ARC-TS seeks input on next generation HPC cluster

By | Events, Flux, General Interest, Happenings, HPC, News | No Comments

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

By | General Interest, Happenings, HPC, News | No Comments

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.

MIDAS starting research group on mobile sensor analytics

By | Educational, Events, General Interest, Happenings, News | No Comments

The Michigan Institute for Data Science (MIDAS) is convening a research working group on mobile sensor analytics. Mobile sensors are taking on an increasing presence in our lives. Wearable devices allow for physiological and cognitive monitoring, and behavior modeling for health maintenance, exercise, sports, and entertainment. Sensors in vehicles measure vehicle kinematics, record driver behavior, and increase perimeter awareness. Mobile sensors are becoming essential in areas such as environmental monitoring and epidemiological tracking.

There are significant data science opportunities for theory and application in mobile sensor analytics, including real-time data collection, streaming data analysis, active on-line learning, mobile sensor networks, and energy efficient mobile computing.

Our working group welcomes researchers with interest in mobile sensor analytics in any scientific domain, including but not limited to health, transportation, smart cities, ecology and the environment.

Where and When:

Noon to 2 pm, April 13, 2017

School of Public Health I, Room 7625

Lunch provided

Agenda:

  • Brief presentations about challenges and opportunities in mobile sensor analytics (theory and application);

  • A brief presentation of a list of funding opportunities;

  • Discussion of research ideas and collaboration in the context of grant application and industry partnership.

Future Plans: Based on the interest of participants, MIDAS will alert researchers to relevant funding opportunities, hold follow-up meetings for continued discussion and team formation as ideas crystalize for grant applications, and work with the UM Business Engagement Center to bring in industry partnership.

Please RSVP.  For questions, please contact Jing Liu, Ph.D, MIDAS research specialist (ljing@umich.edu; 734-764-2750).

Gilbert, Rudelson, Wu named Simons Foundation Fellows in Mathematics

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Three University of Michigan professors have been named Simons Fellows in Mathematics by the Simons Foundation:

  • Anna Gilbert, the Herman H. Goldstine Collegiate Professor of Mathematics, core faculty member at the Michigan Institute for Data Science (MIDAS), and Professor of Electrical and Computer Engineering
  • Mark Rudelson, Professor of Mathematics
  • Sijue Wu, Robert W. and Lynn H. Browne Professor of Science and Professor of Mathematics.

Forty fellows were named in all.

The fellowships provide funding that allows faculty to take up to a semester-long research leave from teaching and administrative duties. The foundation also gives fellowships in Theoretical Physics.

For more information, see www.simonsfoundation.org.

Workshop co-chaired by MIDAS co-director Prof. Hero releases proceedings on inference in big data

By | Al Hero, Educational, General Interest, Research | No Comments

The National Academies Committee on Applied and Theoretical Statistics has released proceedings from its June 2016 workshop titled “Refining the Concept of Scientific Inference When Working with Big Data,” co-chaired by Alfred Hero, MIDAS co-director and the John H Holland Distinguished University Professor of Electrical Engineering and Computer Science.

The report can be downloaded from the National Academies website.

The workshop explored four key issues in scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models.

The workshop brought together statisticians, data scientists and domain researchers from different biomedical disciplines in order to identify new methodological developments that hold significant promise, and to highlight potential research areas for the future. It was partially funded by the National Institutes of Health Big Data to Knowledge Program, and the National Science Foundation Division of Mathematical Sciences.

Emily Mower Provost Receives NSF CAREER Award to Develop Emotion and Mood Recognition for Mental Health Monitoring and Treatment

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Prof. Emily Mower Provost has been awarded an NSF CAREER grant for her research project, “Automatic Speech-Based Longitudinal Emotion and Mood Recognition for Mental Health Monitoring and Treatment.”

Prof. Mower Provost’s research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology. The goals of her research are motivated by the complexities of human emotion expression and perception.

More information about the project is available from the College of Engineering and Prof. Mower Provost’s CAREER Award Posting by NSF.

Ambuj Tewari selected as 2017 Alfred P. Sloan Research Fellow in Computer Science

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Ambuj Tewari is one of seven U-M scientists selected for Sloan Fellowships.  Dr. Tewari obtained his PhD from UC Berkeley, and joined the University of Michigan as Assistant Professor of Statistics in 2012.  He also holds a courtesy appointment at the Department of Electrical Engineering and Computer Science.  Dr. Tewari is also an Affiliate Faculty member in MIDAS.

The two-year, $60,000 fellowships are awarded to scientists “in recognition of distinguished performance and a unique potential to make substantial contributions to their field,” according to the organization’s website. Researchers are considered based on nominations, and then selected by an independent panel of senior scientists.

The Alfred P. Sloan Foundation supports early career researchers in eight fields, including chemistry, computational and evolutionary molecular biology, computer science, economics, mathematics, neuroscience, ocean sciences, and physics.

For more information, please see the U-M news release.

MDST announces Detroit blight data challenge; organizational meeting Feb. 16

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The Michigan Data Science Team and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit on a data challenge that seeks to answer the question: How can blight ticket compliance be increased?

An organizational meeting is scheduled for Thursday, Feb. 16 at 5:30 p.m. in EECS 1200.

The city is making datasets available containing building permits, trades permits, citizens complaints, and more.

The competition runs through March 15. For more information, see the competition website.