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 requirements for Spring 2012.
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.
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.
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.
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
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
Date: Tuesday, February 21, 2012
Time: 11:00 am — 12:15 pm
Place: Mechanical Engineering 218
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.
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.
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.
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.
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.
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.
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.
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