Talks from the 2017 MICDE Annual Symposium are now available on the Advanced Research Computing YouTube channel.
Due to technical difficulties, the afternoon sessions (Emanuel Gull and J. Tinsley Oden) were not recorded.
The videos can be viewed at http://myumi.ch/J2j33
University of Michigan students are invited to apply for Michigan Institute for Computational Discovery and Engineering (MICDE) Fellowships for the 2017-2018 academic year. These $4,000 fellowships are available to students in both the Ph.D in Scientific Computing and the Graduate Certificate Program in Computational Discovery and Engineering. Applicants should be graduate students enrolled in either program, although students not yet enrolled but planning to do so may simultaneously submit program and fellowship applications.
Fellows will receive a $4,000 research fund that can be used to attend a conference, to buy a computer, or for any other approved activity that enhances the Fellow’s graduate experience. We also ask that Fellows attend at least 8 MICDE seminars between Fall 2017 and Winter 2018, attend one MICDE students’ networking event, and present a poster at the MICDE Symposium on March 22, 2018. For more details and to apply please visit http://micde.umich.edu/
Interested students should download and complete the application form, and submit it with a one-page resume as a SINGLE PDF DOCUMENT to MICDE-apps@umich.edu. The due date for applications is May 19, 2017, 5:00 E.T. We expect to announce the awardees onJune 5, 2017.
We encourage applications from all qualified candidates, including women and minorities.
The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.
The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.
To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.
CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.
Contact Patrick Brady (pgbrady@umich.edu) at IHPI or James Henderson (jbhender@umich.edu) at CSCAR for more information.
The data acquisition and availability was funded by IHPI and the U-M Data Science Initiative.
A 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 description | registration)
• 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)
January 30, 2017
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.
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.
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).
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:
For more information, see the Coursera webpage.
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).
CSCAR is a unit of the U-M Office of Research 
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