UNM Computer Science

Colloquia

Google Calendar of Colloquia

Future Colloquia (Tentative Schedule)

Info on Colloquia Requirements for Students

For students taking the colloquia course, here is some information on course requirements.

Fault-Tolerance for Extreme Scale Systems -- A Systems Level Perspective

Date: Thursday, May 2, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Kurt Ferreira
Sandia National Laboratories

Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require significant advancements in several fundamental areas. Recent reports from the U.S. Department of Energy place resilience as as one of these challenges. This resilience challenge is cross cutting and will likely require advancements in multiple layers in the systems software stack of these extreme-scale systems, from the OS to the application. In this, I will summarize current work at Sandia National Laboratories to address this important challenge. I will characterize this challenge in the context of extreme-scale capability computing, outline current approaches and their benefits, and point out unexplored areas where more work is needed.

Bio:
Kurt Ferreira A senior member of Sandia's technical staff, Kurt Ferreira is an expert on system software and resilience/fault-tolerance methods for large-scale, massively parallel, distributed-memory, scientific computing systems. Kurt has designed and developed many innovative, high-performance, and resilient implementations of low-level system software for a number of HPC platforms at Sandia National Laboratories. His research interests include the design and construction of operating systems for massively parallel processing machines and innovative application- and system-level fault-tolerance mechanisms for HPC.

Incremental SMT-based Safety Checking of Synchronous Systems

Date: Tuesday, April 30, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Cesare Tinelli
Dept. of Computer Science
University of Iowa

This talk provides an overview of our current research on automated verification. We present an incremental and parallel architecture for verifying safety properties of finite- and infinite-state synchronous systems. The architecture, implemented in the Lustre model checker Kind, relies on solvers for Satisfiability Modulo Theories (SMT) as its main inference engines. It is designed to accommodate the incorporation of automatic invariant generators to enhance Kind's basic verification algorithm (k-induction). It also allows the verification of multiple properties incrementally and the use of proven input properties to aid the verification of the remaining ones. We also briefly discuss two general approaches we have developed to produce invariant generators for the architecture above. The schemes themselves rely on efficient SMT solvers and their ability to quickly generate counter-models of non-invariant conjectures.

Finally, we provide some experimental evidence showing how parallelism, incrementality and invariant generation improve the speed and the precision of our baseline k-induction algorithm, making Kind highly competitive with other infinite-state model checkers.

Bio:
Cesare Tinelli is a professor of Computer Science and collegiate scholar at the University of Iowa. He received a PhD in Computer Science from the University of Illinois at Urbana-Champaign in 1999. His research interests include automated reasoning, formal methods, software verification, foundations of programming languages, and applications of logic in computer science.

His research has been funded by both governmental agencies (AFOSR, AFRL, NASA, NSF) and corporations (Intel, Rockwell Collins) and has appeared in more than 50 refereed publications. He has led the development of the award winning Darwin theorem prover and the Kind model checker, and co-led the development of the widely used CVC3 and CVC4 SMT solvers. He is a founder and coordinator of the SMT-LIB initiative, an international effort aimed at standardizing benchmarks and I/O formats for Satisfiability Modulo Theories solvers. He received an NSF CAREER award in 2003 for a project on improving extended static checking of software by means of advanced automated reasoning techniques, and a Haifa Verification Conference award in 2010 for his role in building and promoting the SMT community. He has given invited talks at such conferences as CAV, HVC, NFM, TABLEAUX, VERIFY, and WoLLIC.

He is an associate editor of the Journal of Automated Reasoning and a founder the SMT workshop series and the Midwest Verification Day series. He has served in the program committee of numerous automated reasoning and formal methods conferences and workshops, and in the steering committee of CADE, IJCAR, FTP, FroCoS and SMT. He was the PC chair of FroCoS'11.

Towards provably correct design of human-automation systems: Hybrid system observability and reachability

Date: Thursday, April 18, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Meeko Oishi
Assistant Professor of Electrical and Computer Engineering
University of New Mexico

In many complex cyber-physical systems, human interaction with coupled cyber and physical components can significantly complicate system safety. Such systems are often large enough that simple intuition is not enough to determine whether the user-interface, a device that both provides information to the user about the underlying automation and allows the user to issue input commands to the system, as well as the corresponding automation, is correctly designed. Consider, for example, automation surprises and other mode errors that can occur in flight management systems, despite extensive simulation and experimental testing. We propose the development of observability and reachability techniques to create a new level of confidence and reliability in safety- critical cyber-physical systems, by predicting, at the design stage, configurations under which failures might occur. Observability techniques can determine whether the user has adequate information to accomplish a known task; reachability techniques can prevent the system from reaching configurations known a priori to be unsafe. Such control theoretic techniques could form the basis of design aids for provably correct human-automation systems.

Bio:
Meeko Oishi is an Assistant Professor of Electrical and Computer Engineering at the University of New Mexico. She received the Ph.D. (2004) and M.S. (2000) in Mechanical Engineering from Stanford University, and a B.S.E. in Mechanical Engineering from Princeton University (1998). Her research interests include hybrid control theory, control of semi-automated systems, reachability analysis, nonlinear systems, and control-based modeling of Parkinson's disease. She is the recipient of a Peter Wall Institute Early Career Scholar Award, the Truman Postdoctoral Fellowship in National Security Science and Engineering, the NSF Graduate Research Fellowship and the John Bienkowski Memorial Prize, Princeton University. She has been a Science and Technology Policy Fellow at The National Academies, and a visiting researcher at NASA Ames Research Center, Honeywell Technology Center, and Sandia National Laboratories.

Modeling Brain Activity Associated with Optimal and Suboptimal Memory Strategies

Date: Tuesday, April 9, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Laura Matzen
Researcher in the Cognitive Systems department at Sandia National Laboratories

Memory underlies and supports all forms of high-level cognition and accurate memory is essential to good decision making. However, human memory is extremely fallible. Although there are many strategies and techniques that can improve memory, cognitive biases generally lead people to choose suboptimal memory strategies. One memory strategy that is very effective, yet underutilized, is retrieval practice. If learners have practice with retrieving information from memory, they are likely to perform better when that information is tested. In the present experiment, participants memorized word lists under a variety of study and test conditions while their brain activity was recorded using electroencephalography (EEG). In one study condition, they studied words passively. In another, the studied words were quizzed before the memory test, providing participants with retrieval practice. In a third condition, studied words were repeated, giving participants an opportunity to use a retrieval practice strategy if they chose to do so. Participants who adopted a retrieval practice strategy for the repeated items should have better memory for the words in that condition than participants who studied the words passively. We developed a computational model that characterized the brain activity associated with passive study and with explicit retrieval practice. We used that model to predict which participants adopted a retrieval practice strategy for the repeated study items. Finally, we evaluated the behavioral performance of the participants who were classified as using a retrieval practice strategy compared to those who were not. This analysis revealed that, as predicted, the participants whose brain activity was consistent with a retrieval practice strategy had better memory performance at test.

Bio:
Dr. Laura Matzen is a researcher in the Cognitive Systems department at Sandia National Laboratories. She runs Sandia.s Human Performance Laboratory, which uses electroencephalography (EEG), eye tracking, and behavioral measures to study a variety of cognitive processes. She holds a Ph.D. in cognitive psychology from the University of Illinois.

Discovering causes and effects from observational data in the presence of hidden variables

Date: Thursday, April 4, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Subramani Mani
Assistant Professor
Department of Biomedical Informatics
Vanderbilt University

In this talk we will introduce causal Bayesian networks (CBN) and provide a working definition of causality. After a short survey of methods for learning CBNs from data we will discuss two causal discovery algorithms: the Bayesian local causal discovery algorithm (BLCD) and the post processing Y-structure algorithm (PPYA). We will present results from five simulated data sets and one real world population based data set in the medical domain. We will conclude with some potential applications in Biomedicine and research directions for the future.

Bio:
Subramani Mani trained as a physician and completed his residency training in internal medicine (1990) and a research fellowship in Cardiology from the Medical College of Trivandrum, India. He then obtained a Master's degree in Computer Science from the University of South Carolina, Columbia in 1994 and worked as a post-graduate researcher in the Department of Information and Computer Science at the University of California, Irvine. He completed his Ph.D in Intelligent Systems with a Biomedical informatics track from the University of Pittsburgh in 2005. He joined as an Assistant professor in the Department of Biomedical informatics in 2006 and was Director of the Discovery Systems Lab there before moving to the Translational Informatics Division in the Department of Internal Medicine as Associate professor in the Fall of 2012.

His research interests are data mining, machine learning, predictive modeling and knowledge discovery with a focus on discovering cause and effect relationships from observational data.

Finding Hidden Structure in Networks

Date: Tuesday, April 2, 2013
Time: 11:00 am — 12:30 pm
Place: Mechanical Engineering 218

Cristopher Moore
Santa Fe Institute

There is more network data becoming available than humans can analyze by hand or eye. At the same time, much of this data is partial or noisy: nodes have attributes like demographics, location, and content that are partly known and partly hidden, many links are missing, and so on. How can we discover the important structures in a network, and use these structures to make good guesses about missing information? I will present a Bayesian approach based on generative models, powered by techniques from machine learning and statistical physics, with examples from food webs, word networks, and networks of documents. Along the way, we will think about what "structure" is anyway, and I will end with a cautionary note about how far we can expect to get when we think of "networks" in a purely topological way.

Bio:
Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in Physics from Cornell. He has published over 100 papers at the boundary between physics and computer science, ranging from quantum computing, to phase transitions in NP-complete problems, to the theory of social networks and efficient algorithms for analyzing their structure. With Stephan Mertens, he is the author of The Nature of Computation, published by Oxford University Press. He is a Professor at the Santa Fe Institute.

Sampling-Based Motion Planning: From Intelligent CAD to Crowd Simulation to Protein Folding

Date: Thursday, March 28, 2013
Time: 11:00 am — 12:30 pm
Place: Centennial Engineering Room 1041

Nancy Amato
Department of Computer Science and Engineering
Texas A&M University
ACM Distinguished Lecturer

Motion planning arises in many application domains such as computer animation (digital actors), mixed reality systems and intelligent CAD (virtual prototyping and training), and even computational biology and chemistry (protein folding and drug design). Surprisingly, one type of sampling-based planner, the probabilistic roadmap method (PRM), has proven effective on problems from all these domains. In this talk, we describe the PRM framework and give an overview of some PRM variants developed in our group. We describe in more detail our work related to virtual prototyping, crowd simulation, and protein folding. For virtual prototyping, we show that in some cases a hybrid system incorporating both an automatic planner and haptic user input leads to superior results. For crowd simulation, we describe PRM-based techniques for pursuit evasion, evacuation planning and architectural design. Finally, we describe our application of PRMs to simulate molecular motions, such as protein and RNA folding. More information regarding our work, including movies, can be found at http://parasol.tamu.edu/~amato/

Bio:
Nancy Amato is Unocal Professor in the Department of Computer Science and Engineering at Texas A&M University where she co-directs the Parasol Lab and is a Deputy Director of the Institute for Applied Math and Computational Science (IAMCS). She received undergraduate degrees in Mathematical Sciences and Economics from Stanford University, and M.S. and Ph.D. degrees in Computer Science from UC Berkeley and the University of Illinois at Urbana-Champaign. She was an AT&T Bell Laboratories PhD Scholar, she is a recipient of a CAREER Award from the National Science Foundation, is a Distinguished Speaker for the ACM Distinguished Speakers Program, was a Distinguished Lecturer for the IEEE Robotics and Automation Society, and is an IEEE Fellow. She was co-Chair of the NCWIT Academic Alliance (2009-2011), is a member of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W) and of the ACM, IEEE, and CRA sponsored Coalition to Diversity Computing (CDC). Her main areas of research focus are motion planning and robotics, computational biology and geometry, and parallel and distributed computing. Current representative projects include the development of a technique for modeling molecular motions (e.g., protein folding), investigation of new strategies for crowd control and simulation, and STAPL, a parallel C++ library enabling the development of efficient, portable parallel programs.

Interactive Learning of Dependency Networks for Scientific Discovery

Date: Tuesday, March 19, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Diane Oyen
UNM Department of Computer Science
PhD Student

Machine learning algorithms for identifying dependency networks are being applied to data in biology to learn protein correlations and neuroscience to learn brain pathways associated with development, adaptation and disease. Yet, rarely is there sufficient data to infer robust individual networks at each stage of development or for each disease/control population. Therefore, these multiple networks must be considered simultaneously; dramatically expanding the space of solutions for the learning problem. Standard machine learning objectives find parsimonious solutions that best fit the data; yet with limited data, there are numerous solutions that are nearly score-equivalent. Effectively exploring these complex solution spaces requires input from the domain scientist to refine the objective function.

In this talk, I present transfer learning algorithms for both Bayesian networks and graphical lasso that reduce the variance of solutions. By incorporating human input in the transfer bias objective, the topology of the solution space is shaped to help answer knowledge-based queries about the confidence of dependency relationships that are associated with each population. I also describe an interactive human-in-the-loop approach that allows a human to react to machine-learned solutions and give feedback to adjust the objective function. The result is a solution to an objective function that is jointly defined by the machine and a human. Case studies are presented in two areas: functional brain networks associated with learning stages and with mental illness; and plasma protein concentration dependencies associated with cancer.

Bio:
Diane Oyen received her BS in electrical and computer engineering from Carnegie Mellon University. She then worked for several years designing ethernet controller chips and teaching math before returning to academia. Currently, she is a PhD Candidate advised by Terran Lane in computer science at the University of New Mexico. Her broad research interests are in developing machine learning algorithms to aid the discovery of scientific knowledge. She has focused on using transfer learning in structure identification of probabilistic graphical models learned from data with interaction from a human expert. She has been invited to present her research at LANL and currently serves on the senior program committee of AAAI.

Discovering Roles and Types through Hierarchical Information Network Analysis

Date: Thursday, March 7, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Tim Weninger
PhD Candidate
University of Illinois Urbana-Champaign

Graphs are all around us. They can be made to model countless real-world phenomena ranging from the social to the scientific including engineering, biology, chemistry, medical systems, and e-commerce systems. We call these graphs information networks because they represent bits of information and their relationships. This talk focuses on discovering roles and types in very large scale information networks by exploring hierarchies inherent within the networks. We focus on the Web-information network, as well as specialized sub-networks like Wikipedia, where we aim to determine the type of a Web page or Wiki page as well as its position in the type-hierarchy (e.g., professor, student, and course exists within a department within a college) and their relationships to each other. This new information can then used to answer expressive queries on the network and allows us to explore additional properties about the network that were previously unknown.

Bio:
Tim Weninger is graduating from the Department of Computer Science at the University of Illinois Urbana-Champaign where he is a member of the DAIS group and the Data Mining Lab. His research interests are in large scale information network analysis, especially on the Web, as well as "big data"-bases, "big data"-mining, information retrieval and social media. Tim is a recipient of the National Defense Science and Engineering Graduate Fellowship (NDSEG) and the National Science Foundation Graduate Research Fellowship (NSF GRFP). He has been an invited speaker at many international venues and has served as a reviewer, external reviewer or PC member for dozens of international journals, conferences and workshops.

TEXPLORE: A Reinforcement Learning Algorithm for Robots

Date: Tuesday, March 5, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Todd Hester
Post-doctoral researcher and research educator
Department of Computer Science
University of Texas at Austin

Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots.

While there has been considerable research on RL, there has been relatively little research on applying it to practical problems such as controlling robots. In particular, for an RL algorithm to be applicable to such problems, it must address the following four challenges: 1) learn in very few actions; 2) learn in domains with continuous state features; 3) handle sensor and/or actuator delays; and 4) continually select actions in real time. In this talk, I will present the TEXPLORE algorithm, which is the first algorithm to address all four of these challenges. I will present results showing the ability of the algorithm to learn to drive an autonomous vehicle at various speeds. In addition, I will present my vision for developing more useful robots through the use of machine learning.

Bio:
Todd Hester is a post-doctoral researcher and research educator in the Department of Computer Science at the University of Texas at Austin. He completed his Ph.D. at UT Austin in December 2012 under the supervision of Professor Peter Stone. His research is focused on developing new reinforcement learning methods that enable robots to learn and improve their performance while performing tasks. Todd instructs an undergraduate course that introduces freshmen to research on autonomous intelligent robots. He has been one of the leaders of UT Austin's RoboCup team, UT Austin Villa, which won the international robot soccer championship from a field of 25 teams in 2012.

Interactive Machine Learning in High-Expertise Domains

Date: Thursday, February 28, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Kevin Small
Tufts University
Research Scientist

Machine learning and data mining methods have emerged as cornerstone technologies for transforming the deluge of data generated by modern society into actionable intelligence. For applications ranging from business intelligence to public policy to clinical guidelines, the overarching goal of "big data" analytics is to identify, analyze, and summarize the available evidence to support decision makers. While ubiquitous computing has greatly simplified data collection, successful deployment of machine learning techniques is also generally predicated on obtaining sufficient quantities of human-supplied annotations. Accordingly, judicious use of human effort in these settings is crucial to building high-performance systems in a cost-effective manner.

In this talk, I will describe methods for reducing annotation costs and improving system performance via interactive learning protocols. Specifically, I will present models capable of exploiting domain-expert knowledge through the use of labeled features -- both within the active learning framework to explicitly reduce the need for labeled data during training and the more general setting of improving classifier performance in high-expertise domains. Furthermore, I will contextualize this work within the scientific systematic review process, highlighting the importance of interactive learning protocols in a particular scenario where information must be reliably extracted from multiple information sources, synthesized into a cohesive report, and updated as new evidence is made available in the scientific literature. I will demonstrate that we can partially automate many of the aspects of this important task, thus reducing the costs incurred when interacting with highly-trained experts.

Bio:
Kevin Small received his Ph.D. degree in computer science from the University of Illinois at Urbana-Champaign (Cognitive Computation Group) in 2009. From 2009 to 2012, he held positions as a postdoctoral researcher at Tufts University (Machine Learning Group) and as a research scientist at Tufts Medical Center (Center for Evidence-based Medicine). He is presently conducting research within the Division of Program Coordination, Planning, and Strategic Initiatives at the National Institutes of Health. Kevin's primary research interests are in the areas of machine learning, data mining, natural language processing, and artificial intelligence. Specifically, his research results concern using interactive learning protocols to improve the performance of machine learning algorithms while reducing sample complexity.

Scalable paradigms for scientific discovery and time-sensitive decision making in the era of Big Data

Date: Tuesday, February 26, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Trilce Estrada
University of Delaware
Post-doctoral Researcher

Nowadays, emerging distributed technologies enable the scientific community to perform large-scale simulations at a rate never seen before. The pressure those systems put on the scientists is twofold. First, they need to analyze the massive amount of data generated as a consequence of those computations. Second, scientists need to make sure they achieve meaningful scientific conclusions with the available resources, oftentimes by changing the course of an experiment at run-time. The first challenge implies the need of new and more efficient clustering and classification techniques that require at most linear time with respect to the amount of data generated. While the second challenge needs algorithms able to build knowledge from the data and make decisions on the fly, in a time-sensitive scenario.

In this talk I will present scalable algorithms that address both challenges; the first one in the context of a high-throughput protein-ligand docking application, and the second in the context of a Volunteer Computing system. I will conclude the talk with future directions of my research including an application for cancer detection that uses crowd sourcing to build its knowledge incrementally.

Bio:
Trilce Estrada is currently a post-doctoral researcher in the Computer and Information Science Department at the University of Delaware, where she earned her PhD in 2012. Her research includes real-time decision-making for high-throughput multi-scale applications, scalable analysis of very large molecular datasets for drug design, and emulation of heterogeneous distributed systems for performance optimization. Trilce earned her MS in Computer Science and BS in Informatics from INAOE and Universidad de Guadalajara, Mexico, respectively. She is an active advocate of women in computing and current mentor of CISters@UD, a student initiative that promotes the participation of women in technology-related fields at her university.

Biological Models with Combinatorial Spaces

Date: Thursday, February 21, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Bonnie Kirkpatrick
University of British Columbia
Post-doctoral Researcher

Probabilistic models are common in biology. Many of the successful models have been readily tractable, leaving calculations on models with a combinatorial-sized state space as an open problem. This talk examines two kinds of models with combinatorial state spaces: continuous-time and discrete-time Markov chains. These models are applied to two problems: RNA folding pathways and family genetics. While the applications are disparate topics in biology, they are related via their models, the statistical quantities of interest, and in some cases the computational techniques used to calculate those quantities.

Bio:
Bonnie Kirkpatrick is from Montana, a state where the population density is one person per square mile. She attended Montana State University for her undergraduate degree in computer science, before moving to California. Once there, she completed her doctoral dissertation on "Algorithms for Human Genetics" under the supervision of Richard M. Karp and received her Ph.D. in computer science. Now she is at the University of British Columbia doing post-doctoral work with Anne Condon in the Department of Computer Science.

Recent Research Endeavors in Mobile Computing and Social Network Privacy

Date: Tuesday, February 19, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Srikanth V. Krishnamurthy
Professor of Computer Science
University of California, Riverside

There has been an explosion both in smartphone sales and usage on one hand, and social network adoption on the other. Our work targets several directions in (a) exploiting the smartphone resources in an appropriate way for computing, information dissemination and sharing and storage and (b) making social networks more usable by providing fine grained privacy controls. In this talk, I present our recent work on mobile computing and privacy in online social networks. Specifically, I will describe (a) how one can go about building a distributed computing infrastructure using smartphones and (b) how one can provision fine-grained privacy controls with Twitter. Below, I provide a brief synopsis of the two projects.

Smartphone Cloud: Every night, a large number of idle smartphones are plugged into a power source for recharging the battery. Given the increasing computing capabilities of smartphones, these idle phones constitute a sizeable computing infrastructure. Therefore, for a large enterprise which supplies its employees with smartphones, we argue that a computing infrastructure that leverages idle smartphones being charged overnight is an energy-efficient and cost-effective alternative to running tasks on traditional server infrastructure. Building a cloud with smartphones presents a unique set of challenges that stem from heterogeneities in CPU Clock speed, variability in network bandwidth and low availability compared to servers. We address may of these challenges to build CWC -- a distributed computing infrastructure using smartphones.

Twitsper: User privacy has been an increasingly growing concern in online social networks (OSNs). While most OSNs today provide some form of privacy controls so that their users can protect their shared content from other users, these controls are typically not sufficiently expressive and/or do not provide fine-grained protection of information. We consider the introduction of a new privacy control---group messaging on Twitter, with users having fine-grained control over who can see their messages. Specifically, we demonstrate that such a privacy control can be offered to users of Twitter today without having to wait for Twitter to make changes to its system. We do so by designing and implementing Twitsper, a wrapper around Twitter that enables private group communication among existing Twitter users while preserving Twitter's commercial interests. Our design preserves the privacy of group information (i.e., who communicates with whom) both from the Twitsper server as well as from undesired users. Furthermore, our evaluation shows that our implementation of Twitsper imposes minimal server-side bandwidth requirements and incurs low client-side energy consumption.

Bio:
Srikanth V. Krishnamurthy received his Ph.D degree in electrical and computer engineering from the University of California at San Diego in 1997. From 1998 to 2000, he was a Research Staff Scientist at the Information Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA. Currently, he is a Professor of Computer Science at the University of California, Riverside. His research interests are in wireless networks, online social networks and network security. Dr. Krishnamurthy is the recipient of the NSF CAREER Award from ANI in 2003. He was the editor-in-chief for ACM MC2R from 2007 to 2009. He is a Fellow of the IEEE.

Fast Primitives for Time Series Data Mining

Date: Thursday, February 14, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Abdullah Mueen
Cloud and Information Services Lab of Microsoft

Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, motifs, outliers, periods etc. as features. Fast algorithms for finding these primitive features are usually approximate whereas exact ones are very slow and therefore never used on real data. In this talk, I present efficient and exact algorithms for two time series primitives, time series motifs and shapelets. The algorithms speed up the exact search for motifs and shapelets by efficient bounds based on triangular inequality. The algorithms are much faster than the trivial solutions and successfully discover motifs and shapelets of real time series from diverse sensors such as EEG, ECG, Accelerometers and Motion captures. I present case studies on some of these data sources and end with promising directions for new and improved primitives.

Bio:
Abdullah Mueen has earned his PhD in computer science at the University of California, Riverside in 2012. His adviser was Professor Eamonn Keogh. He is primarily interested in designing primitives for time series data mining. In addition, he has experiences on working with different forms of data such as XML, DNA, spectrograms, images and trajectories. He has published his work in the top data mining conferences including KDD, ICDM and SDM. His dissertation has been selected as the runner-up in the SIGKDD Doctoral Dissertation Award in 2012. Presently he is a scientist in the Cloud and Information Services Lab of Microsoft and works on telemetry analytics.

Automatic Program Repair Using Genetic Programming

Date: Tuesday, February 12, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Claire Le Goues
PhD Candidate
University of Virginia

"Everyday, almost 300 bugs appear...far too many for only the Mozilla programmers to handle" --Mozilla developer, 2005

Software quality is a pernicious problem. Although 40 years of software engineering research has provided developers considerable debugging support, actual bug repair remains a predominantly manual, and thus expensive and time-consuming, process. I will describe GenProg, a technique that uses evolutionary computation to automatically fix software bugs. My empirical evidence demonstrates that GenProg can quickly and cheaply fix a large proportion of real-world bugs in open-source C programs. I will also briefly discuss the atypical evolutionary search space of the automatic program repair problem, and the ways it has challenged assumptions about software defects.

Bio:
Claire Le Goues is a Ph.D. candidate in Computer Science at the University of Virginia. Her research interests lie in the intersection of software engineering and programming languages, with a particular focus on software quality and automated error repair. Her work on automatic program repair has been recognized with Gold and Bronze designations at the 2009 and 2012 ACM SIGEVO "Humies" awards for Human-Competitive Results Produced by Genetic and Evolutionary Computation and several distinguished and featured paper awards.

Towards building a scalable data center network architecture in the era of cloud computing

Date: Tuesday, February 5, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Yihua He
Yahoo

In the era of cloud computing, a hierarchal network design in a traditional data center can no longer keep up with the requirements in terms of increased bandwidth and different traffic characteristics. In this talk, I will first present the challenges that a traditional data center network architecture faces. I'll then share the rationality in choosing and designing the next generation network architecture to address those challenges and bring 10G network to the host level. Finally, I'm going to present the demand of a SDN-based solution to deploy, monitor and troubleshoot this new network architecture which comes with vastly increased number of switches, viable routes and configuration changes.

Bio:
Yihua He is a member of the technical staff at Yahoo, where he is involved in the architecture, design and automation of large scale next-generation network infrastructures. He has numerous technical publications in the area of Internet routing, topology, measurement and simulation. He is a reviewer for a number of computer networking journals and conferences. Prior to joining Yahoo, he was a graduate student in University of California, Riverside, where he received his PhD degree in computer science in 2007.

Natural intelligence to Artificial intelligence: A new route that can be taken

Date: Thursday, January 31, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Kunjumon Vadakkan
University of Manitoba, Canada

Intelligence is often considered a secondary manifestation resulting from the abilities to memorize. What is the biological mechanism of memories? Current biological experiments are relying on specific behavioral motor outputs of spoken language or locomotion as measures of retrieved memories. But what exactly are memories? If we view memories as virtual internal sensations formed within the nervous system at the time of memory retrieval, how can we make further investigations? In other words, can we study the virtual sensory qualities of the internal sensations of memory? We examined possible basic units of virtual internal sensations of memory at the time of its retrieval, hypothesized re-activable cellular changes from which they can occur and traced the locations of these cellular changes back to the time of associative learning for feasible operational mechanisms. However, it is difficult to prove operation of such mechanism in biological systems. Exploration of this will only be achieved by carrying out the gold standard test of its replication in physical systems. Engineering challenges in this approach include devising methods to convert the first person perspective of internal sensations to appropriate readouts. Experiments to translate theoretically feasible neuronal mechanisms of its formation both by computational and engineering methods are required. I will explain a possible biological mechanism with substantiating evidences and will provide a broad outline of both computational and engineering methods to test the operation in physical systems. There are challenges ahead; but a collaborative efforts between Neurosciences and Physical and Engineering sciences can take further steps.

Bio:
Kunjumon Vadakkan is interested in understanding how internal sensations are created from neuronal activities. Specific features of some of the diseases are likely to provide clues to understand the normal functioning of the nervous system from which formation of internal sensations may be understood. After graduating Medicine in 1988 and practicing family medicine for a short period, Dr. Vadakkan completed the MD program in Biochemistry at the Calicut University, India. This was followed by a Research Associate position at the Jawaharlal Nehru University, New Delhi to study negative regulatory elements upstream of p53 gene. He moved to Canada in 1999, did MSc (under Dr.Umberto DeBoni) and PhD (under Dr. Min Zhuo) from the University of Toronto. Later, he did post-doctoral training in Dr. Mark Zylka's laboratory at the University of North Carolina, Chapel Hill. Currently, he is a 4th year Resident in Neurology at the University of Manitoba.

Fault-tolerant solvers via algorithm/system codesign

Date: Tuesday, January 22, 2013
Time: 11:00 am — 11:50 am
Place: Mechanical Engineering 218

Mark Hoemmen
Sandia National Laboratories
USA

Protecting arithmetic and data from corruption due to hardware errors costs energy. However, energy increasingly constrains modern computer hardware, especially for the largest parallel computers being built and planned today. As processor counts continue to grow, it will become too expensive to correct all of these "soft errors" at system levels, before they reach user code. However, many algorithms only need reliability for certain data and phases of computation, and can be designed to recover from some corruption. This suggests an algorithm / system codesign approach. We will show that if the system provides a programming model to applications that lets them apply reliability only when and where it is needed, we can develop "fault-tolerant" algorithms that compute the right answer despite hardware errors in arithmetic or data. We will demonstrate this for a new iterative linear solver we call "Fault-Tolerant GMRES" (FT-GMRES). FT-GMRES uses a system framework we developed that lets solvers control reliability per allocation and provides fault detection. This project has also inspired a fruitful collaboration between numerical algorithms developers and traditional "systems" researchers. Both of these groups have much to learn from each other, and will have to cooperate more to achieve the promise of exascale.

Bio:
Mark Hoemmen is a staff member at Sandia National Laboratories in Albuquerque. He finished his PhD in computer science at the University of California Berkeley in spring 2010. Mark has a background in numerical linear algebra and performance tuning of scientific codes. He is especially interested in the interaction between algorithms, computer architectures, and computer systems, and in programming models that expose the right details of the latter two to algorithms. He also spends much of his time working on the Trilinos library of (trilinos.sandia.gov).

Optimizing Overlay-Based Virtual Networking Through Optimistic Interrupts and Cut-Through Forwarding

Date: Friday, December 7, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Zheng Cui
Department of Computer Science
University of New Mexico

Overlay-based virtual networking provides a powerful model for realizing virtual distributed and parallel computing systems with strong isolation, portability, and recoverability properties. However, in extremely high throughput and low latency networks, such overlays can suffer from bandwidth and latency limitations, which is of particular concern if we want to apply the model in HPC environments. Through careful study of an existing very high performance overlay-based virtual network system, we have identified two core issues limiting performance: delayed and/or excessive virtual interrupt delivery into guests, and copies between host and guest data buffers done during encapsulation. We respond with two novel optimizations: optimistic, timer-free virtual interrupt injection, and zero-copy cut-through data forwarding. These optimizations improve the latency and bandwidth of the overlay network on 10 Gbps interconnects, resulting in near-native performance for a wide range of microbenchmarks and MPI application benchmarks.

Bio:
Zheng Cui is a PhD student in the Department of Computer Science at the University of New Mexico. Her research interests include virtualization, virtual networking, and HPC.

Neural Network Architectures in Agent Based Simulations

Date: Friday, November 30, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Thomas P. Caudell
Depts. of ECE, CS, and Psychology
University of New Mexico

Agent based simulation has proven itself as a valuable tool in the study of group dynamics. Agents range from particles to ants to robots to people to societies. Often, individual agent behavior is controlled by rule sets or statistical learning algorithms. In this talk, I will describe an aspect of our research that embeds biologically motivated artificial neural network architectures into agents that are endowed with a rich set of sensors and actuators. These agents reside in a 2D virtual Flatland where we are able conduct experiments that measure their performance as a function of neural architecture. I will begin with an introduction to neural networks, describe the simulated agents and Flatland, and then work through a series of architectures from simple to complex, describing their operation and the effects they have on agent behavior. I will end with a discussion of future directions in this type of research.

Bio:
Thomas P. Caudell was appointed to direct UNM's Center for High Performance Computing beginning in February 2007. Promoted to full professor in 2007, Dr. Caudell's research interests include neural networks, virtual reality, machine vision, robotics and genetic algorithms. He teaches courses in programming, computer games, neural networks, virtual reality, computer graphics and pattern recognition. He has been active in the field of virtual reality and neural networks since 1986, has more than 75 publications in these areas, and in 1993 helped organize IEEE.s first Virtual Reality Annual International Symposium. He is also an active member of the IEEE, the International Neural Network Society, and the Association for Computing Machinery.

Resource management in networks: Performance and Security Issues

Date: Friday, November 2, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Srikanth V. Krishnamurthy
Professor of Computer Science
University of California, Riverside

There has been a recent explosion in the number of applications, especially for mobile and social networking platforms. This explosion raises a plurality of performance and security issues that have to be adequately addressed. In this talk, I describe two recent projects from my group, focussing on performance in the wireless context and security in the social network context. Specifically, I will describe our work on (i) auto-configuring WLANs towards maximizing capacity, and (ii) building a distributed OSN towards providing privacy with high availability. Common to the two efforts, is the effective management of resources, either towards maximizing performance in the presence of bandwidth constraints or minimizing cost while guaranteeing both privacy and high availaibilty. Below, I provide more details with regards to the two parts of my talk.

The latest commercial WLAN products that have hit the market today are based on 802.11n. 802.11n devices allow the use of channel bonding wherein, two adjacent frequency bands can be combined to form a new, wider band to facilitate high data rate transmissions. However, the use of a wider band on one link can exacerbate the interference on nearby links. Furthermore, surprisingly, CB does not always provide benefits even in interference-free settings and can degrade performance in some cases. We investigate the reasons for why this is the case via extensive experiments. Based on the lessons learned, we design, implement and evaluate ACORN, an auto-configuration framework for 802.11n WLANs. ACORN integrates the functions of user association and channel allocation, since our study reveals that they are tightly coupled when CB is used. We showcase the performance benefits of ACORN via extensive experiments.

Shifting gears, we look at the acute need for privacy in OSNs. Today, OSNs are plagued with privacy concerns. While there are prior solutions towards provisioning privacy, they either impose high costs on users by using excessive resources on the cloud, or compromise the timeliness of sharing of data, by storing it on personal devices. We design and implement C-3PO, an architecture that explicitly allows users to privately share content with both minimum cost and high availability of content. Specifically, C-3PO guarantees the confidentiality of shared content both from untrusted cloud and OSN providers, and undesired users. It minimizes costs by only caching data/metadata associated with recently shared content in the cloud, while storing the rest (stale content) on user's machines. C-3PO is flexible and can be used either a basis for a stand-alone decentralized private OSN, or as an add on to existing OSNs. The latter option is especially attractive since it allows users to integrate C-3PO seamlessly, with the OSN interface they use today. We demonstrate the viability of C-3PO via extensive measurement studies on Facebook and a prototype implementation on top of Facebook.

Bio:
Srikanth V. Krishnamurthy received his Ph.D degree in electrical and computer engineering from the University of California at San Diego in 1997. From 1998 to 2000, he was a Research Staff Scientist at the Information Sciences Laboratory, HRL Laboratories, LLC, Malibu, CA. Currently, he is a Professor of Computer Science at the University of California, Riverside. His research interests are in wireless networks, online social networks and network security. Dr. Krishnamurthy is the recipient of the NSF CAREER Award from ANI in 2003. He was the editor-in-chief for ACM MC2R from 2007 to 2009. He is a Fellow of the IEEE.

Robust Evaluation of Expressions by Distributed Virtual Machines

Date: Friday, October 19, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Lance R. Williams
Department of Computer Science
University of New Mexico

We show how expressions written in a functional programming language can be robustly evaluated on a modular asynchronous spatial computer by compiling them into a distributed virtual machine comprised of reied bytecodes undergoing diusion and communicating via messages containing encapsulated virtual machine states. Because the semantics of the source language are purely functional, multiple instances of each reied bytecode and multiple execution threads can coexist without inconsistency in the same distributed heap.

Bio:
Lance R. Williams received his BS degree in computer science from the Pennsylvania State University and his MS and PhD degrees in computer science from the University of Massachusetts. Prior to joining UNM, he was a post-doctoral scientist at NEC Research Institute. His research interests include computer vision and graphics, digital image processing, and neural computation.

Heterogeneity in Robotic Networks

Date: Friday, October 5, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Rafael Fierro
Department of Electrical & Computer Engineering
University of New Mexico

As advances in mechanics, drive technology, microelectronics, control and communications make mobile robots ever more capable and affordable, the deployment of robotics networks is becoming a reality. A team of robots equipped with a diverse set of sensors, radios and actuators offers numerous advantages over a single unit. Some of the potential advantages include increased fault tolerance, redundancy, greater area coverage, distributed sensing and coordinated manipulation of large objects. Achieving the desired group behavior requires adequate integration of control and decision making mechanisms, and communication protocols.

In this talk, I will describe approaches that enable prioritized sensing and make use of team of robotic agents with different capabilities when large search areas need to be investigated. A heterogeneous team allows for the robots to become specialized in their abilities and therefore accomplish sub-goals more efficiently which in turn makes the overall mission more efficient. Moreover, I will present our recent results on planning for robotic routers to establish a communication network that will allow human operators or other agents to communicate with remote base stations or data fusion centers. Finally, I will outline our current work on key methodologies that enable agile load transportation using micro UAVs.

Bio:
Rafael Fierro is an Associate Professor of the Department of Electrical & Computer Engineering, University of New Mexico where he has been since 2007. He received a M.Sc. degree in control engineering from the University of Bradford, England and a Ph.D. degree in electrical engineering from the University of Texas-Arlington in 1997. Prior to joining UNM, he held a postdoctoral appointment with the GRASP Lab at the University of Pennsylvania and a faculty position with the Department of Electrical and Computer Engineering at Oklahoma State University. His research interests include nonlinear and adaptive control, robotics, hybrid systems, autonomous vehicles, and multi-agent systems. He directs the Multi-Agent, Robotics, Hybrid and Embedded Systems (MARHES) Laboratory. Rafael Fierro was the recipient of a Fulbright Scholarship, a 2004 National Science Foundation CAREER Award, and the 2007 International Society of Automation (ISA) Transactions Best Paper Award. He is serving as Associate Editor for the IEEE Control Systems Magazine and IEEE Transactions on Automation Science and Engineering.

Emergent Reliability: Recent Results in Byzantine Agreement

Date: Friday, September 28, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Jared Saia
Department of Computer Science
University of New Mexico

Imagine we have a collection of agents, some of which are unreliable, and we want to build a reliable system. This fundamental problem is faced by many natural systems like social insect colonies, the brain and the immune system. A key component of these systems is that periodically all agents commit to a particular action. The Byzantine agreement problem formalizes the challenge of commitment by asking: Can a set of agents agree on a value, even if some of the agents are unreliable? Application areas of Byzantine agreement include: control systems, distributed databases, peer-to-peer systems, mechanism design, sensor networks, and trust management systems.

In this talk, we describe several recent results in Byzantine agreement. First, we describe an algorithm for Byzantine agreement that is scalable in the sense that each agent sends only O(sqrt(n)) bits, where n is the total number of agents. Second, we describe very efficient algorithms to solve Byzantine agreement in the case where all agents have access to a global coin. Finally, we describe a very recent result that gives an algorithm to solve Byzantine agreement in the presence of an adversary that is adaptive: the adversary can take over up to a third of the agents at any point during the execution of the algorithm. Our algorithm runs in expected polynomial time and is the first sub-exponential algorithm in this model.

Bio:
Jared Saia is an Associate Professor of Computer Science at the University of New Mexico. His broad research interests are in theory and algorithms with a focus on designing distributed algorithms that are robust against a computationally unbounded adversary. He is the recipient of several grants and awards including an NSF CAREER award, School of Engineering Senior and Junior Faculty Research Excellence awards, and several best paper awards.

Fun, Physics, and Fixed Points in Computer Science

Date: Friday, September 21, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Joe Kniss
Department of Computer Science
University of New Mexico

This talk will cover recent research and results at UNM's Advanced Graphics Lab and Art Research Technology and Science Lab in the areas of visualization, robotics, and mayhem. We combine language, physics, mathematics, and human interaction to motivate novel CS research.

Bio:
Joe Kniss has been an Assistant Professor in the Department of Computer Science at UNM since 2007. He is the Founding Director of UNM's Advanced Graphics Lab.

Recent Advances in Computer Go Playing

Date: Friday, September 7, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Patrick Bridges
Department of Computer Science
University of New Mexico

The oriental board game Go (Chinese: Wei'qi, Korean: Baduk, Japanese: Igo) has long been of one of the most challenging board games for computers to play. For example, computers are as strong or stronger than the best human players, backgammon programs play at world championship levels, and checkers is actually solved. In contrast, computer Go programs have long been at best no stronger than an average club player. This is no longer true. Relatively recent advances in computer Go programs have resulted in dramatic advances in computer strength. Computers can now hold their own against professional players on reduced-size 9x9 boards, and hold their own and beat reasonably strong (amatuer dan-level) human players.

In this talk, I describe why Go has historically been difficult for computers to play well and the recent technical advances that have enabled the large increases in computer Go program strength. As part of this, I will also overview the basics of the game itself, and present some recent examples of the growth in computer strength (including one that involved a $10,000 bet). Finally, I will discuss both the future prospects of computer Go play, and the broader relevance of the techniques used to make strong computer Go programs to computer science in general.

Bio:
Patrick Bridges is an associate professor at the University of New Mexico in the Department of Computer Science. He did his undergraduate work at Mississippi State University and received his Ph.D. from the University of Arizona in December of 2002. His research interest broadly cover operating systems and networks particularly, scaling, composition, and adaptation issues in large-scale systems. He works with collaborators at Sandia, Los Alamos, and Lawrence Berkeley National Laboratories, IBM Research, AT&T Research, and a variety of universities.

A peer-to-peer architecture for supporting dynamic shared libraries in large-scale systems

Date: Friday, August 31, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Matthew G. F. Dosanjh
Department of Computer Science
University of New Mexico

Historically, scientific computing applications have been statically linked before running on massively parallel High Performance Computing (HPC) platforms. In recent years, demand for supporting dynamically linked applications at large scale has increased. When programs running at large scale dynamically load shared objects, they often request the same file from shared storage. These independent requests tax the shared storage and the network, causing a significant delay in computation time. In this paper, we propose to leverage a proven file sharing technique, BitTorrent, abstracted by an on-node FUSE interface to create a system-level distribution method for these files. We detail our proposed methodology, related work, and our current progress.

Bio:
Matthew G. F. Dosanjh is a third year PhD student advised by Professor Patrick G. Bridges within the UNM Department of Computer Science. He received his bachelors degree in Computer Science from UNM in the spring of 2010. He decided to stay at UNM to pursue a PhD. His research interests center around high performance computing, particularly in scalability and resilience.

On the Viability of Compression for Reducing the Overheads of Checkpoint/Restart-based Fault Tolerance

Date: Friday, August 24, 2012
Time: 12:00 pm — 12:50 pm
Place: Centennial Engineering Center 1041

Dewan Ibtesham
Department of Computer Science
University of New Mexico

The increasing size and complexity of high performance computing (HPC) systems have led to major concerns over fault frequencies and the mechanisms necessary to tolerate these faults. Previous studies have shown that state-of-the-field checkpoint/restart mechanisms will not scale sufficiently for future generation systems. Therefore, optimizations that reduce checkpoint overheads are necessary to keep checkpoint/restart mechanisms effective. In this work, we demonstrate that checkpoint data compression is a feasible mechanism for reducing checkpoint commit latencies and storage overheads. Leveraging a simple model for checkpoint compression viability, we show: (1) checkpoint data compression is feasible for many types of scientific applications expected to run on extreme scale systems; (2) checkpoint compression viability scales with checkpoint size; (3) user-level versus system-level checkpoints bears little impact on checkpoint compression viability; and (4) checkpoint compression viability scales with application process count. Lastly, we describe the impact that checkpoint compression might have on future generation extreme scale systems.

Bio:
Dewan Ibtesham is a third year PhD student advised by Professor Dorian Arnold within the UNM Department of Computer Science. He received his bachelors degree in Computer Science and Engineering from BUET (Bangladesh University of Engineering Technology). After working two and a half years in the software industry, he moved to the U.S. and started graduate school beginning fall 2009. His research interests are generally in high performance computing and large scale distributed systems; in particular, making sure that the HPC systems are fault tolerant and reliable for users so that the full potential of the systems are properly utilized.

OS-Virtual Machine Collaboration: Improving Introspection to Bridge the Semantic Gap

Date: Friday, May 4, 2012
Time: 4:00 pm — 5:00 pm
Place: Centennial Engineering Center B146 (in the basement)

Daniela Oliveira
Bowdoin College

In the last ten years virtual machines (VMs) have been extensively used for security-related applications, such as intrusion detection systems, malicious software (malware) analyzers and secure logging and replay of system execution. A VM is high-level software designed to emulate a computer's hardware. In the traditional usage model, security solutions are placed in a VM layer, which has complete control of the system resources. The guest operating system (OS) is considered to be easily compromised by malware and runs unaware of virtualization. The cost of this approach is the semantic gap problem, which hinders the development and widespread deployment of virtualization-based security solutions: there is significant difference between the state observed by the guest OS (high level semantic information) and by the VM (low level semantic information). The guest OS works on abstractions such as processes and files, while the VM can only see lower-level abstractions, such as CPU and main memory. To obtain information about the guest OS state these virtualization solutions use a technique called introspection, by which the guest OS state is inspected from the outside (VM layer), usually by trying build a map of the OS layout to an area of memory where these solutions can analyze it. We propose a new way to perform introspection, by having the guest OS, traditionally unaware of virtualization, actively collaborate with a VM layer underneath it by requesting services and communicating data and information as equal peers in different levels of abstraction. Our approach allows for stronger and more fine-grained and flexible security approaches to be developed and it is no less secure than the traditional model, as introspection tools also depend on the OS data and code to be untampered to report correct results.

Bio:
Daniela Oliveira is an Assistant Professor in the Department of Computer Science at Bowdoin College. She received her PhD in Computer Science in 2010 from the University of California at Davis where she specialized in computer security and operating systems. Her current research focuses on employing virtual machine and operating systems collaboration to protect OS kernels against compromise. She is also interested in leveraging social trust to help distinguishing benign and malicious pieces of data. She is the recipient of the NSF CAREER Award 2012.

Geometric Shape Matching with Applications

Date: Thursday, May 3, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Carola Wenk
Associate Professor of Computer Science
University of Texas at San Antonio

Geometric shapes are at the core of a wide range of application areas. In this talk we will discuss how approaches from computational geometry can be used to solve shape matching problems arising in a variety of applications including biomedical areas and intelligent transportation systems. In particular, we will discuss point pattern matching algorithms for the comparison of 2D electrophoresis gels, as well as algorithms to compare and process trajectories for improved navigation systems and for live cell imaging.

Bio:
Carola Wenk is an Associate Professor of Computer Science at the University of Texas at San Antonio (UTSA). She received her PhD from Free University Berlin, Germany. Her research area is in algorithms and data structures, in particular geometric algorithms and shape matching. She has 40 peer-reviewed publications, 22 with students, and she is actively involved in several applied projects including topics in biomedical areas and in intelligent transportation systems. She is the principal investigator on a $1.9M NIH grant funding the Computational Systems Biology Core Facility at UTSA. Dr. Wenk won an NSF CAREER award as well as research, teaching, and service awards at UTSA. She is actively involved in service to the university, including serving as the Chair of the Faculty Senate and as the Faculty Advisor for two student organizations.

A new approach for removing the noise in Monte Carlo rendering

Date: Tuesday, May 1, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Pradeep Sen
Department of Electrical and Computer Engineering
University of New Mexico

Image synthesis is the process of generating an image from a scene description that includes geometry, material properties, and camera/light positions. This is a central problem in many applications, ranging from rendering images for movies/videogames to generating realistic environments for training and tele-presence applications. The most powerful methods for photorealistic image synthesis are based on Monte Carlo (MC) algorithms, which simulate the full physics of light transport in a scene by estimating a series of multi-dimensional integrals using a set of random point samples. Although these algorithms can produce spectacular images, they are plagued by noise at low sampling rates and therefore require long computation times (as long as a day per image) to produce acceptable results. This has made them impractical for many applications and limited their use in real production environments. Thus, solving this issue has become one of the most important open problems in image synthesis and has been the subject of extensive research for almost 30 years.

In this talk, I present a new way to think about the source of Monte Carlo noise, and propose how to identify it in an image using a small number of computed samples. To do this, we treat the rendering system as a black box and calculate the statistical dependency between the outputs and the random parameter inputs using mutual information. I then show how we can use this information with an image-space, cross-bilateral filter to remove the MC noise but preserve important scene details. This process allows us to generate images in a few minutes that are comparable to those that took hundreds of times longer to render. Furthermore, our algorithm is fully general and works for a wide range of Monte Carlo effects, including depth of field, area light sources, motion blur, and path tracing. This work opens the door to a new set of algorithms that make Monte Carlo rendering feasible for more applications.

Bio:
Pradeep Sen is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Mexico. He received his B.S. in Computer and Electrical Engineering from Purdue University in 1996 and his M.S. in Electrical Engineering from Stanford University in 1998 in the area of electron-beam lithography. After two years at a profitable startup company which he co-founded, he joined the Stanford Graphics Lab where he received his Ph.D. in Electrical Engineering in June 2006, advised by Dr. Pat Hanrahan.

He joined the faculty at UNM in the Fall of 2006, where he founded the UNM Advanced Graphics Lab. His core research combines signal processing theory with computation and optics/light-transport analysis to address problems in computer graphics, photography, and computational image processing. He is the co-author of five ACM SIGGRAPH papers (three at UNM) and has been awarded more than $1.7 million in research funding, including an NSF CAREER award to study the application of sparse reconstruction algorithms to computer graphics and imaging. He received two best-paper awards at the Graphics Hardware conference in 2002 and 2004, and the Lawton-Ellis Award in 2009 and the Distinguished Researcher Award in 2012, both from the ECE department at UNM. Dr. Sen has also started a successful educational program at UNM, where his videogame development program is now ranked by the Princeton Review as one of the top 10 undergraduate programs in North America.

Designing Privacy Interfaces: Supporting User Understanding and Control

Date: Thursday, April 26, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Patrick Gage Kelley
Carnegie Mellon University

Users are increasingly expected to manage complex privacy settings in their normal online interactions. From shopping to social networks, users make decisions about sharing their personal information with corporations and contacts, frequently with little assistance. Current solutions require consumers to read long documents or control complex settings buried deep in management interfaces. Because these mechanisms are difficult to use and have limited expressiveness, users often have little to no effective control.

My goal is to help people cope with the shifting privacy landscape. My work explores many aspects of how users make decisions regarding privacy, while my dissertation focuses on two specific areas: online privacy policies and mobile phone application permissions. I explored consumers' current understanding of privacy in these domains, and then used that knowledge to iteratively design, build, and test more comprehensible information displays. I simplified online privacy policies through a "nutrition label" for privacy - a simple, standardized label that helps consumers compare website practices and am currently working to redesign the Android permissions display, which I have found to be incomprehensible to most users.

Bio:
Patrick Gage Kelley is a Ph.D. candidate in Computation, Organizations, and Society at Carnegie Mellon University's (CMU) School of Computer Science, who is co-advised by Lorrie Faith Cranor and Norman Sadeh. His research centers on information design, usability, and education involving privacy. He has worked on projects related to passwords, location-sharing, privacy policies, mobile apps, Twitter, Facebook relationship grouping, and the use of standardized, user-friendly privacy displays. He also works with the CMU School of Art's STUDIO for Creative Inquiry in new media arts and information visualization. For more see http://patrickgagekelley.com

Motors, Voters, and the Future of Embedded Security

Date: Tuesday, April 24, 2012
Time: 11:00 am — 12:15 pm
Place: Centennial Engineering Center 1041 (NOTE DIFFERENT LOCATION FROM USUAL LOCATION)

Stephen Checkoway
Computer Science & Engineering
University of California San Diego

The stereotypical view of computing, and hence computer security, is a landscape filled with laptops, desktops, smartphones and servers; general purpose computers in the proper sense. However, this is but the visible tip of the iceberg. In fact, most computing today is invisibly embedded into systems and environments that few of us would ever think of as computers. Indeed, applications in virtually all walks of modern life, from automobiles to medical devices, power grids to voting machines, have evolved to rely on the same substrate of general purpose microprocessors and (frequently) network connectivity that underlie our personal computers. Yet along with the power of these capabilities come the same potential risks as well. My research has focused on understanding the scope of such problems by exploring vulnerabilities in the embedded environment, how they arise, and the shape of the attack surfaces they expose. In this talk, I will particularly discuss recent work on two large-scale platforms: modern automobiles and electronic voting machines. In each case, I will explain how implicit or explicit assumptions in the design of the systems have opened them to attack. I will demonstrate these problems, concretely and completely, including arbitrary control over election results and remote tracking and control of an unmodified automobile. I will explain the nature of these problems, how they have come to arise, and the challenges in hardening such systems going forward.

Bio:
Stephen Checkoway is a Ph.D. candidate in Computer Science and Engineering at UC San Diego. Before that he received his B.S. from the University of Washington. He is also a member of the Center for Automotive Embedded Systems Security, a collaboration between UC San Diego and the University of Washington. Checkoway's research spans a range of applied security problems including the security of embedded and cyber-physical systems, electronic voting, and memory safety vulnerabilities.

Hybrid Analysis and Control of Malware

Date: Monday, April 23, 2012
Time: 3:30 pm — 4:30 pm
Place: Centennial Engineering Center 1041 (NOTE DIFFERENT LOCATION AND TIME)

Barton P. Miller
Computer Sciences Department
University of Wisconsin

Malware attacks necessitate extensive forensic analysis efforts that are manual-labor intensive because of the analysis-resistance techniques that malware authors employ. The most prevalent of these techniques are code unpacking, code overwriting, and control transfer obfuscations. We simplify the analyst's task by analyzing the code prior to its execution and by providing the ability to selectively monitor its execution. We achieve pre-execution analysis by combining static and dynamic techniques to construct control- and data-flow analyses. These analyses form the interface by which the analyst instruments the code. This interface simplifies the instrumentation task, allowing us to reduce the number of instrumented program locations by a hundred-fold relative to existing instrumentation-based methods of identifying unpacked code. We implement our techniques in SD-Dyninst and apply them to a large corpus of malware, performing analysis tasks such as code coverage tests and call-stack traversals that are greatly simplified by hybrid analysis.

Bio:
Barton P. Miller is a Professor of Computer Sciences at the University of Wisconsin, Madison. He received his B.A. degree from the University of California, San Diego in 1977, and M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley in 1980 and 1984. Professor Miller is a Fellow of the ACM.

Malware Analysis on Mobile and Commodity Computing Platforms

Date: Tuesday, April 17, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Manuel Egele
University of California, Santa Barbara

Two complementing approaches exist to analyze potentially malicious software (malware); static and dynamic analysis. Static analysis reasons about the functionality of the analyzed application by analyzing the program's code in source, binary, or any intermediate representation. In contrast, dynamic analysis monitors the execution of an application and the effects the application has on the execution environment. In this talk I will present a selection of my research in both areas -- static and dynamic analysis.

On commodity x86 computer systems the browser has become a central hub of activity and information. Hence, a plethora of malware exists that tries to access and leak the sensitive information stored in the browser's context. Accordingly, I will present the research and results form my dynamic analysis system (TQANA) targeting malicious Internet Explorer plugins. TQANA implements full system data-flow analysis to monitor the propagation of sensitive data originating from within the browser. This system successfully detects a variety of spyware components that steal sensitive data (e.g., the user's browsing history) from the browser.

In the mobile space, smartphones have become similar hubs for online communication and private data. The protection of this sensitive data is of great importance to many users. Therefore, I will demonstrate how my system (PiOS) leverages static binary analysis to detect privacy violations in applications targeted at Apple's iOS platform. PiOS automatically detects a variety of privacy breaches, such as the transmission of GPS coordinates, or leaked address books. Applications that transmit address book contents recently got in the focus of mainstream media as many popular social network applications (e.g., Path, Gowalla, or Facebook) transmit a copy of the user's address book to their backend servers. The static analysis in PiOS is also the foundation for a dynamic enforcement system that implements control-flow integrity (CFI) on the iOS platform. Thus, this system is suitable to prevent the broad range of control flow diverting attacks on the iOS platform.

Bio:
Manuel Egele currently is a post-doctoral researcher at the Computer Security Group at the Department of Computer Science of the University of California, Santa Barbara. Hereceived his Ph.D. in January 2011 from the Vienna University of Technology under his advisors Christopher Kruegel and Engin Kirda. Before starting his work as a post-doc he visited the Computer Security Group at UCSB as part of his Ph.D. studies. Similarly, he spent six months visiting the iSeclab's research lab in France (i.e., Institute Eurecom). He was very fortunate to meet and work with interesting and smart people at all these locations.

His research interests include most aspects of systems security, such as mobile security, binary and malware analysis, and web security.

Since 2009 he has helped organizing UCSB's iCTF. In 2010 they were the first CTF that featured a challenge with effects on the physical world (i.e., the teams had to control a foam missile launcher). In 2011 they took this concept one step further and teams from around the globe could remote control a unmaned areal vehicle in the conference room of UCSB's Computer Science Department. Before being part of the organzing team for the iCTF he participated as part of the We_0wn_Y0u team of the Vienna University of Technology, as well as on the team of the Institute Eurecom. Furthermore, he participated as part of the Shellphish team at several DefCon CTF competitions in Las Vegas.

Security Across the Software-Silicon Boundary

Date: Tuesday, April 10, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Mohit Tiwari
University of California, Berkeley

The synergy between computer architecture and program analysis can reveal vital insights into the design of secure systems. The ability to control information as it flows through a machine is a key primitive for computer security, however, software-only analyses are vulnerable to leaks in the underlying hardware. In my talk, I will demonstrate how complete information flow control can be achieved by co-designing an analysis together with the processor architecture.
The analysis technique, GLIFT, is based on the insight that all information flows -- whether explicit, implicit, or timing channels -- look surprisingly alike at the gate level where assembly language descriptions crystallize into precise logical functions. The architecture introduces Execution Leases, a programming model that allows a small kernel to directly control the flow of all secret or untrusted information, and whose implementation is verifiably free from all digital information leaks. In the future, my research will use this cross-cutting approach to build systems that make security and privacy accessible to mainstream users while supporting untrusted applications across cloud and client devices.

Bio:
Mohit Tiwari is a Computing Innovation Fellow at University of California, Berkeley. He received his PhD in Computer Science from University of California, Santa Barbara in 2011. His research uses computer architecture and program analyses to build secure, reliable systems, and has received a Best Paper award at PACT 2009, an IEEE Micro Top Pick in 2010, and the Outstanding Dissertation award in Computer Science at UCSB in 2011.

Coexistence, Collaboration, and Coordination Paradigms in the Presence of Mobility

Date: Thursday, April 5, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Gruia-Catalin Roman
University of New Mexico
Dean of the School of Engineering

Mobile computing is a broad field of study made possible by advances in wireless technology, device miniaturization, and innovative packaging of computing, sensing, and communication resources. This talk is intended as a personal intellectual journey spanning a decade of research activities, which have been shaped by the concern with rapid development of applications designed to operate in the fluid and dynamic settings that characterize mobile and sensor networks. The presence of mobility often leads to fundamental changes in our assumptions about the computing and communication environment and about its relation to the physical world and the user community. This, in turn, can foster a radical reassessment of one's perspective on software system design and deployment. Several paradigm shifts made manifest by considerations having to do with physical and logical mobility will be examined and illustrated by research involving formal models, algorithms, middleware, and protocols. Special emphasis will be placed on problems that entail collaboration and coordination in the mobile setting.

Bio:
Gruia-Catalin Roman was born in Bucharest, Romania, he studied general engineering topics for two years at the Polytechnic Institute of Bucharest and became the beneficiary of a Fulbright Scholarship. In the fall of 1971, Roman entered the very first computer science freshman class at the University of Pennsylvania. In the years that followed, he earned B.S. (1973), M.S. (1974), and Ph.D. (1976) degrees, all in computer science. At the age of 25, he began his academic career as Assistant Professor at Washington University in St. Louis. In 1997, Roman was appointed department head. Under his leadership, the Department of Computer Science and Engineering experienced a dramatic transformation in faculty size, level of research activities, financial strength, and reputation. In 2004, he was named the Harold B. and Adelaide G. Welge Professor of Computer Science at Washington University. On July 1, 2011, he became the 18th dean of the University of New Mexico School of Engineering. His aspirations as dean are rooted in his conviction that engineering and computing play central and critical roles in facilitating social and economic progress. Roman sees the UNM School of Engineering as being uniquely positioned to enable scientific advances, technology transfer, and workforce development on the state, national, and international arenas in ways that are responsive to both environmental and societal needs and that build on the rich history, culture, and intellectual assets of the region.

How should computers fix themselves? or Self-healing distributed networks

Date: Tuesday, April 3, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Amitabh Trehan
Technion, Haifa, Israel

Consider a simple turn based game between an attacker and a defender (you) playing on a large connected graph: In her turn, the attacker deletes a node and in your turn you are supposed to connect all the neighbors of the deleted node so that somehow at any point in the game, no node has increased its degree by more than a constant nor has the diameter of the network blown up. Now, consider that the nodes themselves are smart computers or agents and do not know anything about their network other than their 'nearby' nodes and have no centralized help; In essence they have to maintain certain local and global properties by only local actions while under attack from a powerful adversary.

The above game captures the essence of distributed self-healing in reconfigurable networks (e.g. peer-to-peer, ad-hoc and wireless mesh networks etc). Many such challenging and interesting scenarios arise in this context. We will look at some of these scenarios and at our small but rich and evolving body of work. Our algorithms simultaneously maintain a subset of network properties such as connectivity, degree, diameter, stretch, subgraph density, expansion and spectral properties. Some of our work uses the idea of virtual graphs - graphs consisting of 'virtual' nodes simulated by the real nodes, an idea that we will look at in more detail.

Bio:
Amitabh Trehan is a postdoc at Technion, Haifa, Israel. There, he works with Profs. Shay Kutten and Ron Lavi on distributed algorithms and game theory. He has earlier also worked as a postdoc with Prof. Valerie King (at UVIC, Canada). He did his Ph.D. with Prof. Jared Saia at UNM on algorithms for self-healing networks.

His broad research interests are in theory and algorithms with specific interests in distributed algorithms, networks, and game theory.His interest includes designing efficient distributed algorithms for robustness/self-healing/self-* properties in systems under adversarial attack, and studying game theoretic and other mechanisms for evolving networks, such as social networks or distributed systems (P2P networks etc).

Internet voting - threat or menace

Date: Tuesday, March 27, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Jeremy Epstein
SRI International

Internet voting is in the headlines, frequently coupled with the question "if I can bank online and shop online why can't I vote online." This presentation will describe the range of systems that fall under the name "internet voting," explain the security issues in today's internet voting systems, recommend what can and can't be done safely, discuss limitations of experimental systems, and point to future directions and areas for research.

Bio:
Jeremy Epstein is Senior Computer Scientist at SRI International in Arlington, VA where his research interests include voting systems security and software assurance. Prior to joining SRI, Jeremy led product security for an international software vendor. He's been involved with varying aspects of security for over 20 years. He is Associate Editor in Chief of IEEE Security & Privacy magazine, an organizer of the Annual Computer Security Applications Conference, and serves on too many program committees. Jeremy grew up in Albuquerque where he attended Sandia High School and UNM (part time while in high school), before fleeing the big city to earn a B.S. from New Mexico Tech in Computer Science, followed by an M.S. from Purdue University. He's lived in Virginia for 25 years, and misses green chile every day.

Using fine-grained code and fine-grained interviews to understand how electrical engineers learn to program

Date: Tuesday, March 20, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Brian Danielak
University of Maryland, College Park

Students can take remarkably different paths toward the development of design knowledge and practice. Using data from a study of an introductory programming course for electrical engineers, we investigate how students learn elements of design in the course, and how their code (and the process by which they generate it) reflects what they're learning about design. Data are coordinated across clinical interviews, ethnographic observation, and fine-grained evolution of students' code, exploring the question of what it means to "know" design practices common to programming, such as functional abstraction and hierarchical decomposition.

Bio:
Brian Danielak is currently a fourth-year Ph.D. student in Science Education Research at the University of Maryland. At the moment, he studies how university engineering students engage in mathematical and physical sensemaking in their courses. He works under Ayush Gupta, and his advisor Andy Elby. His research interests include mathematical sensemaking and symbolic reasoning, representational competency in scientific argumentation, students' epistemological beliefs in science and mathematics, and interplays of emotion, cognition, and student epistemology. He graduated from the University of Buffalo Honors Program, with degrees in Chemistry (BA, 2007) and English (BA, 2007). While there, he worked as an undergraduate research fellow with Kenneth Takeuchi. He also completed an Undergraduate Honors Thesis on the relationships between narrative and science under the direction of Robert Daly.

Principles of Scalable HPC System Design

Date: Tuesday, March 6, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Suzanne Kelly
Sandia National Lab

Sandia National Laboratories has a long history of successfully applying high performance computing (HPC) technology to solve scientific problems. We drew upon our experiences with numerous architectural and design features when planning our most recent computer systems. This talk will present the key issues that were considered. Important principles are performance balance between the hardware components and scalability of the system software. The talk will conclude with lessons learned from the system deployments.

Bio:
Suzanne Kelly is a distinguished member of technical staff at Sandia National Laboratories. Suzanne holds a BS in computer science from the University of Michigan and an MS in computer science from Boston University. Suzanne has worked on projects related to system-level software as well as information systems. In addition to her project management activities, she currently has responsibility for the system software on the Cielo supercomputer. Her previous assignments were leading the operating system teams for the Red Storm and ASCI Red supercomputers. Prior to her 6-year sojourn in information systems for nuclear defense technologies, she worked on various High Performance Computing file archive systems.

CitiSense - Always-on Participatory Sensing for Air Quality

Date: Thursday, March 1, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

William G. Griswold
Department of Computer Science & Engineering
University of California, San Diego

Recent revelations about the impact of air pollution on our health are troubling, yet air pollution and the risks it poses to us are largely invisible. Today, the infrastructure of our regulatory institutions is inadequate for the cause: sensors are few and often far from where we live. What about the air quality on your jogging route or commute? Can you be told when it matters most? Recent advances in computing technology put these capabilities within reach. By pervasively monitoring our immediate environs, aggregating the data for analysis, and reflecting the results back to us quickly, we can avoid toxic locales, appreciate the consequences of our behavior, and together seek a mandate for change. In this talk, I describe CitiSense, which leverages the proliferation of mobile phones and the advent of cheap, small sensors to develop a new kind of .citizen infrastructure.. We have built a robust end-to-end prototype system, exposing an abundance of challenges in power management, software architecture, privacy, inference with "noisy" commodity sensors, and interaction design. The most critical challenge is providing an always-on experience when depending on the personal devices of users. I report on early research results, including those of our first user study, which reveal the incredible potential for participatory sensing of air quality, but also open problems.

Bio:
William G. Griswold is a Professor of Computer Science and Engineering at UC San Diego. He received his Ph.D. in Computer Science from the University of Washington in 1991, and his BA in Mathematics from the University of Arizona in 1985. His research interests include ubiquitous computing and software engineering, and educational technology. Griswold is a pioneer in the area of software refactoring. He also built ActiveCampus, one of the early mobile location-aware systems. His current CitiSense project is investigating technologies for low-cost ubiquitous real-time air-quality sensing. He was PC Chair of SIGSOFT FSE in 2002 and PC co-Chair of ICSE in 2005. He is the current past-Chair of ACM SIGSOFT. He is a member of the ACM and the IEEE Computer Society.

Line up and wait your turn!

Date: Tuesday, February 21, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Tools for the Construction of Optimized Matrix Algebra Software

Date: Tuesday, February 28, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Elizabeth Jessup
University of Colorado
Department of Computer Science

Linear algebra constitutes the most time-consuming part of simulations in many fields of science and engineering. Reducing the costs of those calculations can have a significant impact on overall routine performance, but such optimization is difficult. At each step of the process, the code developer is confronted with many possibilities. Choosing between them generally requires expertise in numerical computation, mathematical software, compilers, and computer architecture, yet few scientists have such broad expertise. This talk will cover two interrelated collaborative projects focused on easing the production of high-performance matrix algebra software.

I will first present work in progress on a taxonomy of software that can be used to build highly-optimized matrix algebra software. The taxonomy will provide an organized anthology of software components and programming tools needed for that task. It will serve as a guide to practitioners seeking to learn what is available for their programming tasks, how to use it, and how the various parts fit together. It will build upon and improve existing collections of numerical software, adding tools for the tuning of matrix algebra computations. Our objective is to build a taxonomy that will provide all of the software needed to take a matrix algebra problem from algorithm description to a high-performance implementation.

I will then introduce one of the tuning tools to be included in the taxonomy, the Build to Order (BTO) compiler which automates loop fusion in matrix algebra kernels. This optimization serves to reduce the amount of data moved between memory and the processor. In particular, I will describe BTO's analytic memory model which accelerates the compiler by substantially reducing the number of loop fusion options processed by it. The initial draft of the model took into account traffic through the caches and TLB. I will discuss an example that motivated us to improve the accuracy of the model by adding register allocation.

Bio:
Elizabeth Jessup's research concerns the development of efficient algorithms and software for matrix algebra problems. This work began with the development of innovative memory-efficient algorithms and, more recently, has moved toward tools to aid in programming of matrix algebra software. Dr. Jessup has recently been collaborating with experts in compiler technology, focusing on compilers that create fast numerical software. Their initial focus has been on making efficient use of the memory hierarchy on a single processor but they are moving into multicore and GPU implementations. She is also interested in usability of scientific software. To that end, Dr. Jessup is working with collaborators on a tool to automate the construction of numerical software. Given a problem specification, the tool will find and tune appropriate routines for its solution.

Dr. Jessup was co-developer of an award-winning, NSF-funded undergraduate curriculum in high-performance scientific computing and have continued to work on innovative approaches to education in her field. She has also conducted research on factors that influence women's interest in computer science.

Line up and wait your turn!

Date: Tuesday, February 21, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Tom Hayes
University of New Mexico
Department of Computer Science

We have all had the experience of waiting in a line before getting our turn to do something. I will talk about some simple algorithms involving lining up, and their sometimes surprising behavior.

Bio:
Tom Hayes is an assistant professor at the University of New Mexico in the Department of Computer Science. Broadly speaking, he is interested in Theoretical Computer Science and Machine Learning. Some of his particular interests include: convergence rates for Markov chains, sampling algorithms for random combinatorial structures, and online decision-making algorithms.

Inefficient Performance, or Doing this faster by Doing things again

Date: Thursday, February 16, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Patrick Bridges
University of New Mexico
Department of Computer Science

Modern systems are becoming increasingly challenging to fully leverage, especially but not exclusively at the system software level, with parallelism and reliability becoming major challenges. Current programming techniques do not address these challenges well, relying either on complex synchronization that is hard to understand, debug, analyze, and optimize, or forcing almost complete separation between cores. In this talk, I will present a new approach to programming system software for modern machines that leverages replication and redundancy to extract performance from multi-core hardware. In addition, its use of replication as a key structuring element has the potential to provide for a more reliable system that is robust in the face of failure. I describe the approach overall, discuss its novel features, advantages, and challenges, present performance numbers from work applying this approach in the context of a network protocol stack implementation, and discuss potential directions for future work.

Bio:
Patrick Bridges is an associate professor at the University of New Mexico in the Department of Computer Science. He did his undergraduate work at Mississippi State University and received his Ph.D. from the University of Arizona in December of 2002. His research interest broadly cover operating systems and networks particularly, scaling, composition, and adaptation issues in large-scale systems. He works with collaborators at Sandia, Los Alamos, and Lawrence Berkeley National Laboratories, IBM Research, AT&T Research, and a variety of universities.

Detecting malware: traffic classification, botnets, and Facebook scams

Date: Thursday, February 9, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Michalis Faloutsos
University of California, Riverside

In this talk, we highlight two topics on security from our lab. First, we address the problem of Internet traffic classification (e.g. web, filesharing, or botnet?). We present a fundamentally different approach to classifying traffic that studies the network wide behavior by modeling the interactions of users as a graph. By contrast, most previous approaches use statistics such as packet sizes and inter-packet delays. We show how our approach gives rise to novel and powerful ways to: (a) visualize the traffic, (b) model the behavior of applications, and (c) detect abnormalities and attacks. Extending this approach, we develop ENTELECHEIA, a botnet-detection method. Tests with real data suggests that our graph-based approach is very promising.

Second, we present, MyPageKeeper, a security Facebook app, with 13K downloads, which we deployed to: (a) quantify the presence of malware on Facebook, and (b) protect end-users. We designed MyPageKeeper in a way that strikes the balance between accuracy and scalability. Our initial results are scary and interesting: (a) malware is widespread, with 49% of our users are exposed to at least one malicious post from a friend, and (b) roughly 74% of all malicious posts contain links that point back to Facebook, and thus would evade any of the current web-based filtering approaches.

Bio:
Michalis Faloutsos is a faculty member at the Computer Science Dept. at the University of California, Riverside. He got his bachelor's degree at the National Technical University of Athens and his M.Sc and Ph.D. at the University of Toronto. His interests include, Internet protocols and measurements, peer-to-peer networks, network security, BGP routing, and ad-hoc networks. With his two brothers, he co-authored the paper on power-laws of the Internet topology, which received the ACM SIGCOMM Test of Time award. His work has been supported by many NSF and military grants, for a cumulative total of more than $6 million. Several recent works have been widely cited in popular printed and electronic press such as slashdot, ACM Electronic News, USA Today, and Wired. Most recently he has focused on the classification of traffic and web-security, and co-founded a cyber-security company founded in 2008, offering services as www.stopthehacker.com, which received two SBIR grants from the National Science Foundation, and institutional funding in Dec 2011.

Risk Scoring and Future Directions

Date: Thursday, February 2, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Joseph R. Barr
Chief Scientist at ID Analytics

Part 1: ID Analytics main business is scoring applications (for credit/services) for risks including identity/ authenticity & credit. By definition an application is a vector of identity elements (SSN, Name, Address, Phone, DOB, <more>), a vector known as .SNAPD., as well as additional fields. ID Analytics process the data, extract pertinent features and calculate risk score on the fly. The entire process has a sub-second latency. At the basis of our analytics is the ID Network – a virtual graph with SNAPD-vectors as nodes. One can envision making a connection between two nodes if they share some identity element. The weight of the edge is the strength of the connection. As one can imagine various graphical parameters are the predominant inputs to our risk models. At the time I write this, the ID network has 1.5 billion nodes (corresponding to number of transactions); this of course means that the graph is too large to be stored in memory, and needless to say, how we do it is a trade secret, but I will indicate some principles behind the ideas.

Part 2: The risk ID Analytics is scoring falls under the more general rubric of consumer behavior. We are interested in the spatial / temporal aspects of our network and how it related to macroeconomic and social data including demographics, geography, housing, census, interest rates, unemployment, federal deficit, foreign balance of trade and whatnot. Under certain conditions, we will avail our data to an outside organization to participate in publishable research.

Introducing id: a labs, a research-oriented organization which promotes collaborations with academia and other research institutions.

Bio:
Joseph R. Barr is the Chief Scientist at ID Analytics (www.idanalytics.com). After a few years in academia (as Math/CS Assistant Professor at California Lutheran University,) he has spent the past 17 years in industry as a risk & consumer behavior (analytics) professional. He was awarded a Ph.D. in mathematics from the University of New Mexico on his work on graph colorings, under the direction of Professor Roger C. Entringer. His current interests include the application of statistics, machine-learning and combinatorial algorithms to risk management and consumer behavior. Joe is married, has two young children, a boy and a girl, and an older son, a software engineer at Intel.

SCR: The Scalable Checkpoint/Restart Library

Date: Thursday, January 26, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Kathryn Mohror
Lawrence Livermore National Lab

Applications running on high-performance computing systems can encounter mean times between failures on the order of hours or days. Commonly, applications tolerate failures by periodically saving their state to checkpoint files on reliable storage, typically a parallel file system. Writing these checkpoints can be expensive at large scale, taking tens of minutes to complete. To address this problem, we developed the Scalable Checkpoint/Restart library (SCR). SCR is a multi-level checkpointing library; it checkpoints to storage on the compute nodes in addition to the parallel file system. Through experiments and modeling, we show that multi-level checkpointing benefits existing systems, and we find that the benefits increase on larger systems. In particular, we developed low-cost checkpoint schemes that are 100x-1000x faster than the parallel file system and effective against 85% of our system failures. Our approach improves machine efficiency up to 35%, while reducing the load on the parallel file system by a factor of two.

Bio:
Kathryn Mohror is a Postdoctoral Research Staff Member at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. Kathryn.s research on high-end computing systems is currently focused on scalable fault tolerant computing and performance measurement and analysis. Her other research interests include scalable automated performance analysis and tuning, parallel file systems, and parallel programming paradigms. Kathryn received her Ph.D. in Computer Science in 2010, an M.S. in Computer Science in 2004, and a B.S. in Chemistry in 1999 from Portland State University in Portland, OR.

Graph and Structure Inference for Scientific Data Mining

Date: Thursday, January 19, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218

Terran Lane
UNM Department of Computer Science

Many modern scientific phenomena are best described in terms of graphs. From social networks to brain activity networks to genetic networks to information networks, attention is increasingly shifting to data that describe or originate in graph structures. But because of nonlinearities and statistical dependencies in graphical data, most "traditional" statistical methods are not well suited to such data. Coupled with the explosion of raw data, stemming from revolutions inscientific measurement equipment, domain scientists are facing steep challenges in statistical inference and data mining.

In this talk, I will describe work that my group has been doing on the identification of graph structure from indirect data. This problem is very familiar to the machine learning community, where it is known to be both computationally and statistically challenging, but has received substantially less attention in a number of scientific communities, where it is of substantial practical interest. I will examine an approach to graph structure inference that roots into the topology of graph structure space. By imposing metric structure on this otherwise unstructured set, we can develop fast, efficient, accurate inference mechanisms. I will explain our approach and illustrate the core idea and variants with examples drawn from neuroscience and genomics and introduce recent results on malware identification.

Bio:
Terran Lane is an associate professor of computer science at UNM. His personal research interests include behavioral modeling and learning to act/behave (reinforcement learning), scalability, representation, and the tradeoff between stochastic and deterministic modeling. All of these represent different facets of his overall interest in scaling learning methods to large, complex spaces and using them to learn to perform lengthy, complicated tasks and to generalize over behaviors. While he attempts to understand the core learning issues involved, he often situates his work in domain studies in practical problems. Doing so both elucidates important issues and problems for the learning community and provides useful techniques to other disciplines.

Colloquia Archives