MICDE announces 2017-2018 Fellowship recipients

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MICDE is pleased to announce the recipients of the 2017-2018 MICDE Fellowships for students enrolled in the PhD in Scientific Computing or the Graduate Certificate in Computational Discovery and Engineering. We had 91 applicants from 25 departments representing 6 schools and colleges. Due to the extraordinary number of high quality applications we increased the number of fellowships from 15 to 20 awards. See our Fellowship page for more information.

AWARDEES

Diksha Dhawan, Chemistry
Negar Farzaneh, Computational Medicine & Bioinformatics
Kritika Iyer, Biomedical Engineering
Tibin John, Neuroscience
Bikash Kanungo, Mechanical Engineering
Yu-Han Kao, Epidemiology
Steven Kiyabu, Mechanical Engineering
Christiana Mavroyiakoumou, Mathematics
Ehsan Mirzakhalili, Mechanical Engineering
Colten Peterson, Climate and Space Sciences & Engineering
James Proctor, Chemical Engineering
Evan Rogers, Biomedical Engineering
Longxiu Tian, S. Ross School of Business
Jipu Wang, Nuclear Engineering and Radiological Sciences
Yanming Wang, Chemistry
Zhenlin Wang, Mechanical Engineering
Alicia Welden, Chemistry
Anna White, Industrial & Operations Engineering
Chia-Nan Yeh, Physics
Yiling Zhang, Industrial & Operations Engineering

HONORABLE MENTIONS

Geunyeong Byeon, Industrial & Operations Engineering
Ayoub Gouasmi, Aerospace Engineering
Joseph Kleinhenz, Physics
Jia Li, Physics
Changjiang Liu, Biophysics
Vo Nguyen, Computational Medicine & Bioinformatics
Everardo Olide, Applied Physics
Qiyun Pan, Industrial & Operations Engineering
Pengchuan Wang, Civil & Environmental Engineering
Xinzhu Wei, Ecology & Evolutionary Biology

ARC-TS seeks input on next generation HPC cluster

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The University of Michigan is beginning the process of building our next generation HPC platform, “Big House.”  Flux, the shared HPC cluster, has reached the end of its useful life. Flux has served us well for more than five years, but as we move forward with replacement, we want to make sure we’re meeting the needs of the research community.

ARC-TS will be holding a series of town halls to take input from faculty and researchers on the next HPC platform to be built by the University.  These town halls are open to anyone and will be held at:

  • College of Engineering, Johnson Room, Tuesday, June 20th, 9:00a – 10:00a
  • NCRC Bldg 300, Room 376, Wednesday, June 21st, 11:00a – 12:00p
  • LSA #2001, Tuesday, June 27th, 10:00a – 11:00a
  • 3114 Med Sci I, Wednesday, June 28th, 2:00p – 3:00p

Your input will help to ensure that U-M is on course for providing HPC, so we hope you will make time to attend one of these sessions. If you cannot attend, please email hpc-support@umich.edu with any input you want to share.

Job Opening: Research Cloud Administrator

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Advanced Research Computing – Technology Services (ARC-TS)  has an exciting opportunity for a Research Cloud Administrator.

This position will be part of a team working on a novel platform for research computing in the university for data science and high performance computing.  The primary responsibilities for this position will be to develop and create a resource sharing environment to enable execution of Data Science and HPC workflows using containers for University of Michigan researchers.

For more details and to apply, visit: http://careers.umich.edu/job_detail/142372/research_cloud_administrator_intermediate

HPC training workshops begin Monday, May 15

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series of training workshops in high performance computing will be held May 15, May 17 and May 24, 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.”
• Monday, May 15, 9 a.m. – noon. (full descriptionregistration)

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.
• Wednesday, May 17, 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.
• Wednesday, May 24, 1 – 5 p.m. (full description | registration)

NOTE: Additional workshops may be scheduled if demand warrants. Please sign up for the waiting list if the workshops are full, and you will be given first priority for any additional sessions.

New private insurance claims dataset and analytic support now available to health care researchers

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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.

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)

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.