CSE Undergrad News

Hello,

Please join the eScience Institute Wednesday, May 1, 4:00 pm in EEB-303.  Refreshments will be provided. 

Jeff Gardner (UW Physics)

Jeff Gardner is Director of Research for Physical Sciences at the eScience Institute, Affiliate Assistant Professor in the Physics and Astronomy departments, and Visiting Faculty at Google, Inc.  Jeff received his PhD in Astronomy from UW in 2000.  In 2003, he become a Sr. Research Scientist at the Pittsburgh Supercomputing Center, where he participated in the deployment of the NSF TeraGrid (Extensible Terascale Facility; ETF), which became the largest open platform for scientific computing in the world. His research has focused on the overcoming the challenges of analyzing extremely large scientific datasets using a variety of approaches, including scalable DBMSs, MapReduce, as well as domain-specific libraries.  He is also actively involved in building the next generation of computational astrophysics codes capable of sustaining a petaflop (1 thousand trillion mathematical operations per second) and generating petabytes of data.

Simulating the Universe on Google’s Exacycle Platform

The Large Synoptic Survey Telescope (LSST; http://www.lsst.org ) is one of the most ambitious astrophysical research programs ever undertaken.   From the 9,000 ft Cerro Pachon peak in Northern Chile, the LSST’s 3.2 Gigapixel camera will repeatedly survey the southern sky, taking one image every 15 seconds, generating tens of petabytes of data every year.  The images and catalogs from the LSST have the potential to transform both our understanding of the universe and the way that we undertake science.  As part of the implementation phase of this project, the LSST collaboration has undertaken a formidable program to simulate the flow of data from the telescope.  The image simulator traces individual photons of light from stars, galaxies, asteroids, through the earth’s atmosphere, the telescope optics, and onto the detector.  These simulations are used to optimize how the LSST surveys the sky, to develop the analytics required to understand how the universe forms and evolves, and to determine how astronomers (and the public as a whole) will scale science to data sets that will exceed a hundred petabytes in size.  For over a year now, Google has given LSST access to their Exacycle platform in order to perform these simulations (http://research.google.com/university/exacycle_program.html), reducing the time required to simulate one night of LSST observing, roughly 5 million images, from 3 months down to a few days.  This rapid turnaround enables the LSST engineering teams to test new designs and new algorithms with unprecedented precision, which will ultimately lead to bigger and better science.

Upcoming Seminars:

* May 13, 4 PM (EE303)

      Fernando Perez  (Berkeley)

               TBD

* May 22, 4 PM (EE303)

      Joe Hellerstein  (Berkeley)

             Why Computer Scientists Should and Can Learn Computer Science

If you want to receive emails about the various talks going on in the department, you should subscribe to the talks list.

https://mailman.cs.washington.edu/mailman/listinfo/talks

We generally will not post reminders here, but do towards the beginning of each quarter. Also, remember that you can sign in to the blog to set preferences on the types of message you receive.

Crystal

Information about upcoming Colloquia sponsored by the University of Washington, Department of Computer <talks@cs.washington.edu>	Mar 28 (4 days ago)
to cs-ugrads

Intriguing title on this one…!

UNIVERSITY OF WASHINGTON
Computer Science and Engineering
COLLOQUIUM

SPEAKER:   Shayan Oveis Gharan, Stanford University

TITLE:     New Approximation Algorithms for Traveling Salesman Problem

DATE:      Thursday, April 4, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOST:      Anup Rao

ABSTRACT:
TSP is a central and perhaps one of the most well-known problems in
theoretical computer science. Due to its combination of simplicity, appeal
to imagination, and intractability, TSP has attracted the attention of
mathematicians and computer scientists for decades. Despite this
attention, the best approximation algorithm known for TSP goes back to
1976. In his unpublished manuscript, Christofides presented a simple
3/2-approximation algorithm for the problem.

In a joint work with Saberi and Singh, we design a new approximation
algorithm for a canonical special case of the TSP known as graphic TSP.
This algorithm finally breaks the 3/2 barrier by a very small constant.
Our algorithm employs a new technique for rounding the optimum fractional
solution of linear programming relaxations of combinatorial optimization
problems, called the rounding by sampling method.  Our analysis builds on
recent developments in probability theory on properties of strongly
Rayleigh measures, as well as new insights from combinatorics and
polyhedral theory. As a byproduct of our result, we show new properties of
near minimum cuts of any graph, which may be of independent interest.

Bio:
Shayan Oveis Gharan is currently finishing his PhD at Stanford University
under the supervision of Amin Saberi. Prior to Stanford, Shayan received a
BA in computer engineering from Sharif University of Technology. His
research interests include Approximation Algorithms, Spectral Algorithms,
Online Algorithms and Applied Probability. He is a recipient of several
awards including best paper award at SODA 2010 and FOCS 2011 for his works
on the Traveling Salesman Problem, Stanford Graduate Fellowship, and the
Miller Fellowship.

Refreshments to be served in room prior to talk.

*NOTE* This lecture will be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

The University of Washington is committed to providing access, equal
opportunity and reasonable accomodation in its services, programs,
activities, education and employment for individuals with disabilities.
To request disability accommodation, contact the Disability Services
Office at least ten days in advance of the event at: (206) 543-6450/V,
(206) 543-6452/TTY, (206) 685-7264 (FAX), or email at
dso@u.washington.edu.
______________________________

Hello!

We would like to invite you and your students to our next webinar highlighting Machine Learning, Natural Language Process, and Artificial Intelligence. This webinar will feature a presentation by two Microsoft employees solving the complex problems in search. Learn about what recent college hires are working on, as well as how Microsoft is approaching the world of search on a daily basis. By tuning in live, you will have the opportunity to ask questions and interact with our presenters in real-time.

Upcoming Webinar – “Be Smarter Than Your Computer: Machine Learning, Natural Language Processing & Artificial Intelligence”

Wednesday, March 13^th @ 12:00 PM (PST)

Register to attend at http://bit.ly/URwebinarAI1

Thursday, March 21^st @ 6:00 PM (PST)

Register to attend at http://bit.ly/URwebinarAI2

Do I need to call in to get audio?  Yes, because of the high volume of callers and varying internet connections we have found it is more reliable to call in for audio.  Toll Number: 213-416-1560

Access Code: 240-746-226.  International call in recommendations are listed in the attached flyer.

Are you calling in internationally? If so, we recommend VOIP so you can access the audio. If you’re using a landline, cell phone, Skype, Nonoh, or a similar program, please make sure to dial 00-1 before dialing the number above. If you continue to have problems signing in, please note that we will be posting these webinars online here!

Overview:
3/7: Madrona Start Up Office Hours

Madrona Start Up Office Hours
3/7; 11:00 – 12:30pm; Atrium

Startup Office Hours is your time to come and discuss anything startup-related. Hakon Verespej, from Madrona Venture Group, will be available.

You are welcome to come and learn more about local startups, talk about a startup idea you have, get feedback on a project you’re working on, have your resume reviewed, or anything else you have on your mind.

Hakon can also be reached at hakon@madrona.com for questions regarding the office hour or anything else he can help with.

This week on Tuesday…

UNIVERSITY OF WASHINGTON
Joint Computer Science and Engineering and Statistics
COLLOQUIUM

SPEAKER:   Ben Recht, University of Wisconsin, Madison

TITLE:     How to make predictions when you’re short on information

DATE:      Tuesday, February 26, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOST:      Carlos Guestrin

ABSTRACT:
With the advent of massive social networks, exascale computing, and
high-throughput biology, researchers in every scientific department now
face profound challenges in analyzing, manipulating and identifying
behavior from a deluge of noisy, incomplete data. In this talk, I will
present a unifying framework to make such data analysis tasks less
sensitive to corrupted and missing data by exploiting domain specific
knowledge and prior information about structure.

Specifically, I will show that when a signal or system of interest can be
represented by a combination of a few simple building blocks—called
atoms—it can be identified with dramatically fewer sensors and
accelerated acquisition times. For example, a few principal factors can
determine preferences across a user-base, a small number of genes may
constitute the signature of a disease, and a sum of a few permutations can
summarize the ranking of sports teams. In each application, the challenge
lies not only in defining the appropriate set of atoms, but also in
estimating the most parsimonious combination of atoms that agrees with a
small set of measurements.

This talk advances a framework for transforming notions of simplicity and
latent low-dimensionality into convex optimization problems.  My approach
builds on the recent success of generalizing compressed sensing to matrix
completion, creating a unified framework that greatly extends the catalog
of objects and structures recoverable from partial information.  This
framework provides a standardized methodology to sharply bound the number
of observations required to robustly estimate a variety of structured
models.   It also enables focused algorithmic
development that can be deployed in many different applications, a variety
of which I will detail in this talk.  I will close by demonstrating how
this framework provides the abstractions necessary to scale these
optimization algorithms to the massive data sets we now commonly acquire.

Bio:
Benjamin Recht is an Assistant Professor in the Department of Computer
Sciences at the University of Wisconsin-Madison and holds courtesy
appointments in Electrical and Computer Engineering, Mathematics, and
Statistics.  He is a PI in the Wisconsin Institute for Discovery (WID), a
newly founded center for research at the convergence of information
technology, biotechnology, and nanotechnology. Ben received his B.S. in
Mathematics from the University of Chicago, and received a M.S. and PhD
from the MIT Media Laboratory. After completing his doctoral work, he was
a postdoctoral fellow in the Center for the Mathematics of Information at
Caltech. He is the recipient of an NSF Career Award, an Alfred P. Sloan
Research Fellowship, and the 2012 SIAM/MOS Lagrange Prize in Continuous
Optimization.

Refreshments to be served in room prior to talk.

This lecture will be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

On Thursday of this week…

UNIVERSITY OF WASHINGTON
Computer Science and Engineering
COLLOQUIUM

SPEAKER:   Ari Juels, Chief Scientist, RSA

TITLE:     Aggregation and Distribution in Cloud Security

DATE:      Thursday, February 28, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOST:      Tadayoshi Kohno

ABSTRACT:
Cloud computing and virtualization, a key supporting technology, offer
flexibility and agility in the placement of resources. Certain risks,
however, arise from cloud services’ tendency to aggregate sensitive data
and workloads. I’ll discuss side-channel attacks resulting from the co-
location of disparate tenants’ virtual machines (VMs) on hosts and the
vulnerabilities posed by databases aggregating the authentication secrets,
e.g., password hashes, of numerous users. Conversely, cloud computing
offers new opportunities to distribute data. I’ll describe a new,
research-driven RSA product that splits sensitive data across systems or
organizations, removing the single points of compromise that otherwise
naturally arise in cloud services.

Bio:
Dr. Ari Juels is Chief Scientist of RSA, The Security Division of EMC, and
Director of RSA Laboratories. He joined RSA in 1996.

Refreshments to be served in room prior to talk.

This lecture will be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

Overview:
2/19: Context Relevant Tech Talk
2/20: Simply Measured Tech Talk
2/21: Madrona Startup Office Hours
2/21: Google Office Hours
2/21: Apptio Tech Talk

Context Relevant Tech Talk
2/19; 6:00 – 7:00pm; EEB 105

Simply Measured Tech Talk: From Clueless to Crushing It
2/20; 6:00 – 7:00pm; EEB 125

How to go from having no clue what to do, to crushing the analytics reporting market. Who we are, what we do, and the Big Data and Engineering problems we deal with.

Short presentation followed by open Q&A with Director of Engineering, Colin Henry.

www.simplymeasured.com/careers

Simply Measured is actively Hiring:
• Software Engineers
• Product Engineers
• Ops Engineers
• QA Engineers

Madrona Startup Office Hours
2/21; 11:00am – 12:30pm; Atrium

Startup Office Hours is your time to come and discuss anything startup-related. Hakon Verespej (http://www.linkedin.com/in/verespej), from Madrona Venture Group, will be available Thursdays from 11am to 12:30pm in the atrium of the Paul G. Allen Center. You are welcome to come and learn more about local startups, talk about a startup idea you have, get feedback on a project you’re working on, have your resume reviewed, or anything else you have on your mind. Hakon can also be reached at hakon@madrona.com for questions regarding the office hour or anything else he can help with.

Google Office Hours
2/21; 12:00 – 1:00pm; Atrium

Apptio Tech Talk
2/21; 6:00 – 7:00pm; EEB 105

Come join the team!

Voted as one of The Best Places to Work in Washington State, Apptio is looking for the best and the brightest to join our software engineering team!  At Apptio, we have a true sense of community built on collaboration, innovation and fun! What you will do at Apptio matters and will have a direct impact on our customers and our business.  Bring your passion for coding, pack your favorite Nerf Gun and join us as we grow to be the next enterprise level software company in the Puget Sound!

Current Job openings at Apptio
•    Interns (SDE and SDET)
•    Application Engineer
•    Technical Support Analyst
•    Software Development Engineer (Applications, Platform, Data)
•    Software Development Engineer in Test

Next Thursday – not broadcast, not taped…

UNIVERSITY OF WASHINGTON
Computer Science and Engineering

COLLOQUIUM

SPEAKER:   Chris Re, University of Wisconsin, Madison

TITLE:     Making Applications that use Statistical Analysis Easier to
Build and Maintain

DATE:      Thursday, February 21, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOST:      Magdalena Balazinska

ABSTRACT:
NOTE:  This talk will NOT be broadcast live, and will not be taped except
for internal CSE use!

The question driving my work is: How should one deploy statistical data-
analysis tools to enhance data-driven systems? Even partial answers to
this question may have a large impact on science, government, and
industry—each of whom are increasingly turning to statistical techniques
to get value from their data.

To understand this question, my group has built or contributed to a
diverse set of data-processing systems: a system called GeoDeepDive that
reads and answers questions about the geology literature and is used by
geologists to gain insights into the Earth’s carbon cycle; a muon filter
that is used in the IceCube neutrino telescope to process over 250 million
events each day in the hunt for the origins of the universe; and a host of
enterprise analytics applications with Oracle and EMC/Greenplum. Even
within this diverse set, we have found common abstractions, which can be
used to build and maintain such systems in a more cost-effective way. In
this talk, I will describe some of these abstractions along with the
theoretical and algorithmic questions that they raise. Finally, I will
describe my vision of how and why classical data management will continue
to play an important role in the age of statistical data analysis.

Papers, software, virtual machines that contain installations of our
software, links to applications that are discussed in this talk, and our
list of collaborators are available from http://www.cs.wisc.edu/hazy
We also have a YouTube channel:
http://www.youtube.com/HazyResearch  with videos about our projects.

Bio:
Christopher (Chris) Re is an assistant professor in the department of
computer sciences at the University of Wisconsin-Madison. The goal of his
work is to enable users and developers to build applications that more
deeply understand and exploit data. Chris received his PhD from the
University of Washington in Seattle under the supervision of Dan Suciu.
For his PhD work in probabilistic data management, Chris received the
SIGMOD 2010 Jim Gray Dissertation Award. Chris’s papers have received four
best-paper or best-of-conference citations, including best paper in PODS
2012, best-of-conference in PODS 2010 twice, and one best-of- conference
in ICDE 2009). Chris received an NSF CAREER Award in 2011.

Refreshments to be served in room prior to talk.

*NOTE* This lecture will be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

The University of Washington is committed to providing access, equal
opportunity and reasonable accomodation in its services, programs,
activities, education and employment for individuals with disabilities.
To request disability accommodation, contact the Disability Services
Office at least ten days in advance of the event at: (206) 543-6450/V,
(206) 543-6452/TTY, (206) 685-7264 (FAX), or email at
dso@u.washington.edu.

On Thursday of this week:

UNIVERSITY OF WASHINGTON
Computer Science and Engineering
COLLOQUIUM

SPEAKER:   Nima Mesgarani, U of Maryland/U of California, San Francisco

TITLE:     Reverse engineering the brain computations involved in speech
production and perception

DATE:      Thursday, February 14, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOSTS:      Les Atlas and Rajesh Rao

ABSTRACT:
NOTE:  No live broadcast or on-demand or future UWTV taping!  This will be
taped for internal use only!

The brain empowers humans and other animals with remarkable abilities to
sense and perceive their acoustic environment in highly degraded
conditions. These seemingly trivial tasks for humans have proven extremely
difficult to model and implement in machines. One crucial limiting factor
has been the need for a deep interaction between two very different
disciplines, that of neuroscience and computer engineering. In this talk,
I will present results of an interdisciplinary research effort to address
the following fundamental
questions: 1) what computation is performed in the brain when we listen to
complex sounds? 2) How could this computation be modeled and implemented
in computational systems? and 3) how could one build an interface to
connect brain signals to machines? I will present results from recent
invasive neural recordings in human auditory cortex that show a
distributed representation of speech in auditory cortical areas.
This representation remains unchanged even when an interfering speaker is
added, as if the second voice is filtered out by the brain.<p> </p> In
addition, I will show how this knowledge has been successfully
incorporated in novel automatic speech processing applications and used by
DARPA and other agencies for their superior performance.<p> </p> Finally,
I will demonstrate how speech can be read directly from the brain that
eventually, can allow for communication by people who have lost their
ability to speak. This integrated research approach leads to better
scientific understanding of the brain, innovative computational
algorithms, and a new generation of Brain-Machine interfaces.

Refreshments to be served in room prior to talk.

*NOTE* This lecture will be NOT be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

The University of Washington is committed to providing access, equal
opportunity and reasonable accomodation in its services, programs,
activities, education and employment for individuals with disabilities.
To request disability accommodation, contact the Disability Services
Office at least ten days in advance of the event at: (206) 543-6450/V,
(206) 543-6452/TTY, (206) 685-7264 (FAX), or email at
dso@u.washington.edu.

Up this week on Tuesday:

UNIVERSITY OF WASHINGTON
Computer Science and Engineering
COLLOQUIUM

SPEAKER:   Pradeep Ravikumar, CMU/UT, Austin

TITLE:     Statistical Machine Learning and Big-p, Big-n, Complex Data

DATE:      Tuesday, February 12, 2013
TIME:      3:30pm
PLACE:     EEB-105
HOST:      Carlos Guestrin

ABSTRACT:
Drawing upon disparate fields as economics, psychology, operations
research and statistics, the subfield of statistical machine learning has
provided practically successful tools ranging from search engines to
medical diagnosis, image processing, speech recognition, and a wide array
of problems in science and engineering. However, over the past decade,
faced with modern data settings, off-the-shelf statistical machine
learning methods are frequently proving insufficient. These modern
settings pose three key challenges, which largely come under the rubric of
“Big Data”: (a) the data might have a large number of features, in what we
will call “Big-p” data, to denote the fact that the dimension “p” of the
data is large, or (b) the data might have a large number of data
instances, in what we will call “Big-n” data, to denote the fact that the
number of samples “n” is large, or (c) the data-types could be complex:
such as permutations, or strings, or graphs, which typically lie in some
large discrete space. A key approach in addressing such “Big Data”
settings has involved leveraging systems-related approaches such as
parallel and distributed algorithms, as well as architecture and
algorithms for efficient, possibly distributed, data access and storage.
In this talk, we will discuss the complementary approach of statistical
modeling, but which importantly is tuned to each of these three aspects of
modern statistical machine learning: big-p data, big-n data, and complex
data-types.

Statistical machine learning for Big-p data, with more variables than
samples, has been the focus of considerable research over the last decade.
It is now well understood that estimation with strong statistical
guarantees is still possible under such high-dimensional settings provided
we impose suitable constraints on the model space.
Accordingly, we will discuss a unified framework for learning general
structurally constrained high-dimensional models (such as models that are
sparse, low-rank, and so on). For Big-n data, a key sub-field that is
increasingly gaining importance is that of non-parametric models, where
the model components potentially lie in infinite-dimensional spaces. A key
caveat to the wide-spread use of these models has been the larger number
of observations required by these models as compared to parametric
methods, but this is much less of a problem in Big-n settings.
Accordingly, we will discuss a unified framework of structurally
constrained semi-parametric models (such as sparse additive models and so
on). For complex-typed data, even standard machine learning questions such
as devising suitable loss functions, and devising suitable statistical
models that respect interesting structure, are still outstanding. We will
address some of these questions for the specific complex data-type of
permutations.

Bio:
Pradeep Ravikumar received his B.Tech. in Computer Science and Engineering
from the Indian Institute of Technology, Bombay, and his PhD in Machine
Learning from the School of Computer Science at Carnegie Mellon
University. He was then a postdoctoral scholar at the Department of
Statistics at the University of California, Berkeley. He is now an
Assistant Professor in the Department of Computer Science, at the
University of Texas at Austin. He is also affiliated with the Division of
Statistics and Scientific Computation, and the Institute for Computational
Engineering and Sciences at UT Austin. His thesis has received honorable
mentions in the ACM SIGKDD Dissertation award and the CMU School of
Computer Science Distinguished Dissertation award. He is also a recipient
of the NSF CAREER Award.

Refreshments to be served in room prior to talk.

*NOTE* This lecture will be broadcast live via the Internet. See
http://www.cs.washington.edu/news/colloq.info.html for more information.

Email: talk-info@cs.washington.edu
Info: http://www.cs.washington.edu/
(206) 543-1695

The University of Washington is committed to providing access, equal
opportunity and reasonable accomodation in its services, programs,
activities, education and employment for individuals with disabilities.
To request disability accommodation, contact the Disability Services
Office at least ten days in advance of the event at: (206) 543-6450/V,
(206) 543-6452/TTY, (206) 685-7264 (FAX), or email at
dso@u.washington.edu.