CSE Undergrad News

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.

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

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

Overview:
2/5: Isilon Tech Talk
2/6: eBay Tech Talk
2/7: Zillow Tech Talk

Isilon Tech Talk
2/5; 6:00 – 7:00pm; EEB 105

EMC Isilon will be coming to UW! Jason Behmer, EMC Isilon Software Engineer and UW Alum, will lead a tech talk about High Performance File Systems and answer your questions about engineering careers with EMC in Seattle. Brilliant engineering discussion with a side of pepperoni. Join us!

eBay Tech Talk: Algorithmic Game Theory of eBay’s Buyer-Seller Matching
2/6; 6:00 – 7:00pm; EEB 125

Speaker: Kamal Jain, Distinguished Research Scientist

Kamal is a scientist interested in the interface of technology and business innovation. He earned his doctorate from Georgia Tech in 2000 in areas consisting of computer science, mathematics, & operation research. He is well known in both academic community and technology industry. He has published 75+ research papers in top notch conferences and journals. One of his papers inspired a book on iterative methods in algorithms. He has also published 100+ patents, a third of which have already been granted. He enjoys mentoring. He has mentored dozens of PhD students, most of whom are now at top universities & companies.

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

Please join David Beitel, CTO, and Garrett McAuliffe, VP, as they talk about Zillow’s technology.

David Beitel – Chief Technology Officer As CTO, David manages the Technical Engineering team and is responsible for all website, mobile and internal product development, corporate IT, and datacenter operations. One of the initial members of the Expedia team, David held many leadership roles in product development during his 10-year tenure there. Before his Expedia years, David worked at Microsoft in the handheld computing group. David earned a bachelor’s degree in computer science and a master’s of engineering degree in computer science from Cornell University.

Garrett McAuliffe – Vice President – Engineering Garrett joined Zillow in 2005 and currently leads the Mobile Technology strategy for Zillow. He’s worked in the Consumer Internet space as a technology leader for companies like Expedia, Inc. and Microsoft. He received his undergraduate degree from the University of California, Irvine in Computer Science.

Zillow’s mission is to empower people to make smart home-related decisions.

Overview:
1/28: VMware Open House
1/28: Google Tech Talk: Inside Google X
1/29: Redfin Tech Talk: Where Ideas Come From
1/30: Mock Technical Interviews
1/31: Amazon Tech Talk

VMware Open House
1/28; 3:00 – 5:00pm; Seattle office

Attention CS students! Please join us for an open house at VMware’s local Seattle office!

Interested in checking out VMware’s Seattle location? If so, sign up for an opportunity to meet with a few of our R & D leaders and Seattle employees on-site and learn more about our full-time and intern opportunities for students. Interested in meeting with us?

You must have RSVP’d to this event by January 24. Note: you will need to arrange your own transportation/carpool to our location.

Google Tech Talk: Inside Google X
1/28; 6:00 – 7:30pm; EEB 125

UW CSE Ph.D. alum Adrien Treuille, a faculty member at Carnegie Mellon on leave at Google X, will begin by providing an overview of activities at Google X.

Nick Hobbs, a graduate of Olin College and a PM at Google X, will discuss the Driverless Car Project.

Plan on roughly 60 minutes of presentation and 30 minutes of Q&A.

RSVP here to ensure we order enough food: http://news.cs.washington.edu/2013/01/16/google-x-talks-at-uw-cse-january-28-6-p-m/

Redfin Tech Talk: Where Ideas Come From
1/29; 6:00 – 7:00pm; EEB 105

Every new company starts with one thing: an idea. how do you build an idea that’s sustainable and marketable enough to become a successful business?

Redfin CEO Glenn Kelman is coming to discuss how to plant the seed that will grow a thriving company. With useful tips for starting your own business and unlikely suggestions for drumming up ideas, you’ll learn how to tell the difference between total genius and total flop, so you know when to go all-in or walk away.

Glenn founded Plumtree Software in 1997, staying through its 2002 IPO. He now runs Redfin, one of Seattle’s premier pre-IPO consumer internet companies with $7 billion in real estate sales. His essays have appeared in the New York Times, TechCrunch and The Wall Street Journal; he was featured on “60 Minutes” and “Today.”

Everyone is welcome. Sushi provided. redfin.com/college

Mock Technical Interviews
1/30; 6:00 – 8:15pm; Atrium

Mock technical interviews afford students the opportunity to participate in a one-on-one simulated interview. Engineers, hiring managers, and other technical employees of local software companies conduct the technical interviews which consist of whiteboard questions, problem solving puzzles and coding questions.

Interviews last 30 minutes with an additional 10 minutes allotted for interviewers to give the students honest feedback on how they did and what they can do to improve their technical interview success.

Amazon Tech Talk: Amazon.com Personalization: Item to Item Collaborative Filtering
1/31: 6:00 – 7:15pm; EEB 105

Brent Smith- SDE Sr. Manager and Director of Personalization

Amazon is hiring Software Development Engineers for: Internships, Co-ops, and Full-time

We’ll be giving away Amazon swag and free food!