CSE Undergrad News

Don’t forget! Join us for some free smoothies and a movie afterwards! We’ll be watching Up.

Wind down on this Friday afternoon by joining us in the Atrium at 3:00pm. We’ll head to EEB 045 afterwards for the movie.

NSF REU Summer Program

Software Safety: Methodology, Practice and Research

May 28 – August 2, 2013

http://paris.utdallas.edu/reu

Application deadline: March 15, 2013

The Computer Science Department at the University of Texas at Dallas
will conduct a ten-week summer research program funded by the National
Science Foundation (NSF) for ten undergraduates from May 28 to August 2,
2013. The focus is on ” software safety ,” however, the technology and
skills learned by the students have general applicability to research
and practice in their future studies.

We provide a close collaboration with our industry partners. In addition
to work on assigned research projects at UTD, field trips to Raytheon,
Lockheed Martin Aeronautics Company, and EDS/HP will be arranged to help
the students better understand how software safety is adopted in practice
for real-life applications. This also gives students a chance to directly
communicate with practitioners to receive a first-hand account of the
work environments and lifestyles in the industry.

We also host special workshops on technical writing and oral presentation
to improve students’ proficiency in preparing and delivering technical
reports. A highlight which provides an overview of our program including
research projects, lectures, and field trips is available at YouTube
( http://www.youtube.com/watch?v=1kRIUNotUdQ ). In addition, videos (along
with PowerPoint slide shows) for students’ project presentations as well
as lectures on software safety and technical writing are also posted
( http://paris.utdallas.edu/reu/Video.html ).

Eligible applicants are th ose who have an interest in pursuing a graduate
education, are currently enrolled in an accredited undergraduate institution,
and are U.S. citizens or permanent residents. Incoming seniors will be
given preference, but sophomores and juniors with strong academic records
will also be considered. Special welcome is extended to students from
underrepresented groups (women, minorities, and persons with disabilities)
and academic institutions with limited research opportunities. Selected
participants will receive a scholarship to conduct research with a faculty
member in the Computer Science Department at UTD. Included will be
a $5,000 stipend ($500/week) and a travel reimbursement for those from
outside the Dallas metro area. We also provide housing on campus free to
the REU students.

Eric Wong
Professor & Principal Investigator
Department of Computer Science
University of Texas at Dallas

Email ewong@utdallas.edu
Web: http://www.utdallas.edu/~ewong

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.

Overview:
2/11: a16z Tech Talk
2/12: Microsoft Tech Talk
2/13: Hulu Tech Talk
2/14: Google Office Hours
2/14: Amazon Programming Contest
2/15: ACM Smoothie Day & Movie Night

a16z Tech Talk (Ft. Julep)
2/11; 6:00 – 7:00pm; EEB 125

Join in our tech talk with founders and key engineers from Julep. Come engage in an interactive session to learn about their backgrounds, challenges faced at startups, and the technology around their product! Dessert will be provided.

RSVP: jessica@a16z.com

Microsoft Tech Talk
2/12; 6:00 – 7:00pm; EEB 105

Tech Talk by Ales Holecek, Corporate Vice President leading the Windows Developer Experience

Get the insider’s view of developing Windows 8. Learn about industry and competitive trends and how they shaped Microsoft’s decision making throughout the development process. You will see how these decisions are reflected in the platform all the way down to the API level.

Ales Holecek joined Microsoft in 2006. He is leading a team responsible for the Windows App Platform. Prior to that he was a Distinguished Engineer and the Director of Development for the Windows Experience for Windows 7.

Enjoy free food and bring your resume for a chance to win an Xbox/Kinect Bundle and a Surface!

Hulu Tech Talk
2/13; 6:00 – 7:00pm; EEB 125

Google Office Hours
2/14; 12:00 – 1:00pm; Atrium

Amazon Programming Competition
2/14; 6:00 – 9:00pm; Atrium

Prizes:
1st = $200 Amazon Gift Card
2nd= $100 Amazon Gift Card
3rd= $50 Amazon Gift Card

Sign Up Here: https://catalyst.uw.edu/webq/survey/apacible/191550

Sample Problems, Rules, What to Bring can be found at: http://programmingcontest.net/

Limited to 50 participants so register soon!

If you have any questions or comments, please contact Jennifer Apacible at apacible[at]cs[dot]washington[dot]edu.

ACM Smoothie Day & Movie Night
2/15; 3:00 – 6:00pm; Atrium & EEB 045

Join ACM for some free smoothies to end your week! Afterwards, we’ll be watching Up.

Thank you to everyone who responded to our movie suggestions survey. If you have any other suggestions for the future, please let us know! We’ll be looking through the list of all submissions before each movie night.

Survey Link: https://catalyst.uw.edu/webq/survey/apacible/191898

As part of Google’s ongoing commitment to advancing computing and technology, we are pleased to provide scholarships to students studying computer science.  Google’s scholarship programs award students from diverse backgrounds who are studying computer science to encourage them to excel in their studies and become active role models and leaders in the field. If selected, recipients will each receive a $10,000 scholarship for the 2013-2014 academic year and be invited to attend the all-expense paid Annual Google Scholars’ Retreat at a Google office this summer.  Scholarships will be awarded based on the strength of applicants’ academic background and demonstrated passion for computer science.

Please see below for a list of open scholarships and upcoming deadlines:

Google Anita Borg Scholarship – Deadline to apply is February 18, 2013

Google Lime Scholarship – Deadline to apply is February 18, 2013

Google HSF Scholarship – Deadline to apply is March 4, 2013

Google UNCF Scholarship – Deadline to apply is March 31, 2013

Google SVA Scholarship for student veterans – Deadline to apply is March 31, 2013

Google AISES Scholarship – Deadline to apply is March 31, 2103

For complete details on all of our scholarships, please visit www.google.com/students/scholarships.

Hello,

Please join the eScience Institute Wednesday, February 13, 4:00 pm in EEB-303.  Refreshments will be provided.

Gary M. Johnson (Computational Science Solutions)

Dr. Johnson specializes in: management of high performance computing, applied mathematics, and computational science research activities; advocacy, development, and management of high performance computing centers; development of national science and technology policy; and creation of education and research programs in computational engineering and science.

He has worked in Academia, Industry and Government.  He has held full professorships at Colorado State University and George Mason University, been a researcher at United Technologies Research Center, and worked for the Department of Defense, NASA, and the Department of Energy.

He is a graduate of the U.S. Air Force Academy; holds advanced degrees from Caltech and the von Karman Institute; and has a Ph.D. in applied sciences from the University of Brussels.

Waiting for Exascale

By current estimates, we’re about a decade away from having exascale computing capability.  That’s a pretty long time – especially in our world of HPC.  What will the world be like in 2023?  What form will exascale computing take when it’s real?  These are difficult questions to answer.  Never before has the HPC community focused so intensely on a machine so far beyond its grasp.  Nevertheless, stalwart cadres around the globe are drafting strategies, plans, and roadmaps to get from here to exascale.  So, what about the rest of us?  Are there useful things we could do while waiting – or instead of waiting – for exascale?  Perhaps there are.  In this talk we’ll take a look at a few possibilities, including:

·         Education

·         eScience

·         Big Data

·         Broad HPC Deployment

·         Computing in Industry

·         Public Engagement

·         Infrastructure Development and Build Out

·         Success Metrics

Exascale computing may be a decade away, but there’s a lot to accomplish to be ready to exploit it.  We’ll explore a few options here.  We make no claim that these constitute the right agenda for the coming decade – nor do we suggest that we’ve given an exhaustive to-do list.  Our intention is rather to open the conversation about what we should do while “waiting” for exascale.

So, come to the talk and let us know what you think.

Upcoming Seminars:

* March 13, 4 PM (EE303)

Carlos Guestrin  (UW)

GraphLab: Making Fast Machine Learning on Big Data Accessible to Data Scientists

* April 11, 4 PM (EE303)

Barry Wark  (Physion Consulting)

TBD

* May 1, 4 PM (EE303)

Jeff Gardner  (UW)

Simulating the Universe on Google’s Exacycle Platform

* May 13, 4 PM (EE303)

Fernando Perez  (Berkeley)

TBD

ACM is planning some upcoming movie nights and we’d like to hear what you want to watch!

Please submit your suggestions here: https://catalyst.uw.edu/webq/survey/apacible/191898