Microsoft's Global View of the Security and Malware Ecosystem
Presenter: Joe Faulhaber
Abstract: The Microsoft Security Intelligence Report (SIR) analyzes the threat landscape of exploits, vulnerabilities, and malware using data from Internet services and over 600 million computers worldwide. Threat awareness can help you protect your organization, software, and people.
Bio: Joe graduated from MSU with a degree in CS in 1996, and started working at Microsoft in 1998, and helped ship the first two versions of the Sharepoint product. After that, he did security-related projects inside the company that ended up coalescing around Microsoft Antimalware Protection technologies and the creation of the Microsoft Malware Protection Center in 2008. Since then, he has led telemetry-gathering efforts in the MMPC, and is working in the business intelligence team, sorting through a quarter billion rows of data collected daily.
Date: Monday, Dec 2 2013 - 4:10 pm
Location: EPS 108
MITATE: Mobile Internet Testbed for Application Traffic Experimentation
Presenter: Utkarsh Goel
Abstract: MITATE is first-of-its-kind large-scale mobile application prototyping platform that will allow experimentation with custom mobile application traffic between mobile devices and cloud infrastructure endpoints. MITATE will enable developers to evaluate protocol design choices, application deployment alternatives, and component mobility mechanisms - all in live mobile networks spanning geographic areas, carriers, and devices. In this talk Utkarsh will showcase MITATE functionality and design. He will also present some preliminary measurement made using a prototype of the system.
Date: Monday, Nov 25 2013 - 4:10 pm
Location: EPS 108
Knowing Your NGS Upstream: Alignment and Variants
Presenter: Gabe F. Rudy
Abstract: Next Generation Sequencing technology has made it affordable and commoditized to sequence genes, exomes and genomes for individual diagnostic or research purposes. The cheap and plentiful generation of data from sequencing quickly becomes a informatics problem to process that data to be of use for a clinician or researcher. With the finishing of the human reference genomes, we have a common coordinate system in which to compare an individual’s genome and find differences that we call variants or mutations. Most of these are benign or of low functional consequence, but a single "letter" substitution in an important gene can be the cause of a sever disease. In this talk, I introduce the algorithmic and data challenges of making NGS genomic data accessible. We will deep dive into some of the algorithmic solutions from a computer science perspective and discuss the remaining challenges that are both bioinformatic and basic science oriented to enable easy interpretation of genomes for personal, clinical and research purposes.
Bio: Gabe is a 10-year veteran at Golden Helix and spends his days collaborating with a diverse set of scientists and building solutions to enable their research. He earned his Masters in Computer Science from the University of Utah before setting his sights on the fast-changing field of genomics and bioinformatics. Gabe has been involved in developing various algorithms from copy number segmentation to runs of homozygosity and rare variant association testing. Gabe blogs about the genomics field from the perspective of someone building solutions and curating genomic annotations and public databases. His series "A Hitchhiker’s Guide to Next Generation Sequencing" has become quite popular as a starter guide for those entering the field.
Date: Monday, Nov 18 2013 - 4:10 pm
Location: EPS 108
Mobile Computing in a Connected World
Presenter: Dr. Yung-Hsiang Lu
Abstract: Since the first laptop and the first cellular phone in early 1980s, mobile computing has made significant progress and fundamentally changed everyone's life. This seminar will examine the trends of mobile computing. Mobile computers have many limitations, such as weight, size, and energy. Many solutions have been developed to extend the operational time of mobile computers. Some solutions integrate the convenience of mobile computers and the nearly unlimited resources in cloud servers for heavy computation, such as image processing. This seminar will describe some of these solutions and explain this integration will accelerate. The seminar will then describe the speaker's current projects that bring image processing capabilities to mobile users.
Bio: Yung-Hsiang Lu is an associate professor in the School of Electrical and Computer Engineering at Purdue University. His research topics include mobile computing, image processing, wireless sensor networks, and autonomous robots. He is a member of the ACM Distinguished Speakers Program (2013-2016). In 2011, he was a visiting associate professor in the Department of Computer Science at the National University of Singapore. In 2008, he was one of the three recipients of Purdue's Class of 1922 Helping Student Learn Award. In 2004, he obtained a career award from the National Science Foundation for studying energy conservation by operating systems. He is a senior member of the IEEE and the ACM. He is an associate editor of ACM Transactions on Embedded Computing Systems and ACM Transactions on Design Automation of Electronic Systems. He was a past chair of the Green Multimedia Interest Group in the IEEE Multimedia Communication Technical Committee and a past vice-chair of the Low Power Technical Committee in ACM SIGDA. He has served in the program committees of dozens of conferences, symposia, and workshops. He received the Ph.D. degree from the Department of Electrical Engineering at Stanford University and BSEE from National Taiwan University.
Date: Monday, Nov 4 2013 - 4:10 pm
Location: EPS 108
A Poisson-Lognormal Conditional-Autoregressive Model for Multivariate Spatial Analysis of Pedestrian Crash Counts across Neighborhoods
Presenter: Yiyi Wang
Abstract: In this talk, I will discuss about a spatial count model for analyzing 3-year pedestrian crash counts across neighborhoods in Austin, Texas, while controlling for various land use, network, and demographic attributes (e.g., land use balance, residents’ access to commercial land uses, sidewalk density, lane-mile densities [by roadway class], and population and employment densities [by type]). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference.
Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (like lighting conditions and local sight obstructions), along with spatially-lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with greater pedestrian crash rates across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates.
Bio: Yiyi Wang is an assistant professor in the Civil Engineering Department at Montana State University. Her research focuses on applying (advanced) spatial statistical methods to analyze transportation-related data (e.g., lane use, travel behavior, vehicle ownership, and traffic crashes). She is also researching innovative methods to estimate complex models while maintaining computational efficiency. She has published peer-reviewed articles in the Accident Analysis & Prevention, the Journal of Transport Geography, the Journal of Transportation and Land Use, and the Transportation Research Record. Honors and awards include UT Austin’s Robert Herman Endowed Scholarship in 2012 and the Helene M. Overly Memorial Scholarship issued by the Women’s Transportation Seminar, Heart of Texas, in 2011.
Date: Monday, Oct 28 2013 - 4:10 pm
Location: EPS 108
Assessment of Multi-Hop Interpersonal Trust in Social Networks by Three-Valued Subjective Logic
Presenter: Guangchi Liu
Abstract: Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSNs topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncertainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs.
Date: Monday, Oct 21 2013 - 4:10 pm
Location: EPS 108
Design Methods in Software Engineering
Presenter: Kaznin Alexey
Abstract: The first part of presentation will be devoted to Northern (Arctic) Federal University (NArFU) which is located in the North-West of Russia. The second part of the presentation will include general information about research in the field of software engineering (especially design stage) held at NArFU. In the third part of the presentation Dr. Alexey Kaznin will talk about his research. He developed method of systems modelling via Polychromatic Sets and Polychromatic Graphs approach and implemented this method in design stage of software engineering.
Bio: Dr. Alexey Kaznin is an Associate Professor at Institute of Mathematics, Information and Space Technologies of Northern (Arctic) Federal University named after M.V. Lomonosov in Arkhangelsk, Russia. He received Ph.D. in technical sciences from the Moscow State Technological University “STANKIN” in 2010. His research interests include the design information systems within software engineering and also development and implementation of new approaches of information systems design.
Date: Monday, Oct 7 2013 - 4:10 pm
Location: EPS 108
Extending Inference in Continuous Time Bayesian Networks
Presenter: Liessman Sturlaugson
Abstract: The continuous time Bayesian network (CTBN) has been defined to enable reasoning about complex systems in continuous time by representing the system as a factored, finite-state, continuous-time Markov process. As the CTBN is a relatively new model, many extensions that have been defined and researched with respect to static Bayesian networks have not yet been extended to CTBNs. This proposal intends to address some of these. First, we intend to formally prove several complexity results with respect to CTBNs. Specifically, it is known that exact inference in CTBN is NP-Hard due to the use of a Bayesian network to set the nodes' initial states. However, we propose to prove that exact inference in CTBNs is still NP-Hard even when the initial states are fully observed. Furthermore, we suspect and intend to prove that approximate inference in CTBNs, as with static Bayesian networks, is also NP-Hard. Second, we propose to formalize both uncertain and negative evidence in the context of CTBNs and extend existing inference algorithms to be able to support these new types of evidence. Third, we show how methods for sensitivity analysis of Markov processes can be applied to the CTBN while exploiting the conditional independence structure of the network. This is done through what we call "node isolation," which approximates a nodes' unconditional intensity matrix, analogous to marginalization in a static Bayesian network. Lastly, we intend to research how and when the node isolation process might be used in approximate inference to increase efficiency without significantly decreasing accuracy. This presentation will review preliminary progress on these goals and outline the direction of future research for the completion of this doctoral research.
Date: Monday, Oct 7 2013 - 8:00 am
Location: CS Conference Room
Design Pattern Decay: An Extended Taxonomy and Empirical Study of Grime and its Impact on Design Pattern Evolution
Presenter: Isaac Griffith
Abstract: Design patterns are well known solutions to common problems and are extensively utilized in software development. Yet, little empirical work has been conducted to evaluate or validate the consequences that poor design decisions have on pattern realizations. This paper describes a research program to further the understanding of design pattern evolution. Specifically, we focus on design pattern decay by studying how grime, a decisively negative consequence of software evolution occurs. The research proposed herein furthers the exploration of design pattern decay by providing empirical evidence of grime buildup, a new grime taxonomy, and the consequences exhibited through decreased adaptability and maintainability in actual realizations of patterns in code. These notions will be supported through the development of semi-automated grime detection and refactoring research tools that will also link to existing forms of design decay such as code smells, anti-patterns, and modularity violations. An extension of this research focuses on the exploration of these notions inlying coupled pattern realizations.
Date: Monday, Sep 30 2013 - 4:10 pm
Location: EPS 108
Optimal and Approximation Algorithms for Non-Preemptive Power Scheduling
Presenter: Sean Yaw
Abstract: Smart grid technology has the opportunity to revolutionize our control over power consumption. Currently power-requesting jobs are scheduled in an on-demand fashion; power draw begins when the consumer requests power (turns on an appliance) and ends when the job is complete (appliance is tuned off). Often such jobs may have some flexibility in their starting times (e.g. a dishwasher or electric vehicle charger). We consider the problem scheduling power jobs so as to minimize peak demand. We first consider a general version of the problem in which the job intervals can be staggered. While the problem is known to be NP-hard (we show it is even NP-hard to approximate), we provide an optimal algorithm based on dynamic programming that is fixed-parameter tractable (FPT). For some important special cases we provide new constant-factor approximation algorithms that improve on previous results. Extensive simulation results show that our algorithms improve on existing methods.
This talk is a part of Sean's qualification examination.
Date: Monday, Sep 23 2013 - 4:10 pm
Location: EPS 108
Imaging Seismic Tomography in Sensor Networks
Abstract: Existing volcano instrumentation and monitoring systems do not yet have the capability to recover physical dynamics with sufficient resolution. At present, raw seismic data are typically collected at central observatories for post processing and tomographic imaging. However, the real-time data collection from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. Thus neither real-time nor high-resoultion tomography imaging are possible today thus limite our understanding of volcano dynamics. Our NSF-funded VolcanoSRI project is developing a VolcanoSRI (Volcano Seismic Realtime Imaging) system, a large-scale sensor network of low-cost geophysical stations, that sense and analyze seismic signals, and compute real-time, three-dimensional fluid dynamics of a volcano conduit system (e.g., 4D volcano tomography) within the sensor network. Realizing such a VolcanoSRI system requires a transformative approach to tomography computation algorithm, collaborative signal processing, and the associated sensor network design. In this talk, we present our recent research on distributed tomography algorithms that process data and invert volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs realtime tomography inversion under the constraints of network resources.
Bio: Dr. WenZhan Song is an Associate Professor of Computer Science and Diector of Sensorweb Research Laboratory at Georgia State University. His research mainly focuses on sensor web, smart grid and smart environment where sensing, computing, communication and control play a critical role and need a transformative study. He has received $6 million+ research funding from NSF, NASA, USGS, Boeing and etc since 2005. Dr. Song is a recipient of Outstanding Research Contribution Award (2012) in GSU Computer Science, Chancellor Research Excellence Award (2010) in WSU Vancouver and NSF CAREER Award (2010). His research has been featured in MIT Technology Review, Network World, Scientific America, New Scientist, National Geographic, etc. During his PhD study, he was also a recipient of 2004 National Outstanding Oversea Student Scholarship, awarded by Ministry of Education of China (only 40 awarded in USA). Before that, he was a software engineer and team leader in Alcatel-Lucent Shanghai Bell. Dr. Song serves the editorial board of several premium journals including IEEE Transaction on Parallel and Distributed Systems.
Date: Friday, Sep 6 2013 - 4:10 pm
Location: Robert Hall 113
Description: To help kick off Fall Semester, the department will hold a mandatory (attendance will be taken) Welcome Seminar on Aug 26th in EPS 108 that begins at 4:10 p.m. You will have a chance to meet your peers, the faculty and the staff. In addition, we will share general information and news that concerns the department. You will also have the opportunity to ask questions. Light refreshments will be served.
Date: Monday, Aug 26 2013 - 4:10 pm
Location: EPS 108
Fuzzy Bayesian Networks for Prognostics and Health Management
Description: MS Project Seminar for Comprehensive
Presenter: Nicholas F. Ryhajlo
Abstract: In systems diagnostics it is often dicult to dene test requirements and acceptance thresholds for these tests. A technique that can be used to alleviate this problem is to use fuzzy membership values to represent the degree of membership of a particular test outcome. Bayesian networks are commonly used tools for diagnostics and prognostics; however, they do not accept inputs of fuzzy values. To remedy this we present a novel application of fuzzy Bayesian networks in the context of prognostics and health management. These fuzzy Bayesian networks can use fuzzy values as evidence and can produce fuzzy membership values for diagnoses that can be used to represent component level degradation within a system. We developed a novel execution ordering algorithm used in evaluating the fuzzy Bayesian networks, as well as a method for integrating fuzzy evidence with inferred fuzzy state information. We use three dierent diagnostic networks to illustrate the feasibility of fuzzy Bayesian networks in the context of prognostics. We are able to use this technique to determine battery capacity degradation as well as component degradation in two test circuits.
Date: Thursday, Jul 11 2013 - 1:00 pm
Location: CS Conference Room
Process-oriented Information Logistics
Presenter: Prof. Bela Mutschler from University of Applied Sciences Ravensburg-Weingarten (Germany)
Abstract: A continuously increasing amount of data makes it difficult for knowledge-workers to identify the information they need to perform their tasks in the best possible way. Particularly challenging in this context is the alignment of process-related information (e.g., working instructions, best practices) with business processes. In fact, process-related information (process information for short) and business processes are usually handled separately. On one hand, shared drives, databases, and information systems are used to manage process information, on the other hand, process management technology provides the basis for managing business processes. In practice, enterprises often establish (Intranet) portals to connect both perspectives. However, such conventional portals are not always sufficient. Reason is that process information is delivered without considering the process participants’ work context and business processes are usually presented in a rather static manner. Therefore, enterprises crave for new ways of making process information available in a user-adequate manner. This talk picks up this challenge and presents the niPRO framework, which is based on semantic technology, enabling the intelligent delivery and user-adequate visualization of process information.
Bio: Bela Mutschler holds a Diploma and a PhD in Computer Science. Since November 2008 he has been appointed as full professor at the University of Applied Sciences Ravensburg-Weingarten in Weingarten, Germany (Business Informatics Group, Faculty of Computer Science and Electrical Engineering). His main research interests include the design and introduction of process-oriented information systems, advanced process management technologies, and process-oriented knowledge management. Bela serves on the program committees of various international academic conferences and workshops.
Date: Monday, Apr 22 2013 - 4:10 pm
Location: EPS 108
Distributed Estimation in Dynamic Networks
Presenter: Dr. Stacy Patterson
Abstract: Distributed estimation algorithms generate an estimate of a global network state using only local interactions. In large networks with a vast wealth of data, it is important that these algorithms capture relevant information in a compact form, and that they are robust to network dynamics.
In the first part of this talk, I will focus on the effects of network dynamics on a well-known distributed estimation problem, the distributed average consensus problem. Distributed average consensus algorithms have a wide variety of applications, including sensor fusion, distributed optimization, and autonomous vehicle formation control. I will present our recent work on the stability and robustness of consensus algorithms in dynamic networks and show the analytical relationship between algorithm performance and the network size, topology, and dynamic characteristics. A notable result is that network topology imposes fundamental limitations on the scalability of distributed consensus algorithms that cannot be overcome without global information.
In the second part of the talk, I will address a more sophisticated approach to distributed estimation based on compressed sensing. Recent works have demonstrated that compressed sensing is applicable to a variety of problems in sensor networks, including urban environment monitoring and traffic estimation. I will show how we leveraged work in the database literature to develop a distributed compressed sensing algorithm that outperforms previous approaches by several orders of magnitude in both time and message complexity, making it an efficient solution for resource-challenged networks. I will then connect this algorithm with distributed average consensus by showing how we combine the two algorithms to obtain an efficient distributed compressed sensing algorithm suitable for dynamic networks.
Bio: Stacy Patterson is a Technion Postdoctoral Fellow and a Viterbi Postdoctoral Fellow in the Department of Electrical Engineering at Technion – Israel Institute of Technology. She received her M.S. and Ph.D. in Computer Science from the University of California, Santa Barbara in 2003 and 2009, respectively. From 2009 to 2011, she was a postdoctoral scholar in the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. Her research interests are in distributed algorithms and applications for aggregation, estimation, and control in dynamic networks.
Date: Friday, Apr 12 2013 - 4:10 pm
Location: Roberts 113
Cascading Impact of Lag on User Experience in Multiplayer Games
Presenter: Eben Howard
Abstract: People who play online games want those games to be fun. Current lag mitigation techniques focus on the impact of lag only for the lagged player. We show that unlagged players are also impacted by being in a group with a lagged player. Without taking this cascading impact into account, network and application designers are not able to make fully informed systematic decisions
This talk is a part of Eben's comprehensive examination.
Date: Monday, Apr 8 2013 - 4:10 pm
Technical Debt Management: From Automated Refactoring to Decision Support and Beyond
Presenter: Isaac Griffith
Abstract: Technical debt is a metaphor describing the economic consequesnces associated with taking short cuts to satisfy the short term goals of a project (i.e., meeting a release deadline) at the expense of long term goals (i.e., refactored software that meets quality criteria). Technical debt management is essentially the identification, measurement, and decision analysis surrounding the remediation and acceptance of technical debt in a project. In this presentation I describe previous research conducted in the areas of automated refactoring and code smell detection in the context of managing technical debt. Further, I describe current research specifically dealing with the management decisions involving release planning in the context of technical debt managment. In conclusion, I will show how this previous research has chartered an initial research path towards my dissertation in design pattern evolution and design pattern grime.
Date: Monday, Mar 25 2013 - 4:10 pm
Location: EPS 108
Search and Machine Learning @ Oracle RightNow
Presenter: Rajesh Kommineni
Abstract: In this seminar, Rajesh will provide a high level overview of how Search and Machine Learning is currently being used at Oracle RightNow. The talk will include description of problems encountered by Artificial Intelligence applications in a typical industrial usage.
Bio: Rajesh Kommineni works as a Development Manager at Oracle RightNow. Rajesh has been working in the area of Knowledge Systems, Search and Machine Learning for about 13 years at various companies.
Date: Monday, Mar 18 2013 - 4:10 pm
Systems for Improving Internet Availability and Performance
Presenter: Ethan Katz-Bassett
Abstract: The Internet is now central to many aspects of modern society, yet it remains remarkably fragile. Partial outages are common, and performance problems are widespread. Operators would like to address these issues, but poor diagnostic tools hamstring their efforts. I will argue that a more robust Internet -- one with the predictable performance and high availability needed to provide critical services -- requires the development of a new generation of better tools. We must move towards a self-healing Internet that fixes problems rapidly, without requiring the hours or days that operators often currently take. With collaborators, I have developed practical distributed systems to understand Internet problems and to provide crucial steps towards automated remediation. Our systems are deployable today, without requiring modifications to the network. In the first half of the talk, I will present Reverse Traceroute, a system to measure the routing and performance behavior of reverse paths back to the local host from other networks. While tools have long existed to measure the forward direction, the reverse path has been largely opaque, hindering troubleshooting efforts. I will show how Google and other content providers can use reverse traceroute to troubleshoot their clients' performance problems. In the second half of the talk, I will present LIFEGUARD, our system that uses Reverse Traceroute and related techniques to diagnose and automatically repair availability problems, even without the participation of the network containing the failure.
Bio: Ethan Katz-Bassett is an Assistant Professor at the University of Southern California. Previously, he worked for Google investigating mobile performance and Internet routing. He earned his PhD in Computer Science at the University of Washington in 2012, where his dissertation won the department's William Chan Memorial Dissertation Award. His research also won Best Paper awards at NSDI in 2008 and 2010. Ethan's research goal is to design deployable techniques to dramatically improve Internet reliability and performance, based on the needs of operators and providers and grounded in rigorous network measurements.
Date: Monday, Feb 25 2013 - 4:10 pm
The Discrete Frechet Distance and Applications
Presenter: Timothy Wylie
Abstract: Modern computational geometry plays a critical role across a vast number of diverse research fields where theoretical results for provably efficient algorithms are necessary. Many of these problems are based on matching geometric objects or finding paths through given points with polygonal curves. This work focuses on the study and application of polygonal curves with respect to the discrete Frechet distance. Specifically, we cover the alignment, comparison, and simplification of protein backbone chains. We address the issue of visualization via the chain pair simplification problem on the protein chains. Further, we address several applications with map routing given noisy or missing data. The presentation will overview the finished work and outline the direction of future research on the discrete Frechet distance for the completion of the doctoral research.
Date: Monday, Feb 11 2013 - 12:30 pm
Location: CS Conference Room
Big Data Analytics of Solar Dynamics Observatory at MSU
Presenter: Dr. Rafal Angryk
Description: In this talk Dr. Angryk will summarize recent research performed in the MSU’s Data Mining Laboratory. The focus of the entire seminar is on Big Data Analytics of Solar Dynamics Observatory (SDO), which is a flagship of NASA’s current “Living with a Star” program. The audience will first learn about the importance of solar data analysis, then about complexity of data maintained on the servers in our department. After that, three large research projects will be introduced with the hope of creating research interest among our graduate students. Finally, we will briefly talk about the future of the massive Solar Data Mining effort at MSU, and the potential for new research funding.
Date: Monday, Feb 4 2013 - 4:10 pm
Location: EPS 108
What You Don't Know That You Don't Know
Presenter: Andrew Le
Abstract: Your CS degree makes for a great foundation, but if you're like most students today, you'll graduate with no job and poor prospects. This despite employers constantly complaining about how hard it is to find "good engineers". The secret is that "good" encompasses much more than just Computer Science. In this session, I'll share my experience as an independent software consultant, a business owner, an engineering manager, a quitter of jobs, and all around troublemaker. I'll show you things you didn't even know you needed to know in order to get your dream job, skip rungs on the ladder, or build your own ladder.
Bio: Andrew Le is a product design and development consultant based in sunny Santa Barbara, California. He is a partner at SideProject, a web products consulting company, and cofounder of Cleverific, where he helps create exceptional tools for professional photographers.
Date: Monday, Jan 28 2013 - 4:10 pm
Location: EPS 108
Graph Queries in Networks and Linked Data
Presenter: Ramya Raghavendra
Abstract: In a wide array of disciplines, data can be modeled as an interconnected network of entities, where various attributes are associated with both the entities and the relations among them, enabling us to apply the wealth of graph algorithms to query and manage the data. In this talk, we first look at a few compelling use cases for modeling physical and information networks as graphs and using graph analysis techniques to perform tasks such as network management, anomaly detection and analytics. Next, we look at the current landscape of graph storage and processing software available. Graph databases are emerging as an important technology that can support faster graph query support compared to relational databases and scale more naturally to large data sets . Graph processing techniques include classical graph algorithms such as shortest path and reachability, which depend only on the structure of the network, as well as emerging queries that require both the topology and content information of the network data. Finally, we take a look some of the current approaches to visualize large graph data.
Bio: Ramya Raghavendra is a Research Staff Member in the Wireless Networking group at IBM TJ Watson Research Center since 2010. Prior to that, she received her MS and PhD in Computer Science from University of California Santa Barbara. Her research is focused on network measurement and management, with publications including ACM Sigcomm, IMC and IEEE INFOCOM. She is the recipient of Google Anita Borg scholarship, UCSB Oustanding Graduate Student award and ACM SRC award. Complete list of publications can be found at http://researcher.watson.ibm.com/view.php?person=us-rraghav
Date: Friday, Jan 11 2013 - 3:10 pm
Location: Roberts Hall 101