

SWE Seminar: Taming Uncertainty in Self-Adaptive Software

Tuesday, February 28, 2012
12:00 pm
ENGR 4201
Naeem Esfahani, Ph.D. Candidate Computer Science

Abstract

Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation decisions. This has been one of the key obstacles to wide-spread adoption of self-adaption techniques in risk-averse real-world settings. In this talk, I describe an approach, called POssIbilistic SElfaDaptation (POISED), for tackling the challenge posed by uncertainty in making adaptation decisions. POISED builds on possibility theory to assess both the positive and negative consequences of uncertainty. It makes adaptation decisions that result in the best range of potential behavior.

Speaker's Bio

Naeem Esfahani is a Ph.D. candidate in Computer Science Department, Volgenau School of Engineering. He got his B.Sc. degrees on Electrical and Computer Engineering from University of Tehran in 2005. He also received a M.Sc. degree in Computer Engineering from Sharif University of Technology in 2008. His current research mainly focuses on Software Architecture, Self-Adaptive Software Systems, and Software Quality of Service Analysis & Improvement.

CS Colloquium: Geometric Modeling for Humans

Friday, March 02, 2012
10:00am-11:00am
ENGR 4201
Yotam Gingold

Abstract

Digital content creation is fundamental to many areas of computer graphics, from image processing to 3D geometry processing and animation. For example, the creation and editing of 3D models impacts everything from the design of objects in the real world to visualization and digital entertainment. And yet, the tools used to create and edit 3D geometry are cumbersome, accessible only to a small group of experts.

In this talk, I will present my vision of accessible digital content creation for everyone, from novices to highly trained experts (and even computers). I will demonstrate tools that allow novices to participate in 3D modeling by leveraging skills they naturally possess. I will also discuss new ways to leverage the expertise of experts. We will be helped along the way by fast and stable optimization techniques.

I will conclude my talk by presenting a new way to approach problems in computer graphics. I will show how Human Computation enables us to make seemingly impossible algorithms a reality.

Speaker's Bio

Yotam Gingold is a post-doctoral researcher in the computer science departments of Columbia University and Rutgers University. His research interests include interactive geometric modeling, human computation, topology for computation, and game design. Yotam earned his Ph.D. in Computer Science from New York University in 2009 under the supervision of Denis Zorin.

SANG Seminar: Multipath Routing in Wireless Mesh Networks

Monday, March 05, 2012
12:30-1:30 PM
ENGR 4201
Li Xiao, Associate Professor, Michigan State University

Abstract

The wireless mesh network offers a breakthrough approach that delivers wireless services for a large variety of applications. Equipped with recent significant advances in wireless radios, multi-radio/multi-channel mesh routers provide both great promise and substantial research challenges to significantly enhance network performance while dramatically reducing cost. In this talk, I will present our research efforts on multi-path routing in wireless mesh networks. Multi-source video on-demand streaming has been applied in wired networks with great success. However, it remains a challenging task in wireless networks due to wireless interference. I will introduce our multi-path routing and rate allocation study to support multi-source video on-demand applications in wireless mesh networks.

Speaker's Bio

Li Xiao is an associate professor of computer science and engineering at Michigan State University. Her research interests are in the areas of distributed and networking systems, overlay networks and applications, wireless networks and mobile computing. Her research has been supported by the National Science Foundation, Microsoft Research, the Internet2 program, and the Michigan Space Grant Consortium. She is serving on the Editorial Boards of IEEE Transactions on Parallel and Distributed Systems and Peer-to-Peer Networking and Applications Journal. She has served as workshop program chair, conference vice chair and track chair, and on various technical program committees for conferences in the areas of distributed systems and computer networks. She received her PhD degree in computer science from the College of William and Mary.

Distributed Simulation Seminar: Parallel Computing & Massive Simulations at ISISLab

Tuesday, March 06, 2012
2:00 PM
ENGR 4201
Vittorio Scarano, Dipartimento di Informatica, Università di Salerno

Abstract

In this talk, we will report on the research that is conducted in our lab on how parallel computing can be used for massive simulations. In particular, our focus is on different sw/hw architectures (GPUs, MPI, heterogeneous clusters) and how the challenge of efficient simulations is differently tackled under different constraints.

ISISLab: http://www.isislab.it

GRAND Seminar: Quantitative Myocardial Perfusion MRI: From Sector-Based to Pixel-Based Analysis

Tuesday, March 06, 2012
12:00 PM
ENGR 4201
Li-Yueh Hsu, National Heart, Lung & Blood Institute, National Institutes of Health

Abstract

Dynamic contrast-enhanced myocardial perfusion MRI has become an important tool for the diagnosis of coronary artery disease. Quantitative assessment of cardiac perfusion images using estimates of myocardial blood flow and myocardial perfusion reserve has shown promising results in several pre-clinical and clinical studies. This presentation will provide an overview of current sector-based analysis for quantifying first-pass contrast-enhanced cardiac MR images. A new approach for pixel-based myocardial blood flow quantification will also be presented. The results of high-resolution perfusion pixel maps from both animal and human studies will be presented and discussed. Technical challenges in fully automated pixel-wise quantification of cardiac perfusion MR images will then be addressed.

Speaker's Bio

Li-Yueh Hsu received his D.Sc. degree in Biomedical Engineering from the George Washington University. He is currently a staff scientist at the National Heart, Lung, and Blood Institute at the National Institutes of Health. His broad research interests are biomedical imaging and image analysis, computational modeling in biology and physiology, computer-aided detection, and diagnosis systems. His recent research has focused on the cardiovascular magnetic resonance imaging and quantitative analysis of myocardial perfusion and tissue characterization.

SANG Seminar: An Analysis of the Business Behind Monetizing Spam

Friday, March 09, 2012
12:00AM - 1:00PM
ENGR 4201
Damon McCoy, Assistant Professor, Computer Science

Abstract

Spam advertising is a business that continues to exist despite attempts to intervene at many of the levels visible in the actual spam messages (i.e. spam filtering, URL blacklisting, domain and hosting takedowns). It continues to exist, in spite of these adversarial pressures, because it fuels a profitable enterprise. In this talk, I will present our efforts to perform a holistic empirical analysis that quantifies the full set of resources employed to monetize spam advertising counterfeit goods by collecting extensive measurements of three months of email sp chases from spam-advertised sites, along with an in-depth analysis of leaked data that provides a rare view into the inner-working's of major affiliate programs that monetize spam. Through this analysis we provide valuable insight into the cost structure of these affiliate programs and strong evidence of payment bottlenecks in their value chain.

Speaker's Bio

Damon McCoy is an assistant professor in the computer science department at George Mason University. He obtained his Ph.D. from the University of Colorado, Boulder in 2009. In 2010 and 2011, he was a Computer Innovation Fellow at the University of California, San Diego. His research includes work on the economics of e-crime, wireless privacy, anonymous communication systems, and cyber-physical security. More generally, he is interested in exploring and improving the security and privacy of large-scale systems.

CS Seminar: Bayesian Inference

Tuesday, March 20, 2012
PART 1: 8:00-11:00 AM. PART 2: 12:30-3:30 PM
SUB II (The Hub) Rooms 3, 4, and 5
John Myles White

Abstract

NOTE: This seminar is in two parts. The first part runs from 8 to 11 AM. The second part runs from 12:30 to 3:30 PM.

Section 1: An Introduction to Bayesian Inference - Introduce the Bayesian paradigm of inference as probabilistic calculation - Provide a loose treatment of the Cox axioms - Discuss useful statistical theory: - Likelihood functions - Maximum likelihood estimation - Fisher information - Bias, variance, consistency and the Central Limit Theorem for estimators - Review standard probability distributions - Go through the classical coin-filpping example in detail with a beta prior - Describe results of Bayesian inference as comparable to MLE with regularization added in

Section 2: BUGS as a Tool for Automating Bayesian Inference - Describe how to specify models using BUGS language - Go through many example models - Normal with unknown mean, known variance - Normal with unknown mean, unknown variance - Linear regression: unknown coefficients and variance, Normal priors - Linear regression with Laplace priors - Logistic regression - Hierarchical models - LDA - SNA models

Section 3 (Optional): Implementing Samplers and MCMC by Hand - Introduce the Ising model as a canonical distribution for sampling - Review sampling techniques: - Rejection sampling - Slice sampling - Metropolis-Hasting sampling - Gibbs sampling

Speaker's Bio

John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Along with the political scientist Drew Conway, he is the author of a book recently published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. John is now working with the statistician Mark Hansen on a book for laypeople about exploratory data analysis. He is also the lead maintainer for several popular R packages, including ProjectTemplate and log4r.

CS Colloquium: Procedural Content Generation for Game Design

Tuesday, March 20, 2012
11:00am-12::00pm
ENGR 4201
Gillian Smith

Abstract

The future of digital games lies in the development of new technologies that support new game genres and player experiences. One such technology is procedural content generation -- the use of a computer to create game content that would normally be made by a human designer -- which offers a number of opportunities for game design: it can be employed as an on-demand game designer, capable of assisting human designers with creating content or crafting a unique experience for each player. However, such abilities can only be achieved through imbuing the generator with an understanding of game design principles and creating a means for a human designer to communicate about these principles with the generator. This talk describes an approach to procedural content generation that incorporates an understanding of game pacing, and discusses the implications of this design decision for the game design process through examining two different projects: a tool that uses procedural content generation to support players in designing their own game levels, and a game that has players explore an infinitely generated world that morphs according to their choices.

Speaker's Bio

Gillian Smith is a PhD candidate in the Center for Games and Playable Media (Augmented Design Lab and Expressive Intelligence Studio) at the University of California, Santa Cruz. Her research interests sit at the intersection of artificial intelligence, human-computer interaction, game studies, and design studies. Her current focus is on procedural content generation and how it changes the game design process, in terms of both creating tools for novice designers and enabling entirely new kinds of games. Her latest project, Endless Web, is a game that uses procedural content generation to create an infinite world for the player to explore that adapts to the choices they've made. She is also interested in studying gender issues in games, and methods for increasing girls' and women's participation in computer science and game design.

SANG Seminar: A Server's Perspective of Internet Streaming Delivery to Mobile Devices

Friday, March 23, 2012
2:00 PM
ENGR 4201
Yao Liu

Abstract

Internet streaming services to mobile devices are getting more and more popular with the pervasive adoption of all kinds of mobile devices in practice. To understand and provide better streaming services to mobile devices, a number of studies have been conducted to investigate streaming services to mobile devices. However, these studies have mainly focused on the client side resource consumption and streaming quality. So far, little is known about the server side, which is the key for providing successful mobile streaming services.

In this talk, we present our investigation of the Internet mobile streaming service at the server side. For this purpose, we have collected a one-month server log (with 212 TB delivered video traffic) from a top Internet mobile streaming service provider serving worldwide mobile users. I will present our findings through the measurement and analysis.

Speaker's Bio

Yao Liu is a Ph.D. student of Computer Science Department at George Mason University.

CS Colloquium: Physical Motion Control and Analysis in Games, Visual Effects and Training.

Monday, March 26, 2012
11:00am-12:00
ENGR 4201
Brian Allen

Abstract

The synthesis of realistic motion is a key component of visual effects and computer games. Correspondingly, the dual problem of recognizing a particular grace in motion, has the potential to improve the training of movement skills and animation. As humans become proficient with a manual skill, their motion becomes more fluid, more efficient and more compliant. Physical simulation, now cheap and ubiquitous, is a promising means for creating and understanding motion. In contrast to key-frame animation or motion capture, characters driven by physical laws can move in new, dynamic and unforeseen ways in response to their environment and user interaction. However, a key challenge with using physically simulated characters is developing controllers capable of reproducing the fluidity and compliance of well-practiced motion.

In this talk, I will present new approaches for both the control and the analysis of fluid and compliant physical motion. For control, I will introduce a novel solution to a classic, low-level control equation. This solution provides an analytic method for determining the character's compliance, that is, how the simulated character will respond to unexpected collisions. I will also introduce a biologically inspired method for generating high-level controllers capable of complex and dynamic whole-body behaviors.

Additionally, I will show that such control techniques can also serve in the analysis of human motion, for example, in estimating motor-skill level based only on observed motion, or in predicting future movements. I will illustrate both synthesis and analysis of motion with examples from a range of applications in computer games, visual effects, robotics, virtual reality and medical training.

Speaker's Bio

Brian F. Allen is a Senior Research Fellow at the Institute for Media Innovation at the Nanyang Technological University in Singapore. His research focuses on natural motion and physical simulation with applications to games, visual effects and medicine. He has ten years of software development experience, including working with Industrial Light and Magic R&D, University of Southern California's Institute for Creative Technologies, and co-founding and serving as CTO of Silicon Age, a San Francisco-based software consultancy. He received his B.S. in Computer Science from Iowa State University and his PhD from the University of California, Los Angeles with the advisement of Petros Faloutsos.

GRAND Seminar: White-box Data Mining Algorithms

Monday, March 26, 2012
3:00pm
ENGR 3507
Boris Delibasic

Abstract

Choosing the right algorithm for data at hand was always a major problem in data mining. We propose a new architecture for decision-support systems for data mining, with the ability of generic algorithm design to help users choose the right algorithm. Opposite to the prevalent black-box approach of using algorithms in data mining were users have the ability to define inputs, setup parameters and read outputs, we propose using reusable component (RC) based algorithms. The RC-based algorithms are assembled from reusable components, which are standalone algorithm units which were originally found in black-box algorithms and their partial improvements. RC based algorithms have been proven to better adapt to data than black-box algorithms that, due to “hard” bindings of algorithm parts, are disabled to achieve best results on some datasets. On the other hand, the RC-based approach of algorithm design produces a galore of algorithms making it thus harder to search through the algorithm space. We show how this problem can be solved using meta-heuristics for searching through the algorithm space. We also propose further research directions that will enable to connect the proposed approach with meta-learning. We believe that users will be better supported in the future for choosing an adequate algorithm for the problem at hand, because the decision support system will be enabled to perform an intelligent search through the algorithm space that is based on dataset properties, algorithm performance results, empirical rules gained from meta-learning and theoretical support.

Speaker's Bio

Boris Delibasic is an associate professor at the University of Belgrade in Serbia. His main research interests are data mining, decision support systems, business intelligence, and decision theory. Dr. Delibasic is also an adjunct lecturer at the University of Jena in Germany. He has already published several research articles in top-ranked international journals. A project he is currently engaged with is dealing with design of white-box algorithms for data mining (www.whibo.fon.bg.ac.rs). In 2011, Prof. Delibasic received a prestigious Fulbright fellowship to work as a visiting scholar at Zoran Obradovic’s Center for data analytics and biomedical informatics at Temple University in Philadelphia, PA. His current research objectives are to design spatio-temporal algorithms for analysis of ski injuries and to discover ski injury patterns that could be used for injury prevention. Algorithms developed for ski injury analysis, are planned in a later stage to be extended, to analyze large scale data on road traffic accidents. Dr. Delibasic is also ski patroller on Serbian mountains during the winter season.

CS Colloquium: 3D Virtual Characters: Skinning, Clothing, and Weird Math

Thursday, March 29, 2012
11:00am-12:00pm
ENGR 4201
Ladislav Kavan

Abstract

This talk presents an overview of my research on real-time 3D graphics, focusing on technology related to virtual characters. First, I will talk about skinning, i.e., the problem of how to translate skeletal animation to full body deformations. I will explain the advantages of using dual quaternions as opposed to the more traditional matrix representation. Next, I will follow with my contribution to real-time cloth animation, discussing how to create upsampling operators that add fine-scale details to coarse mesh simulations. The techniques required to design efficient and robust upsampling operators include harmonic regularization (an extension of the classical Tikhonov approach) and "tracking," i.e., constraining fine-scale physics to follow a given coarse-scale animation. I will conclude with some ideas for future work, for example, how to make digital content creation more intuitive.

Speaker's Bio

Ladislav Kavan is a Senior Researcher at ETH Zurich, working in Interactive Geometry Lab with Prof. Olga Sorkine. Prior to joining ETH, he was a Research Scientist for Disney Interactive Studios, where he worked on next generation technology for computer games with Peter-Pike Sloan. Ladislav's recent work has focused on combining data-driven techniques with physically-based simulation, subspace methods, and real-time character animation. Some of these results have been used in production in the game and film industries. Ladislav received his M.S. in computer science from Charles University and Ph.D. from Czech Technical University in Prague.

GRAND Seminar: Pedestrians to Cities with Agent-based modeling and GIS

Tuesday, April 03, 2012
12:00pm
ENGR 4201
Andrew Crooks

Abstract

This talk will explore how agent-based models can be explicitly linked to "real world" locations with spatial information and be used to explore a wide range of social phenomena. From that of the small scale movement of pedestrians over seconds; to that of urban growth over decades. All the applications will focus on individuals or groups of individuals and how such interactions lead to more aggregate patterns emerging. Moreover, the talk will demonstrate how new technologies and sources of information (e.g. volunteered geographic information and twitter) can be used to inform the model building process.

Speaker's Bio

Andrew Crooks is an assistant professor in the Department of Computational Social Science and a researcher in the Center for Social Complexity at George Mason University. He holds a PhD from University College London.

His research relates to exploring, understanding and the communication of urban built and socio-economic environments using geographic information systems (GIS), spatial analysis, geovisualisation, social network analysis and agent-based modeling methodologies. Further information about these interests is available on his blog http://gisagents.blogspot.com/ or personal website http://www.css.gmu.edu/andrew/

SWE Seminar: Adding Criteria-Based Tests to Test Driven Development

Friday, April 06, 2012
1:30pm
ENGR 4801
Bill Shelton

Abstract

Test driven development (TDD) is the practice of writing unit tests before writing the source. TDD practitioners typically start with example-based unit tests to verify an understanding of the software’s intended functionality and to drive software design decisions. Hence, the typical role of test cases in TDD leans more towards specifying and documenting expected behavior, and less towards detecting faults. Conversely, traditional criteria-based test coverage ignores functionality in favor of tests that thoroughly exercise the software. This paper examines whether it is possible to combine both approaches. Specifically, can additional criteria-based tests improve the quality of TDD test suites without disrupting the TDD development process?

This paper presents the results of an observational study that generated additional criteria-based tests as part of a TDD exercise. The criterion was mutation analysis and the additional tests were designed to kill mutants not killed by the TDD tests. The additional unit tests found several software faults and other deficiencies in the software. Subsequent interviews with the programmers indicated that they welcomed the additional tests, and that the additional tests did not inhibit their productivity.

Speaker's Bio

William "Bill" Shelton is a PhD student in the Computer Science Department at the Volgenau School of Information Technology and Engineering at GMU, currently focusing on applying software testing research to real world situations. He received his bachelor’s degree in Music from Berklee College of Music, Boston, MA., and his M.S. degree in Software Engineering from George Mason University. He is currently employed as a Sr. Software Engineer at the new Consumer Financial Protection Bureau where his focus is, among many other software engineering tasks, test automation and continuous delivery.

SWE Seminar: Better Algorithms to Minimize the Cost of Test Paths

Friday, April 06, 2012
1:30pm
ENGR 4801
Nan Li

Abstract

Model-based testing creates tests from abstract models of the software. These models are often described as graphs, and test requirements are defined as subpaths in the graphs. As a step toward creating concrete tests, complete (test) paths that include the subpaths through the graph are generated. Each test path is then transformed into a test. If we can generate fewer and shorter test paths, the cost of testing can be reduced. The minimum cost test paths problem is finding the test paths that satisfy all test requirements with the minimum cost.

This paper presents new algorithms to solve the problem, and then presents data from an empirical comparison. The algorithms adapt approximation algorithms for the shortest superstring problem. The comparison is with an existing tool that uses a brute force approach to extend each subpath to a complete path. One new algorithm is based on the greedy set-covering algorithm and the other is based on finding a matching over a prefix graph. The comparison was performed on open software and showed that both new solutions generate fewer test paths than the brute force approach. The prefix-graph based solution takes much less time than the other two solutions when the number of test requirements is large.

The paper has been accepted for ICST 2012 in Montreal, Canada. This seminar is a practice for Nan's conference presentation.

Speaker's Bio

Nan Li is a PhD student in Computer Science Department, Volgenau School of Information Technology and Engineering at GMU. He received his bachelor’s degree in Software Engineering from Beihang University in China in 2006 and his M.S. degree in Computer Science from Fairleigh Dickinson University in 2008. His current research mainly focuses on mutation testing, model-based testing and test automation.

Oral Defense of Doctoral Dissertation: Defeating Insider Attacks via Autonomic Self-Protective Networks

Friday, April 06, 2012
10:00am-12:00pm
ENGR 1602
Faisal M. Sibai

Abstract

There has been a constant growing security concern with insider attacks on network accessible computer systems. Users with power credentials can do almost anything they want with the systems they own with very little control or oversight. Most breaches occurring nowadays by power users are considered legitimate access and not necessarily intrusions. Developing a solution for such problems is challenging because power users need flexible requirements to administer or maintain their systems. The increased usage of virtual environments, virtual systems, teleworking, and remote usage has made network access the preferred method for system administration.

This dissertation describes the design and implementation of a network Autonomic Violation Prevention System (AVPS) framework that is intended to defeat the insider threat in organizations. The AVPS sits between privileged users and applications. It monitors traffic that traverses the network and takes actions as needed. A proof of concept prototype for the system was developed in a virtualized environment. FTP and Telnet were part of the application testbed. Rules that pertain to privileged user administration were applied. Actions that were tested successfully included traffic monitoring, replacement, blocking, and dropping.

This work also examined the scalability of the AVPS design. An experimental testbed was built to obtain performance measures of the AVPS overhead, throughput, and response time. FTP, Database and Web servers were used in the application testbed. A variety of tests were performed including automated simultaneous transactions and manual simultaneous transactions. An M/M/N//M analytic queuing model was used to assess how well the AVPS system would perform for a finite population where the numbers of applications, users and AVPS engines vary under different load levels. The results showed that the AVPS exhibits a very low overhead and is therefore scalable.

The AVPS architecture design was further enhanced to automate how signatures are created. Autonomic self-protection capabilities were added into the framework by implementing high level rules that set the goal for how violations are detected and signatures are created. Supervised self-learning capabilities were added via the use of Support Vector Machines (SVM) in order to classify the raw data and make final decisions on what is considered a violation and what is considered normal insider behavior.

Speaker's Bio

Bachelor of Computer and Information Systems Science, King Saud University, 2000 Master of Science, George Mason University, 2008

SANG Seminar: S-STORE: Socially-Aware Distributed Data Storage

Friday, April 06, 2012
2:00-3:00pm
ENGR 4201
Duc Tran

Abstract

Online social networking has become ubiquitous. For a social storage system to keep pace with increasing amounts of user data and activities, an intuitive solution is to deploy more servers. A challenge then is how to partition the data across the servers so that server efficiency and load balancing can both be achieved. Another challenge is how to provide data redundancy in the forms of replication or erasure coding in order to improve the system's data availability. These challenges are especially amplified for social data storage because we should take into account the data's social relationships which imply how often certain data are accessed together in a transaction. This is not to mention the dynamics of social data which changes frequently over time. State-of-the-art storage techniques are not socially-aware, not seriously taking into account these issues. This talk presents S-STORE, a novel socially-aware storage framework which consists of three key techniques: S-PUT for data partition, S-CLONE for data replication, and S-CODE for erasure data coding. In addition to theoretical concepts, preliminary evaluation results are also discussed.

Speaker's Bio

Duc A. Tran is an Assistant Professor of Computer Science at the University of Massachusetts at Boston, leading the Network Information Systems Laboratory (NISLab). He received a PhD degree from the University of Central Florida (Orlando, Florida). Dr. Tran's research interests are in the areas of networking and distributed systems. The results of his work have led to research awards from the NSF, Best Paper Award at ICCCN 2008, and Best Paper Recognition at DaWak 1999. Dr. Tran has served as a Review Panelist for the NSF, Editor for the Journal on Parallel, Emergent, and Distributed Systems (2010-date) and ISRN Communications Journal (2010-date), Guest-Editor for the Journal on Pervasive Computing and Communications (2009), TPC Co-Chair for CCNet (2010, 2011), GridPeer (2009, 2010, 2011), and IRSN 2009, and TPC Vice-Chair for AINA 2007. Dr. Tran is a Senior Member of the ACM and a Professional Member of the IEEE.

SWE Seminar: Software Testing Research at ICMC

Wednesday, April 11, 2012
11:00am
ENGR 4801
Marcio Delamaro

Abstract

In this talk prof Delamaro will present the highlights of the research he and his group are developing at the Instituto de Ciências Matemáticas e de Computação of the University of São Paulo, in Brazil. The themes addressed include techniques, criteria and tools for software testing in different domains such as embedded systems and virtual reality environments.

Speaker's Bio

Prof Marcio Delamaro has a Bachelor and a Masters degree in Computer Science and a Doctorate degree in Computational Physics. From 1997 he has been working as teacher and researcher in universities in Brazil. In 2000 visited the Politecnico di Milano in Italy for a one year pot-doc stage. Currently he is Associate Professor at Universidade de São Paulo, the larger and most prestigious university in Latin America. His area of interest is software testing.

GRAND Seminar: Clustering Algorithms for Streaming and Online Settings

Friday, April 13, 2012
12:00pm
ENGR 4201
Claire Monteleoni

Abstract

Clustering techniques are widely used to summarize large quantities of data (e.g. aggregating similar news stories), however their outputs can be hard to evaluate. While a domain expert could judge the quality of a clustering, having a human in the loop is often impractical. Probabilistic assumptions have been used to analyze clustering algorithms, for example i.i.d. data, or even data generated by a well-separated mixture of Gaussians. Without any distributional assumptions, one can analyze clustering algorithms by formulating some objective function, and proving that a clustering algorithm either optimizes or approximates it. The k-means clustering objective, for Euclidean data, is simple, intuitive, and widely-cited, however it is NP-hard to optimize, and few algorithms approximate it, even in the batch setting (the algorithm known as "k-means" does not have an approximation guarantee). Dasgupta (2008) posed open problems for approximating it on data streams.

In this talk, I will discuss my ongoing work on designing clustering algorithms for streaming and online settings. First I will present a one-pass, streaming clustering algorithm which approximates the k-means objective on finite data streams. This involves analyzing a variant of the k-means++ algorithm, and extending a divide-and-conquer streaming clustering algorithm from the k-medoid objective. Then I will turn to endless data streams, and introduce a family of algorithms for online clustering with experts. We extend algorithms for online learning with experts, to the unsupervised setting, using intermediate k-means costs, instead of prediction errors, to re-weight experts. When the experts are instantiated as k-means approximate (batch) clustering algorithms run on a sliding window of the data stream, we provide novel online approximation bounds that combine regret bounds extended from supervised online learning, with k-means approximation guarantees. Notably, the resulting bounds are with respect to the optimal k-means cost on the entire data stream seen so far, even though the algorithm is online. I will also present encouraging experimental results.

This talk is based on joint work with Nir Ailon, Ragesh Jaiswal, and Anna Choromanska.

Speaker's Bio

Claire Monteleoni is an assistant professor of Computer Science at George Washington University. Previously, she was research faculty at the Center for Computational Learning Systems, and adjunct faculty in the Department of Computer Science, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Masters in Computer Science, at MIT. Her research focus is on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and Climate Informatics: accelerating discovery in Climate Science with machine learning. Her papers have received several awards, and she currently serves on the Senior Program Committee of the International Conference on Machine Learning, and the Editorial Board of the Machine Learning Journal.

CS PhD Club: Meeting

Friday, April 13, 2012
3:00pm-4:00pm
ENGR 4201
Jana Kosecka

SWE Seminar: Toward Harnessing High-Level Language Virtual Machines for Further Speeding Up Weak Mutation Testing

Friday, April 13, 2012
11:00am
ENGR 4201
Vinicius Durelli

Abstract

High-level language virtual machines (HLL VMs) are now widely used to implement high-level programming languages. To a certain extent, their widespread adoption is due to the software engineering benefits provided by these managed execution environments, for example, garbage collection (GC) and cross-platform portability. Although HLL VMs are widely used, most research has concentrated on high-end optimizations such as dynamic compilation and advanced GC techniques. Few efforts have focused on introducing features that automate or facilitate certain software engineering activities, including software testing. This research suggests that HLL VMs provide a reasonable basis for building an integrated software testing environment. As a proof-of-concept, we have augmented a Java virtual machine (JVM) to support weak mutation analysis. Our mutation-aware HLL VM capitalizes on the relationship between a program execution and the underlying managed execution environment, thereby speeding up the execution of the program under test and its associated mutants. To provide some evidence of the performance of our implementation, we conducted an experiment to compare the efficiency of our VM-based implementation with a strong mutation testing tool (muJava). Experimental results show that the VM-based implementation achieves speedups of as much as 89 percent in some cases.

Speaker's Bio

Vinicius Durelli is a Ph.D. candidate in Computer Science at University of São Paulo, Brazil. He received his M.S.on Computer Science from Federal University of São Paulo in 2008. His research interests focus on Software Testing, High-level Language Virtual Machines, and Refactoring. Currently, he has been trying to retrofit software testing features into high-level language virtual machines.

Oral Defense of Doctoral Dissertation: Performance Management for Energy Harvesting Wireless Sensor Networks

Wednesday, April 18, 2012
10:30-12:30
ENGR 4201
Bo Zhang

Abstract

A Wireless Sensor Network (WSN) consists of spatially distributed sensor nodes which monitors environmental conditions such as temperature, humidity, sound or pressure, etc. Recently there is increasing need to design Wireless Sensor Network systems that support applications with intensive monitoring and control activities. This application class often has significant data collection and processing requirements, requiring increased levels of energy consumption as compared to other WSN applications. Further, many deeply embedded WSN systems with these data collection and processing requirements are expected to operate without manual battery recharging for several decades, and therefore require energy harvesting techniques. For this class of systems, there are currently few effective approaches that balance careful energy management with high performance communication and computation requirements. My dissertation addresses the above problem. Specifically, I propose a set of algorithms and control methods for energy management in performance-sensitive WSN systems, and harvesting-aware rate allocation for application utility maximization. First I formally define the problem of energy harvesting-aware energy management as two optimization problems, one for individual sensor nodes and another for multi-hop sensor networks. I propose energy management algorithm to solve both problems optimally and efficiently. These solutions combine two energy saving techniques, Dynamic Voltage Scaling (DVS), and Dynamic Modulation Scaling (DMS), alongside with energy harvesting techniques. I then address a harvesting aware rate allocation problem with the objective of utility maximization. The problem is solved with an optimal centralized algorithm and a distributed algorithm. I conducted extensive simulation-based experiments to evaluate the effectiveness of my proposed algorithms. Specifically I developed simulation software using TOSSIM, the standard WSN simulator, and EPANET, a public domain, water distribution system modeling program. This software simulates in high fidelity the computation and communication activities of WSN nodes, and considers a variety of network setups, energy harvesting profiles (solar and water), and application scenarios, etc. My algorithms are implemented within this simulation environment and compared against a series of rival algorithms under various experimental setups. Extensive simulation results demonstrate the significant advantage of my algorithms over the rival algorithms.

Computer Science Colloquium: Searching in the "Real World"

Wednesday, April 18, 2012
1:30-2:30pm
ENGR 4201
Ophir Frieder

Abstract

For many, "searching" is considered a mostly solved problem. In fact, for text processing, this belief is factually based. The problem is that most "real world" search applications involve "complex documents", and such applications are far from solved. Complex documents, or less formally, "real world documents", comprise of a mixture of images, text, signatures, tables, etc, and are often available only in scanned hardcopy formats. Search systems for such document collections are currently unavailable.

We describe our efforts at building a complex document information processing prototype. This prototype integrates "point solution" (mature) technologies, such as document readability enhancement, OCR capability, signature matching and handwritten word spotting techniques, search and mining approaches, among others, to yield a system capable of searching "real world documents". The described prototype demonstrates the adage that "the whole is greater than the sum of its parts". Our complex document benchmark development efforts are likewise presented.

Having described the global approach, we describe some point solutions which we developed over the years. These include an image enhancer, an Arabic stemmer, and a natural language source integration fabric called the Intranet Mediator.

Speaker's Bio

Ophir Frieder is the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Chair in Computer Science and Information Processing and is Chair of the Department of Computer Science at Georgetown University. He is a Fellow of the AAAS, ACM, and IEEE.

SWE Seminar: Using Mutants to Detect and Locate Bugs

Monday, April 23, 2012
12:00pm
ENGR 3507
Mike Papadakis

Abstract

One of the most important challenges in software development is the detection and correction of software faults. Software testing and debugging techniques form the current practice for identifying, locating and fixing software defects. Both testing and debugging activities are among the most tedious tasks of software development which are usually performed by hand. Therefore, substantial benefits can be gained by the full or partial automation of these activities. Towards this direction, this talk will present emergent results of the use of mutation analysis in automating both testing and debugging. More precisely, automated techniques with respect to a) test case generation, b) test evaluation and c) fault localization will be presented in this talk.

Speaker's Bio

Mike Papadakis is a research associate at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) of Luxembourg University. He received his B.Sc., M.Sc. and Ph.D. degrees in Computer Science from the Athens University of Economics and Business (Greece). His research interests include software quality assurance and in particular, software testing, software debugging and mutation testing.

GRAND Seminar: Serious Games

Tuesday, April 24, 2012
12:00pm
ENGR 4201
Len Annetta

Abstract

This research presentation will illustrate how Dr. Annetta began studying Serious Games and the funded research he has been awarded throughout his career. His work in scientific visualization, with a specific focus on spatial visualization and mental rotation, will be highlighted from new data on high school students in the National Science Foundation funded GRADUATE project. Finally, this talk will communicate Dr. Annetta’s vision for how Serious Games can bridge a variety of disciplines.

Speaker's Bio

An associate professor of Science Education at George Mason University, Dr. Annetta’s research has focused on distance learning and the effect of instructional technology on science learning of teachers and students in underserved populations. Understanding the popularity of online, multiuser video game play, Dr. Annetta has been awarded over $5 million in grants to support his work on distance learning and the use of Serious Educational Games as a vehicle for learning STEM content and STEM career awareness. In 2008, he was honored with three awards for his extension work teaching K-12 teachers and students’ video game design and creation. These awards were progressive from the College of Education Outstanding Extension Service Award, to the induction into the NC State University Academy of Outstanding Faculty Engaged in Extension to the Distinguished Alumni Engaged in Extension and Outreach award. Moreover, Dr. Annetta has twice been awarded the National Technology Leadership Initiative Fellowship in Science Education and Technology from the Association of Science Teacher Education and the Society for Information Technology and Teacher Education.

Oral Defense of Doctoral Dissertation: A Self-managed Healthcare Emergency Department System

Friday, April 27, 2012
10:00am -12:00pm
ENGR 4801
Serene Al-Momen

Abstract

The delivery of cost-effective and quality Emergency Department (ED) services remains an important and ongoing challenge for the healthcare industry. ED overcrowding has become a common problem in hospitals around the world, threatening the safety of patients who rely on timely emergency treatment. Despite numerous advances in medical procedures and technologies, EDs continue to experience overcrowding problems. The combination of increased demand and diminished resources makes optimizing emergency departments a difficult problem for healthcare decision makers. We address this problem by applying an autonomic computing framework to self-managed emergency departments in order to maintain optimal Quality of Service (QoS) during its operation. This work has potential implications in guiding a hospital's effort to optimize their emergency department system while at the same time meeting cost constraints.

Improving the operational efficiency of an ED is a complex task due to the very large number of ED configurations that involve human and physical resources and due to the unpredictable nature of the ED's workload. Thus, managing the performance of EDs becomes difficult and expensive when carried out by human beings alone. The approach presented in this dissertation for a self-managed ED (SMED) consists in building into the ED the mechanisms required to self-adjust the ED's configuration parameters so that its QoS is constantly met. The design of an autonomic controller for the SMED and the evaluation of its effectiveness in optimizing QoS subject to cost constraints is described in this dissertation. The controller uses a combination of combinatorial search techniques with simulation models. Utility functions are used to represent stakeholder policies, which normally consist of multiple QoS metrics with competing priorities. Experimental results illustrate the operation of the controller and how it reacts to variations of patient interarrival times under various cost constraints.

The evaluation process consisted in analyzing the results obtained with the SMED on a hypothetical ED as well as on a real ED model. In both cases, it is shown that the SMED was able to find the near optimal configuration that optimizes QoS goals within the cost constraint. The results also show how the SMED can be useful in adjusting staffing policies and determining resource factors that play a major role in impacting ED QoS metrics.

Computer Science Colloquium: New Knowledge Discovery Techniques to Support Intelligence Analysts

Monday, April 30, 2012
11:00am-12:00pm
Research Hall, Room 163
Naren Ramakrishnan

Abstract

Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data, and rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. This talk will focus on “storytelling”, the investigative process by which analysts aim to “connect the dots” between seemingly disconnected information. We will introduce how the twin notions of redescriptions and biclusters form compositional building blocks of stories, and how efficient and effective algorithms for storytelling can be designed. In addition, we describe approaches to inject user feedback into the story construction process and the results of user studies demonstrating how participants are adept at using these notions to solve intelligence analysis tasks. Experimental results on large textual and multi-relational corpora will be described. This talk will conclude with a preview into an ongoing project in the area of automatic alert generation from public source data.

Speaker's Bio

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering and the Associate Head for Graduate Studies in the Department of Computer Science at Virginia Tech. At Virginia Tech, he directs the Discovery Analytics Center, a university-wide effort that brings together researchers from computer science, statistics, mathematics, and electrical and computer engineering to tackle knowledge discovery problems in important areas of national interest, including intelligence analysis, sustainability, and health informatics. Ramakrishnan serves on the editorial boards of IEEE Computer, Data Mining and Knowledge Discovery, and many other journals. He is an ACM Distinguished Scientist and was named to two "40 under 40" lists: Computerworld's innovative IT people to watch (2007) and Purdue University's list of distinguished alumni (2010).

Oral Defense of Doctoral Dissertation: Regression Learning in Decision Guidance Systems: Models, Languages and Algorithms

Monday, April 30, 2012
3:00-5:00
ENGR 2901
Juan (Judy) Luo

Abstract

The state-of-art research in the decision guidance applications is trying to build complex systems with predicting capability. This dissertation focuses on a framework, models, languages, and algorithms to integrate the machine learning functionality (regression learning) into DGMS applications as their first class citizen.

A framework CoReJava (Constraint Optimization Regression in Java), which extends the Java programming language with regression learning – the ability of parameter estimation for a function, is proposed and developed. CoReJava is unique in that functional forms for regression analysis are expressed as first class citizens, i.e., as Java programs, in which some parameters are not given in advance, but will be learned from learning data sets provided as input. The if-then-else decision structures of Java language are naturally adopted to represent piecewise functional forms of regression. Thus, minimization of the sum of squared errors involves an optimization problem with a search space that is exponential to the size of learning set. A combinatorial restructuring algorithm is proposed to guarantee learning optimality and furthermore reduce the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds. A Heaviside restructuring algorithm, which expresses the piecewise linear regression function using a unified functional format, instead of multiple pieces, is proposed to decrease the searching complexity further to be polynomial in both the size of learning set and the number of piece-wise bounds, while the learning outcome will be an approximation of the optimality.

A multi-step Expectation Maximization based (EM-based) algorithm (EMMPSR) is proposed to solve piecewise surface regression problem. The multiple steps involved are local regression on each data point of the training data set and a small set of its closest neighbors, clustering on the feature vector space formed from the local regression, regression learning for each individual surface, and classification to determine the boundaries for each individual surface. An EM-based iteration process is introduced in the regression learning phase to improve the learning outcome. The reassignment of a cluster identifier for every data point in the training set is determined by predictive performance of each submodel. Clustering quality validity indices are applied to the scenario in which the number of piecewise surfaces is not given in advance.

The Relational Database Management System (RDBMS) is extended with the piecewise regression learning capability as well. The functional forms are represented as database tables. The EMMPSR algorithm is implemented as stored procedures. A case study is undertaken to describe the decision optimization process based on the learning outcome of the multi-step Expectation Maximization based (EM-based) algorithm.

Evaluation of the resulting research is established by experiments and empirical analysis in comparison with those of related regression learning packages.

GRAND Seminar: Improving Drug Development by Connecting Medicinal Chemistry with Drug Repositioning and Modern Machine Learning Methods

Tuesday, May 01, 2012
12:00pm
ENGR 4201
Iwona Weidlich

Abstract

Developing drug candidates from scratch has turned into a billion-dollar expense that is not delivering enough profitable products to market. Novel approaches which merge chemistry with biology and informatics contribute to the development of selective lifesaving drugs needed by patients. We implement machine learning classifiers for HTS Data Analysis, Screening and drug repurposing with high probability of selecting drug candidates eligible for Phase II of clinical study free from ADME/Tox-related problems. We used small molecule bioactivity data for HCV RNA Polymerase to train and test QSAR models and apply these robust models for compound ranking and hit identification in drug repositioning techniques. Random Forest and kNN algorithms were used with Morgan fingerprints of 679 small molecules with curated IC50 values. After filtering various drug-like databases (DrugBank, MDL, NIAID-NIH, ComGenex) compounds were selected and tested against HCV. We discuss the challenges in drug repositioning faced in academia, government and pharmaceutical industry.

Speaker's Bio

Dr. Weidlich received her Ph.D. in Pharmaceutical Sciences from the University of Medical Sciences, Poznan, Poland in 2005. Her Ph.D. research focused on developing anti-cancer agents that are designed to be activated only inside a cancerous cell but have benign form in the systemic circulation. Her research interests also include using very large collections of chemical databases to filter and extract relevant subsets of molecules for closer analysis, and performing physicochemical and ADME/Tox property predictions. Specifically, she is interested in designing and evaluation of novel anti-cancer agents. She joined the Computer-Aided Drug Design (CADD) group at the Chemical Biology Laboratory, National Cancer Institute in Frederick, NIH as a postdoctoral fellow in October 2005. Dr. Weidlich has been conducting in silico screening for the inhibitors of cancer DNA, specifically tyrosyl-DNA phosphodiesterase (Tdp1) and Shc Src homology 2 (SH2) domain. She designed new, more powerful Tdp1 inhibitors and Shc SH2 domain-binding inhibitors: tetramer peptide-peptoid hybrids exhibiting up to 40-fold increase in affinity.

She accepted a faculty position at the University of Maryland Baltimore County (UMBC) in October 2010, remaining affiliated with NCI/NIH as a Guest Researcher. At UMBC she employs virtual screening to identify novel allosteric inhibitors of the Hepatitis C Virus NS5B Polymerase and also seeks to understand the mechanism by which small molecules inhibit NS5B.

Her current research is also related to combining robust QSAR modeling with drug repositioning and systems biology. This project of hers focuses on resolving problems arising from the management and analysis of huge amount of biological data as well as detailed study of multi-domain proteins and their function. Dr. Weidlich proposes techniques which could result in successful drug selection, and re-investigation of existing drugs for new therapeutic indications.

GRAND Seminar: Robotic Planning with Limited Sensing

Friday, May 04, 2012
11:00am
ENGR 4201
Jason O'Kane

Abstract

The usefulness of a mobile robot is limited by its ability to sense and interact with its environment. However, because information from sensors is limited and sometimes unreliable, robots are often confronted with substantial and difficult-to-resolve uncertainty about the state of the world. This talk will present two lines of research that make progress toward autonomy in spite of such uncertainty. First, I will describe new methods for localization and navigation that allow mobile robots with limited sensing capabilities and noisy actuators to move through their environments in provably reliable ways. Second, I will discuss target tracking applications in which a robot or team of robots seeks to locate and follow moving targets, under several different sensing and motion constraints. The overall theme is that many important tasks in robotics require surprisingly little sensing.

Speaker's Bio

Jason O'Kane is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina. He earned Ph.D. (2007) and M.S. (2005) degrees from the University of Illinois and the B.S. (2001) degree from Taylor University, all in Computer Science. He received an NSF CAREER award in 2010, and is a member of the DARPA Computer Science Study Panel. His research spans algorithmic robotics, planning under uncertainty, and computational geometry.

CS Seminar: A Design Theory for IT Supporting Online Communities

Monday, May 14, 2012
12:00-1:00pm
ENGR 4201
Paolo Spagnoletti

Abstract

Community-centered development methods provide practical guidelines for the design of software environments that effectively support the social interaction of community members. They focus on usability and sociability as general requirements to be addressed through a combination of IT modules and managerial policies. In this talk new design principles and evaluation methods for exploiting the potential of sustainable communities will be introduced. This issue is addressed by presenting a design theory for IT supporting Online Communities (ITsOC) based on transaction costs theory and on complex systems theory. The ITsOC theory is then instantiated in the context of a European project for the development of an intelligent multimedia platform providing innovative social e-services for elderly persons and their social entourage.

Speaker's Bio

Paolo Spagnoletti is assistant professor of Information Systems at LUISS Guido Carli University in Rome (Italy) where he is director of the Master in e-business (www.luiss.it/meb). Since 2011 he coordinates the Research Center on Information Systems (CeRSI) of LUISS. He holds a Ph.D. degree in Information Systems from LUISS University (2007), a Master degree in Business Engineering from Tor Vergata University (2003) and a M.Sc. degree in Electronics Engineering from La Sapienza University (2001). His research interests are related to the social studies of Information Technologies and the design of innovative solutions for transforming organizations: IT supporting online communities, e-health, IT governance, IS security management.

Department of Computer Science: Graduation Celebration and Awards Dinner

Wednesday, May 16, 2012
6:00pm
JC Dewberry Hall
By Invitation Only

Volgenau School of Engineering: Convocation

Thursday, May 17, 2012
2:00pm
Patriot Center

Event Info

IN CASE OF RAIN students should report directly to the Patriot Center floor, via the lower entrance on the "East" side of the Patriot Center (i.e. loading dock door), to be directed to their seats. There will be no procession in the event of rain.

Graduates traditionally assemble at 1:15 p.m. in the Patriot Center parking lot at your department's designated area (indicated by department signs). It is very important that you be at the appropriate location by 1:15 p.m. so we can line up all graduates by degree program for the processional.

At 1:30 p.m., you will process to reserved seating in front of the stage. A brief convocation address will be given by the designated convocation speaker. Following this, the graduates will be called by name to come forth onto the stage to receive their diplomas. Name cards distributed in the parking lot will be handed to the deans who will read the name of each graduate as they cross the stage. Finally, there will be a reception on the Concourse of the Patriot Center for all in attendance.

The anticipated schedule of events is as follows: 1:15pm Processional Preparation 1:30pm Processional 2:00pm Call to Order and Welcome 2:15pm Commencement Address 2:30pm Presentation of Diplomas 3:30pm Reception

There will be no rehearsal, and no tickets are required for this event. You are welcome to bring as many guests as you like. All that you need to do is be sure that you are in the Patriot Center parking lot in the area designated for your department by 1:15 p.m. and that your guests are in the Patriot Center before 2:00 p.m. on the day of the ceremony. Please be sure to wear your cap and gown. Parking for this event will be in Parking Lot A.

We look forward to seeing you and your guests at Volgenau School of Engineering Collegial Convocation!

Here are some easy steps to make sure you receive all the information about Convocation and Commencement:

* Obtain a degree audit. In order to graduate, you will need to insure that you complete the academic requirements necessary. If you expect to graduate in the summer, you are allowed to walk in the May Convocation and Commencement Ceremonies pending approval from your department. The simplest way to do this is to obtain a degree audit from the Office of the Registrar. Check the audit carefully and if you have questions, see your advisor. If you have not completed all your requirements, the degree audit will indicate this. * Register intent to graduate. Students must initiate the graduation process by registering an "intent" online with the Office of the Registrar. You will not be reminded, by mail or otherwise, to do this. Click here for the Register's timeline for May 2012 graduation. * Receive Departmental Approval. Students must check with their individual departments on the process necessary to complete their approval to graduate. It is your responsibility to check with your department’s office AND your advisor to ensure that you have completed all of the requirements necessary to graduate.

* Caps, gowns, announcements and commencement tickets can be picked up in the University Bookstore in the Johnson Center. Faculty that need to rent regalia need to come to the bookstore and place an order before April 1st. Any orders received after April 1st will be charged a $20 late fee in order to cover the cost of express shipping. For further information call the bookstore at 703-993-8170.

Please contact the Office of the Registrar, (703) 993-2431, for any change of address and to check your degree application status (Master's and Doctoral) if you are unsure of your graduation status.

We look forward to seeing you and your guests at Volgenau School of Engineering Collegial Convocation!

For information about Commencement ceremonies, please visit the Office of Events Management Commencement Website.

Have more questions? Please contact Joyce Rose via phone at 703-993-1500.

Department of Computer Science: Post-Convocation Reception

Thursday, May 17, 2012
3:30pm
Atrium, Engineering Building

Event Info

Immediately following the Volgenau School of Engineering Convocation Ceremony. All graduates from the Computer Science Department and their guests are welcome.

Volgenau School of Engineering: New Graduate Student Orientation

Wednesday, August 22, 2012
6:00-8:00pm
Enterprise Hall, Room 80

Event Info

For the sixth year, Volgenau School and Engineering is welcoming its newly admitted graduate students to a special orientation event.

Although orientation is not mandatory, it is highly recommended that both domestic and international students plan to attend. Essential information regarding university services for graduate students and program information from academic departments will be provided. Also, this is your opportunity to meet your peers, the administrative and academic staff members who will assist you during the pursuit of your graduate course work and degree.

Please RSVP online for orientation by visiting: http://volgenau.gmu.edu/graduateresearch/responseform/