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.

Research highlights: U-M group awarded Midwest Big Data Spoke award from NSF for Advanced Computational Neuroscience Network (ACNN)

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A group of three University of Michigan faculty members will lead the Advanced Computational Neuroscience Network project as a “spoke” in the Midwest Big Data Hub program funded by the National Science Foundation.

The Principal Investigator is Richard Gonzalez, Amos N. Tversky Collegiate Professor of the U-M Psychology Department, who has joint appointments in Statistics and Marketing, is Director of the Research Center for Group Dynamics, Research Professor in the Center for Human Growth and Development, and has affiliations with the U-M Comprehensive Cancer Center and the Center for Computational Medicine and Bioinformatics.

Co-PI’s are George Alter, professor in the History Department and the Institute for Social Research, and Ivo Dinov, associate professor in the School of Nursing and School of Medicine and Director of Statistics Online Computational Resources (SOCR), and associate director for Education and Training of the Michigan Institute for Data Science (MIDAS).

All three are affiliated faculty of MIDAS.

The ACNN program will leverage rapid technological development in sensing, imaging, and data analysis to facilitate new discoveries in neuroscience, and will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral sciences together with partnerships in academia, industry and government. ACNN will address three specific problems relating to Big Data in neuroscience:

  • data capture, organization and management involving multiple centers and research groups
  • quality assurance, preprocessing and analysis that incorporates contextual metadata
  • data communication to software and hardware computational resources that can scale with the volume, velocity and variety of neuroscience data sets.

ACNN is a collaboration between U-M, Ohio State University, Indiana University, and Case Western Reserve University.

The BD Hubs and Spokes programs are part of a larger effort at NSF to advance data science and engineering. In Fiscal Year 2017, NSF will invest more than $110 million in Big Data research.

MIDAS seeks candidates for faculty positions

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The University of Michigan, Ann Arbor, seeks candidates for multiple full-time tenured or tenure-track faculty positions in the field of data science. The positions are open to candidates at all ranks and both methodological and applied areas of data science will be considered. The University is especially interested in candidates whose research interests lie in data science methodology and its application to transportation, learning analytics, personalized health and precision medicine, or computational social science.   Successful candidates will have their primary appointments in one or more departments of the University and will also have an affiliation as a core faculty in the recently established Michigan Institute for Data Science (MIDAS). MIDAS currently has over 200 affiliated faculty across its 19 schools and colleges. Interested applicants can apply by submitting an electronic application using our online form. Questions can be addressed to data-science@umich.edu.

We are especially interested in qualified candidates who can contribute, through their research, teaching, and/or service, to the diversity and excellence of the academic community. Underrepresented minorities and women are strongly encouraged to apply. The University of Michigan is a non-discriminatory/affirmative action employer and is responsive to the needs of dual career families.

NSF Program Solicitation: Quantitative Approaches to Biomed. Big Data (QuBBD)

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The NIH Big Data to Knowledge Initiative (BD2K, https://datascience.nih.gov/bd2k), together with the Division of Mathematical Sciences at NSF, announces the release of a new program solicitation (NSF 16-573)

This program is designed to support novel mathematical, statistical, or computational approaches to biomedical big data challenges. Collaborative efforts that bring together quantitative scientists and biomedical researchers are a requirement for this program and must be convincingly demonstrated in the proposal. The program is designed to foster and support new inter- and multi-disciplinary teams of investigators. The due date for full proposals is September 28, 2016.

BD2K is a trans-NIH initiative that aims to support advances in data science, other quantitative sciences, and training that are needed for the effective use of big data in biomedical research. Interested applicants are encouraged to join the BD2K listserv (https://list.nih.gov/cgi-bin/wa.exe?SUBED1=bd2kupdates&A=1) to receive the most up-to- date information about BD2K events and funding opportunities. Please share this opportunity with your interested scientific communities.

Great Lakes Observing System Data Challenge: Call for Issue Experts, Sponsors

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CALL FOR ISSUE EXPERTS AND SPONSORS

The Great Lakes Observing System (GLOS) is hosting the Great Lakes Data Challenge in summer of 2016. As part of our 10 year anniversary, GLOS will be taking open data to the next level by using open innovation to broaden our community and create new partnerships to engage people in problem solving for the Great Lakes. GLOS is currently soliciting support for sponsors and issue experts.

GOALS

  • Inspire a wider audience to engage with Great Lakes issues
  • Use technologies, innovation and creativity to solve Great Lakes problems
  • Encourage the use of open data resources from GLOS and beyond

TIMELINE

  • Late May 2016: Launch challenge
  • June: Kick-off event(s), including IAGLR
  • August 15: Submissions due. Submissions can include an app, data “mash-up”, visualization, story, or other innovative idea for using, collecting, analyzing, visualizing, and/or communicating Great Lakes data.
  • August 15-31: Judging
  • September 15: Winners notified
  • October 12-13: Award presentation at GLOS Annual Meeting in Ann Arbor, MI

GLOS PROVIDES

  • Baseline prize money: $5,000
  • Data, technical support, and resources for developer guidelines, rules, etc.
  • Data Challenge(s) coordination

WE NEED YOU

Sponsors: by May 20 The Great Lakes Data Challenge is a unique opportunity to network the region’s
environmental, governmental and non-profit sectors with the information technology sector. Sponsors must commit by May 20 to ensure inclusion in event promotions.

Consider sponsoring the challenge at one of our suggested levels (see next page) to help support prize
money, event costs, and promotional giveaways. This is a great way to promote your business/organization to a diverse audience of environmental data and technology stakeholders.

Issue Experts: by June 1 We are looking for volunteers with expertise in areas including invasive species, nutrients and algae and boater safety, among others. You would agree to be a resource to teams who have specific questions about the topic at hand. The commitment could be flexible according to your interest and availability.

Please contact GLOS at kpaige@glos.us if you are interested in supporting the data challenge in any of these areas.

Be a part of the Great Lakes Observing System’s Data Challenge

  • SUPERIOR $5,000
    All lower level sponsorship benefits as well as…
    Top billing as Data Challenge co-sponsor in all event promotions and media releases
    Large, prominent logo on event giveaways, promotional signage, and website
  • MICHIGAN $2,500
    All lower level sponsorship benefits as well as…
    Acknowledgement as co-sponsor for a custom challenge category
    Logo on event giveaways
  • HURON $1,000
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement at promotional events including kick-off and award presentation
    Logo on Data Challenge website and promotional signage
  • ONTARIO $500
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement on promotional signage
    Complimentary individual (for 1 person) GLOS membership and registration to the GLOS Annual Meeting
  • ERIE $250
    Sponsorship acknowledgement and website link on Data Challenge website
    Acknowledgement in GLOS Annual Report

Link to sponsor commitment page.

UMHS – PUHSC Joint Institute 2016 Symposium: Call for Posters

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University of Michigan Health System & Peking University Health Science Center

Joint Institute for Translational and Clinical Research

2016 Symposium

Call for Posters

[Downloadable Directions]

Call for Poster Abstracts: Submission Information
Showcase your research to Peking University Health Science Center counterparts as the JI looks to expand by offering funding to non-medical school faculty for new health-related joint research projects. A great venue to meet potential collaborators, the poster session will be Thursday, Oct. 13. Details, including times, will follow poster acceptance.

How to submit
Abstracts should relate to clinical and translational research studies and should be submitted electronically in a Microsoft Word document.

Send abstracts to globalreach@umich.edu by September 9, 2016. Please include the following:
Title

  • Title should be brief but should not contain abbreviations.
  • Do not bold use letters in the title unless necessary.
  • Do not capitalize all letters in title, only the first word and key words.

Authors

  • Include all authors and their affiliations. To associate authors and their institutional affiliations, please place a number in parenthesis after each author’s name (if more than one author) and the corresponding number before each affiliated institution’s name (if more than one institution).
  • Put the submitting/presenting author’s name in bold.
  • Do not capitalize all letters in speaker information, only as appropriate.

Abstract

  • Abstracts are limited to 300 words. Use size 11 Arial or Calibri font.
  • Submit text only. Do not include tables, graphics, or charts.
  • Do not include title, authors, or author affiliations in the abstract text.
  • Abstracts may include background, methods, results, conclusions, and funding-source acknowledgements, if applicable.

Submitter contact information

  • First and last name, degrees
  • Email address

Please proofread carefully – information submitted with errors may be published as is. Use a word processing program to assist with checking for grammar and spelling errors, as well as word count.
The deadline to submit abstracts is Sept. 9, 2016. For more information, contact globalreach@umich.edu.

 

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.

MDST team

Unused cycles on Flux HPC cluster now available to U-M undergraduates at no cost

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Undergraduates working on research that requires high performance computing resources can now use the Flux HPC cluster at no cost.

Flux is the shared computing cluster available across campus, operated by Advanced Research Computing – Technology Services (ARC-TS). Under ARC-TS’s new Flux for Undergraduates program, student groups and individuals with faculty sponsors can access unused computing cycles on Flux for free.

The first student group to take advantage of this program is the Michigan Data Science Team, which was created in Fall 2015 with the goal of helping U-M students enter Big Data competitions. The team enters competitions through sites like Kaggle, and is one of the first such teams affiliated with a university.

The group’s organizer, Jonathan Stroud, a Computer Science and Engineering graduate student, said team members were maxing out the capabilities of their laptops when they first started.

“For the first couple of competitions, we made sure we picked a problem that people could do on their laptops,” Stroud said. “Still, every night before bed, they would set up their experiments and they ran all night.”

He said success in the data science competitions typically depends on trying several approaches simultaneously, which can be taxing on computing resources. Stroud said the team typically uses software such as Python, R, and Matlab. Team members come from a wide range of disciplines, including Engineering, Applied Math, Physics, and one from the Music School, Stroud said.

Jacob Abernethy, assistant professor of Electrical Engineering and Computer Science, is the group’s faculty advisor. He wrote some funding for the group into his NSF CAREER proposal that was awarded in 2015. He said after the group’s first competition, he surveyed the students as to what worked and what didn’t. He said one of the clearest responses was the need for more robust computing resources.

“Our top two competitors talked about maxing out the resources on not only their own laptop, but also on the clusters provided them by their advisors,” Abernethy said. “It became clear that we needed to talk about Flux.”

He said a key method to the machine learning and data science experimentation process is the use of cross-validation, that is, testing the performance of a set of parameters on several subsets of data simultaneously. “This leads to a very obvious need for a distributed system in which we can execute a large number of ‘embarrassingly parallel’ tasks quickly,” Abernethy said.

Being able to use Flux “has been helping us a lot,” Stroud added. “We’ve been contacted by other schools to see how they can do the same thing.”

Jobs submitted under Flux For Undergraduates will run only when unused cycles are available and will be requeued when those resources are needed by standard Flux jobs. To be most efficient, student groups should use short or checkpointed jobs to take advantage of these available cycles.

Student groups can also purchase Flux allocations for jobs that are higher priority or time constrained; those allocations can also work in conjunction with the free Flux for Undergraduates jobs.

“The goal is to provide undergraduates with experience in high performance computing, and access to computational resources for their projects,” said Brock Palen, Associate Director of ARC-TS.

Undergraduate groups and individuals must have sponsorship from a faculty member. To request resources through Flux for Undergraduates, please fill out this form. An abstract of the intended activity must be submitted.

Questions can be directed to arc-contact@umich.edu.