HPC training workshops begin Tuesday, Jan. 31

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series of training workshops in high performance computing will be hed Jan. 31 through Feb. 22, 2017, presented by CSCAR in conjunction with Advanced Research Computing – Technology Services (ARC-TS). All sessions are held at East Hall, Room B254, 530 Church St.

Introduction to the Linux command Line
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also known as the “command line.”
Dates: (Please sign up for only one)
• Tuesday, Jan. 31, 12:30 – 3:30 p.m. (full descriptionregistration)
• Tuesday, Feb. 2, 9 a.m. – noon (full description | registration)
• Tuesday, Feb. 7, 9 a.m. – noon (full description | registration)

Introduction to the Flux cluster and batch computing
This workshop will provide a brief overview of the components of the Flux cluster, including the resource manager and scheduler, and will offer students hands-on experience.
Dates: (Please sign up for only one)
• Thursday, Feb. 9, 1 – 4:30 p.m. (full description | registration)
• Monday, Feb. 13, 1 – 4:30 p.m. (full description | registration)

Advanced batch computing on the Flux cluster
This course will cover advanced areas of cluster computing on the Flux cluster, including common parallel programming models, dependent and array scheduling, and a brief introduction to scientific computing with Python, among other topics.
Dates: (Please sign up for only one)
• Wednesday, Feb. 22, 9 a.m. – noon (full description | registration)
• Friday, Feb. 24, 9 a.m. – noon (full description | registration)

ARC Director Sharon Broude Geva elected vice-chair of Coalition for Academic Scientific Computing

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Sharon Broude Geva, the Director of Advanced Research Computing at the University of Michigan, has been elected vice-chair of the Coalition for Academic Scientific Computation (CASC).

Founded in 1989, CASC advocates for the use of advanced computing technology to accelerate scientific discovery for national competitiveness, global security, and economic success. The organization’s members represent 83 institutions of higher education and national labs.

The vice-chair position is one of four elected CASC executive officers. The officers work closely as a team with the director of CASC. The vice-chair also leads CASC meeting program committees, is responsible for recruitment of new members, substitutes for the chair in his or her absences, and assists with moderating CASC meetings.

Geva served as CASC secretary in 2015 and 2016. Her term as vice-chair is effective for the 2017 calendar year.

The other executive officers for 2017 are are Rajendra Bose, Chair, Columbia University; Neil Bright, Secretary, Georgia Institute of Technology; and Andrew Sherman, Treasurer, Yale University. Curt Hillegas of Princeton University is immediate past chair.

Funding available for data set acquisition

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The new Data Acquisition for Data Science (DADS) program supports acquisition, preparation, management, and maintenance of specialized research data sets used in current and future data science-enabled research projects across U-M, with special focus on the four challenge initiative areas pursued by the Michigan Institute for Data Science (MIDAS): transportation science, health science, social science, and learning analytics.

DADS is meant to provide datasets that can be used by multiple U-M researchers and departments.

DADS is funded through the Data Science Initiative (DSI); total funding is capped at $200,000 per year for 5 years.

DADS will be managed jointly by the Library and Advanced Research Computing (ARC), with support from ARC’s Consulting for Statistics, Computing, and Analytics Research (CSCAR), MIDAS, and ARC-Technology Services (ARC-TS) units.

For more information, see arc.umich.edu/dads.

New geospatial analysis and GIS support at CSCAR

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CSCAR (Consulting for Statistics, Computing and Analytics Research) is offering expanded support for geospatial analysis and geographic information systems (GIS), effective immediately.

Researchers seeking guidance in this area are encouraged to schedule an appointment by calling 764-7828.

Several members of the CSCAR staff have expertise in modeling and analysis of geospatial data, and can provide consultations on basic and advanced methods. A variety of tools including R, Matlab, Python, and Arc-GIS are supported for work in this area. The CSCAR team was recently joined by a consultant holding a PhD in Earth/Environment Sciences, specializing in GIS and remote sensing.

As a result, CSCAR is now able to support a broad range of geospatial analysis activities including GIS, geostatistics, mechanistic modeling, geospatial visualization, and large-scale geospatial data processing on Flux and other advanced infrastructure systems. New workshops in Arc-GIS and other geospatial tools will begin in November (details will appear on this website).

U-M, Coursera offer five-course specialization in Applied Data Science with Python

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Coursera and the University of Michigan are offering a five-course specialization in Applied Data Science with Python starting in September. The courses cost $79 each, and students who complete all coursework, including a capstone project, will receive a Certificate.

The courses, taught by U-M faculty members Christopher Brooks (SI), Kevyn Collins-Thompson (SI and EECS), Daniel Romero (SI and EECS) and VG Vinod Vydiswaran (Medical School and SI), are:

  • Introduction to Data Science in Python
  • Applied Plotting, Charting and Data Representation in Python
  • Applied Machine Learning in Python
  • Applied Text Mining in Python
  • Applied Social Network Analysis in Python (Capstone project)

For more information, see the Coursera webpage.

Building a Community of Social Scientists with Big Data Skills: The ICOS Big Data Summer Camp

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As the use of data science techniques continues to grow across disciplines, a group of University of Michigan researchers are working to build a community of social scientists with skills in Big Data through a week-long summer camp for faculty and graduate students.

Having recently completed its fourth annual session, the Big Data Summer Camp held by the Interdisciplinary Committee for Organizational Studies (ICOS) trains approximately 50 people each spring in skills and methods such as Python, SQL, and social media APIs. The camp splits up into several groups to try to answer a research question using these newly acquired skills.

Working with researchers from other fields is a key component of the camp, and of creating a Big Data social science community, said co-coordinator Todd Schifeling, a Research Fellow at the Erb Institute in the School of Natural Resources and Environment.

“Students meet from across social science disciplines who wouldn’t meet otherwise,” said Schifeling. “And every year we bring back more and more past campers to present on what they’ve been doing.”

Schifeling himself participated in the camp as a student before taking on the role of coordinator this year.

Teddy DeWitt, the other co-coordinator of the camp and a doctoral student at the Ross School of Business, added the camp presents the curriculum in a unique way relative to the rest of campus.

“This set of material does not seem to be available in other parts of the university, at least … with an applied perspective in mind,” he said. “So we’re glad we have this set of resources that is both accessible and well-received by students.”

Participants range in skill from beginning to advanced, but even a relatively advanced student like Jeff Lockhart, a doctoral student in sociology and population studies who describes himself as “super-committed to computational social science,” said that it’s hard to find classes in computational methods in social science departments.

“[The ICOS camp] doesn’t expect a lot of prior knowledge, which I think is critical,” Lockhart said.

Lockhart, DeWitt, and Dylan Nelson, also a sociology doctoral student, are working on setting up a series of workshops in Computational Social Science for fall 2016 (contact Lockhart at jwlock@umich.edu for more information). Lockhart said it’s critical that social scientists learn Big Data skills.

“If we don’t have skills like this, there’s no way for us to enter into these fields of research that are going to be more and more important,” he said.

“A lot of the skills we’ve learned are sort of the on-ramp for doing data science,” DeWitt added.

The camp is co-sponsored by Advanced Research Computing (ARC).

Video, slides available: “Advanced Research Computing at Michigan, An Overview,” Brock Palen, ARC-TS

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Video (http://myumi.ch/aAG7x) and slides (http://myumi.ch/aV7kz) are now available from Advanced Research Computing – Technology Services (ARC-TS) Associate Director Brock Palen’s presentation “Advanced Research Computing at Michigan, An Overview.”

Palen gave the talk on June 27, 2016, outlining the resources and services available from ARC-TS as well as from off-campus resource providers.

MIDAS awards first round of challenge funding in transportation and learning analytics

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Four research projects — two each in transportation and learning analytics — have been awarded funding in the first round of the Michigan Institute for Data Science Challenge Initiatives program.

The projects will each receive $1.25 million dollars from MIDAS as part of the Data Science Initiative announced in fall 2015.

U-M Dearborn also will contribute $120,000 to each of the two transportation-related projects.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.

New on-campus data-science and computational research services available

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Researchers across campus now have access to several new services to help them navigate the new tools and methodologies emerging for data-intensive and computational research.

As part of the U-M Data Science Initiative announced in fall 2015, Consulting for Statistics, Computing and Analytics Research (CSCAR) is offering new and expanded services, including guidance on:

  • Research methodology for data science.
  • Large scale data processing using high performance computing systems.
  • Optimization of code and use of Flux and other advanced computing systems.
  • Advanced data management.
  • Geospatial data analyses.
  • Exploratory analysis and data visualization.
  • Obtaining licensed data from commercial sources.
  • Scraping, aggregating and integrating data from public sources.
  • Analysis of restricted data.

“With Big Data and computational simulations playing an ever-larger role in research in a variety of fields, it’s increasingly important to provide researchers with a comprehensive ecosystem of support and services that address those methodologies,” said CSCAR Director Kerby Shedden.

As part of this significant expansion of its scope, the campuswide statistical consulting service CSCAR has been renamed Consulting for Statistics, Computing and Analytics Research. It was formerly known as the Center for Statistical Consultation and Research.

For more information, see the University Record article.

New graduate course offering: “Methods and Practice of Scientific Computing”

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The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce “Methods and Practice of Scientific Computing”, the first graduate course designed and organized by MICDE faculty. The course will be taught in Fall 2016, coordinated by Dr. Brendan Kochunas. This foundational course in scientific computing has been developed as a broad introduction to the subject, and has been designed to support research in all disciplines represented in MICDE. In addition to Brendan Kochunas, the course was developed by MICDE professors Bill Martin, Karthik Duraisamy, Vikram Gavini, and Shravan Veerapaneni, and MICDE Assistant Director Mariana Carrasco-Teja.

The details follow:

NERS 590
4 credits
Prerequisites: Graduate standing and permission of instructor.

This course is designed for graduate students who are developing the methods, and using the tools, of scientific computing in their research. With the increased power and availability of computers to do massively scaled simulations, computational science and engineering as a whole has become an integral part of research that complements experiment and theory. This course will teach students the necessary skills to be effective computational scientists and how to produce work that adheres to the scientific method. A broad range of topics will be covered including: software engineering best practices, computer architectures, computational performance, common algorithms in engineering, solvers, software libraries for scientific computing, uncertainty quantification, verification and validation, and how to use all the various tools to accomplish these things. The class will have lecture twice a week and have an accompanying lab component. Students will be graded on homeworks, lab assignments, and a course project.

A draft of the syllabus can be found here. Please contact MICDE at micde-contact@umich.edu with any questions.