Reminder about postings, you can sign up to receive notices of talks given in the department by going to: http://www.cs.washington.edu/events/colloquia/
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Here are a few talks coming up:
This Thursday – please join us if you can or watch remotely at
http://www.cs.washington.edu/events/colloq_info/
UNIVERSITY OF WASHINGTON
Computer Science and Engineering
*DISTINGUISHED LECTURE*
SPEAKER: David Patterson, UC Berkeley
TITLE: Myths about MOOCs, Ebooks, and Software Engineering Education
DATE: Thursday, September 26, 2013
TIME: 3:30pm
PLACE: EEB-105
HOST: Ed Lazowska
ABSTRACT:
This talk explains how the confluence of cloud computing and Massive Open
Online Courses (MOOCs) have allowed us greatly improve both the
effectiveness and the reach of UC Berkeley’s undergraduate software
engineering course.
The first part of the talk is motivated by Industry’s long-standing
complaint that academia ignores vital software topics, leaving students
unprepared upon graduation. Traditional approaches to software development
are neither supported by tools that students could readily use, nor
appropriate for projects whose scope matched a college course. Hence,
instructors traditionally lecture about software engineering topics, while
students continue to build software more or less the way they always had,
in practice relegating software engineering to little more than a project
course. This sad but stable state of affairs is frustrating to
instructors, boring to students, and disappointing to industry.
Happily, cloud computing and the shift in the software industry towards
software as a service has led to highly-productive tools and techniques
that are a much better match to the classroom than earlier software
development methods. That is, not only has the future of software been
revolutionized, it has changed in a way that makes it easier to teach.
UC Berkeley’s revised Software Engineering course leverages this
productivity to allow students to both enhance a legacy application and to
develop a new app that matches requirements of non-technical customers. By
experiencing the whole software life cycle repeatedly within a single
college course, students actually use the skills that industry has long
encouraged and learn to appreciate them. The course is now rewarding for
faculty, popular with students, and praised by industry.
The second part of the talk is about our experience using MOOCs to teach
Software Engineering. While the media’s spotlight on MOOCs continues
unabated, a recent opinion piece expresses grave concerns about their role
(“Will MOOCs Destroy Academia?”, Moshe Vardi, CACM 55(11), Nov. 2012). I
will try to bust a few MOOC myths by presenting provocative, if anecdotal,
evidence that appropriate use of MOOC technology can improve on-campus
pedagogy, increase student throughput while raising course quality, and
even reinvigorate faculty teaching. I’ll also explain the role of MOOCs in
enabling half-dozen universities to replicate and build upon our work via
Small Private Online Courses (SPOCs) from EdX and our electronic textbook.
I conclude that the 21st century textbook may prove to be a hybrid of
SPOCs and Ebooks.
Work in collaboration with Armando Fox, UC Berkeley.
Bio:
David Patterson joined UC Berkeley in 1977 after receiving all his degrees
from UCLA.
His most successful projects have likely been Reduced Instruction Set
Computers (RISC), Redundant Arrays of Inexpensive Disks (RAID), and
Network of Workstations (NOW). All three projects helped lead to
multibillion-dollar industries. This research led to many papers and six
books, with the most popular book being Computer Organization and Design
co-authored with John Hennessy and the most recent being Engineering
Software as a Service co-authored with Armando Fox. His current research
is centered on cancer genomics for the AMP and ASPIRE Labs.
In the past, he served as Director of the Parallel Computing Lab, Director
of the Reliable And Distributed Systems Lab, Chair of Berkeley’s CS
Division, Chair of the Computing Research Association (CRA), and President
of the Association for Computing Machinery (ACM).
This work resulted in 35 honors, some shared with friends. His research
awards include election to the National Academy of Engineering, the
National Academy of Sciences, and the Silicon Valley Engineering Hall of
Fame along with being named Fellow of the Computer History Museum, ACM,
IEEE, and both AAAS organizations. He received Distinguished Service
Awards from ACM, CRA, and SIGARCH. His teaching honors include the ACM
Karlstrom Outstanding Educator Award, the IEEE Mulligan Education Medal,
the IEEE Undergraduate Teaching Award, and the UC Berkeley Distinguished
Teaching 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.
____________________________________________
Hello,
Please join the eScience Institute Monday, September 30, 4:00 pm in EEB-303. Refreshments will be provided.
Orly Alter (Utah):
Orly Alter, Ph.D. is a USTAR Associate Professor of Bioengineering and Human Genetics at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She was awarded a National Science Foundation CAREER Award in 2009, and a National Human Genome Research Institute (NHGRI) R01 grant in 2007. She was selected to give the Linear Algebra and its Applications Lecture of the International Linear Algebra Society in 2005, and received an NHGRI Individual Mentored Research Scientist Development Award in Genomic Research and Analysis in 2000, and a Sloan Foundation/Department of Energy Postdoctoral Fellowship in Computational Molecular Biology in 1999. Additional support for her work comes from the Utah Science, Technology and Research (USTAR) Initiative.
Discovery of Principles of Nature from Matrix and Tensor Modeling of Large-Scale Molecular Biological Data
In my Genomic Signal Processing Lab, we are breaking new ground in mathematics, at the interface of mathematics, biology and medicine, and in biology and medicine. In mathematics, we develop generalizations of the mathematical frameworks that underlie the theoretical description of the physical world [1]. At the interface, we use these frameworks to create models that compare and integrate different types of large-scale molecular biological data. In biology and medicine, we use the models to computationally predict previously unknown physical, cellular and evolutionary mechanisms that govern the activity of DNA and RNA. We believe that future discovery and control in biology and medicine will come from the mathematical modeling of large-scale molecular biological data, just as Kepler discovered the laws of planetary motion by using mathematics to describe trends in astronomical data [2].
At the interface, our recent generalized singular value decomposition (GSVD) comparison of two patient-matched genomic datasets uncovered a global pattern of DNA aberrations that is correlated with, and possibly causally related to, brain cancer survival [3]. This new link between a glioblastoma multiforme (GBM) tumor’s genome and a patient’s prognosis offers insights into the cancer’s formation and growth, and suggests promising drug targets. The best prognostic predictor of GBM prior to this discovery was the patient’s age at diagnosis. In mathematics, the higher-order GSVD we formulated is the only framework to date that enables comparison of more than two patient-matched but probe-independent datasets, and, in general, more than two datasets arranged in matrices of the same column dimensions but different row dimensions [4]. In biology, our experiments [5] verified our prediction [6] of a global causal coordination between DNA replication origin activity and mRNA expression, demonstrating that matrix and tensor modeling of DNA microarray data [7] can be used to correctly predict previously unknown biological modes of regulation. Ultimately we hope to bring physicians a step closer to one day being able to predict and control the progression of cell division and cancer as readily as NASA engineers plot the trajectories of spacecraft today.
