[edm-announce] PhD Graduate Student Funding Opportunity

From: Stephen Fancsali <sfancsali@xxxxxxxxx>
To: edm-announce@xxxxxxxxxxxxx
Date: Mon, 6 Oct 2014 09:27:58 -0400

[on behalf of Philip Pavlik]

The University of Memphis recently received notification of a large
subcontract with Carnegie Mellon University for a grant called DIBBs (Data
Infrastructure Building Blocks), which will fully support a University of
Memphis PhD student for 5 years. We are looking for this new student to
start in January with the start of the grant. See the press release below.

This position will likely lead to immediate employment in higher education
or the education industry after completion of the PhD. This position will
also lead to career options in areas like quantitative finance, since much
of the training and work prepares one for the analysis and management of
time series data. This position will also lead to contacts and networking
with faculty at CMU, MIT and Stanford, who are all partners with Memphis on
this project. Excellent opportunities for postdoc appointments will also be
likely.

Application for this position will be competitive, and we will strongly
favor students with computer programming, database experience, and/or a
strong math background. Ability to write papers and interact with a team
are also highly valued. A good fit to the position with interest in data
warehousing and data mining are also highly desired.

We can help with enrollment to the grad school as a temporary non-degree
student for strong applicants who are not yet enrolled as a graduate
student (with the understanding that you would apply as degree seeking for
the following Fall), and existing Masters/PhD students that need mentorship
are welcome to apply. Application submission will close on October 20th. A
decision will be made by November 1.

Please submit a CV/Resume/Portfolio to Philip Pavlik (ppavlik@xxxxxxxxxxx)
for consideration. Candidates who best meet the requirements will be
contacted for an interview.

Press release from NSF website follows:

Press Release 14-132

Laying the groundwork for data-driven science

*NSF announces $31 million in awards to develop tools, cyberinfrastructure
and best practices for data science*
[image: computer generated spyral model of geodynamo used for benchmark
codes]

A model of a geodynamo showing convective flow caused by the rotation of
earth.
Credit and Larger Version
<http://www.nsf.gov/news/news_images.jsp?cntn_id=132880&org=NSF>

*October 1, 2014*

The ability to collect and analyze massive amounts of data is rapidly
transforming science, industry and everyday life, but what we have seen so
far is likely just the tip of the iceberg. Many of the benefits of "Big
Data" have yet to surface because of a lack of interoperability, missing
tools and hardware that is still evolving to meet the diverse needs of
scientific communities.

One of the National Science Foundation's (NSF) priority goals
<http://www.nsf.gov/pubs/2014/nsf14043/nsf14043.pdf> is to improve the
nation's capacity in data science by investing in the development of
infrastructure, building multi-institutional partnerships to increase the
number of U.S. data scientists and augmenting the usefulness and ease of
using data.

As part of that effort, NSF today announced $31 million in new funding to
support 17 innovative projects under the Data Infrastructure Building Blocks
<http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504776> (DIBBs) program.
Now in its second year, the 2014 DIBBs awards support research in 22 states
and touch on research topics in computer science, information technology
and nearly every field of science supported by NSF.

"Developed through extensive community input and vetting, NSF has an
ambitious vision and strategy for advancing scientific discovery through
data," said Irene Qualters, division director for Advanced
Cyberinfrastructure at NSF. "This vision requires a collaborative national
data infrastructure that is aligned to research priorities and that is
efficient, highly interoperable and anticipates emerging data policies."

This year's data cyberinfrastructure awards build capacity and capability
across the nation and across research communities and complement previous
awards.

"Each project tests a critical component in a future data ecosystem in
conjunction with a research community of users," Qualters said. "This
assures that solutions will be applied and use-inspired."

NSF sees these building blocks as digital components that can be joined
together to develop the foundations for a robust data infrastructure. The
building blocks encompass hardware, software and networking tools, as well
as the communities and people who manage data and who are the practitioners
of data science.

Of the 17 awards, two support early implementations of research projects
that are more mature; the others support pilot demonstrations. Each is a
partnership between researchers in computer science and other science
domains.

One of the two early implementation grants will support a research team led
by Geoffrey Fox, a professor of computer science and informatics at Indiana
University. Fox's team plans to create middleware and analytics libraries
to allow data science to work at large scale on high-performance computing
systems (also known as supercomputers).

Fox and his interdisciplinary team plan to test their platform with several
different applications, including those used in geospatial information
systems (GIS), biomedicine, epidemiology and remote sensing.

"Our innovative architecture integrates key features of open source cloud
computing software with supercomputing technology," Fox said. "And our
outreach involves 'data analytics as a service' with training and curricula
set up in a Massive Open Online Course or MOOC."

Other institutions collaborating on the project include: Arizona State
University, Emory University, Rutgers University, University of Kansas,
University of Utah and Virginia Tech.

The other early implementation project is led by Ken Koedinger, professor
of human computer interaction and psychology at Carnegie Mellon University.
Whereas Fox's team focuses on problems in sensing and the life sciences,
Koedinger's team concentrates on developing infrastructure that will drive
innovation in education.

The team will develop a distributed data infrastructure called LearnSphere
that will make more educational data accessible to course developers, while
also motivating more researchers and companies to share their data with the
greater learning sciences community. LearnSphere will include a graphical
user interface, a library of analytical methods and a wide variety of
educational data gathered from such sources as interactive tutoring
systems, educational games and MOOCs.

"We've seen the power that data has to improve performance in many fields,
from medicine to movie recommendations," Koedinger said. "Educational data
holds the same potential to guide the development of courses that enhance
learning while also generating even more data to give us a deeper
understanding of the learning process."

Other institutions collaborating on this project include: MIT, Stanford
University and the University of Memphis.

The DIBBs program awarded each early implementation project $5 million over
5 years.

The second group of awards supports pilot demonstrations that build upon
the advanced cyberinfrastructure capabilities of existing research
communities to address specific challenges in science and engineering
research and extend those data capabilities to meet broad community needs.
The awards provide $1.5 million over 3 years.

Among the projects supported by DIBBs awards are efforts to develop
cyberinfrastructure to visualize geo-chronological data, like uranium
dating of corals (College of Charleston); data capture and curation for
materials science research (University of Illinois Urbana-Champaign); and
efforts to manage data emerging from the Laser Interferometer
Gravitational-wave Observatory or LIGO (Syracuse University).

The DIBBs program is part of a coordinated strategy within NSF to advance
data-driven cyberinfrastructure. It complements other major efforts
including the DataOne
<http://www.nsf.gov/cgi-bin/goodbye?https://www.dataone.org/> project,
the Research
Data Alliance <http://www.nsf.gov/cgi-bin/goodbye?https://rd-alliance.org/>
and Wrangler
<
http://www.nsf.gov/cgi-bin/goodbye?https://www.tacc.utexas.edu/news/press-releases/2013/wrangler-nsf-grant
>,
a groundbreaking data analysis and management system for the national open
science community.
Philip I. Pavlik Jr.
Institute for Intelligent Systems and Department of Psychology
University of Memphis
Memphis, TN 38152

ppavlik@xxxxxxxxxxx
http://optimallearning.org/

[edm-announce] PhD Graduate Student Funding Opportunity

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