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

By | General Interest, Happenings, News | No Comments

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

By | Happenings, News | No Comments

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.

U-M plays leading role in regional big data hub

By | Happenings, News | No Comments

A “big data brain trust” has been established by the National Science Foundation to bring together industry, government and academia to accelerate this emerging field and harness it to solve some of society’s toughest problems.

The University of Michigan will play a leading role in the new Midwest Big Data Innovation Hub—one of four that NSF has set up across the nation. U-M is one of five universities that will lead the Midwest hub. Professor Brian Athey, co-director of U-M’s Michigan Institute for Data Science, will lead the effort at U-M.

“We’re thrilled to be a part of this effort, and are looking forward to establishing dynamic partnerships that will coordinate big data expertise and resources to improve the region’s quality of life,” said Athey, who is the Michael Savageau Collegiate Professor and chair of the Department of Computational Medicine & Bioinformatics in the U-M Medical School and also a professor of psychiatry and internal medicine.

These hubs aim to develop partnerships that will use big data to address region-specific problems. Athey will lead a subgroup of the Midwest Hub that will address health sciences. H.V. Jagadish, U-M professor of electrical engineering and computer sciences, will lead a subgroup on transportation.

The Midwest Hub will focus its efforts in three areas:

  • Society, including smart cities and communities; network science; and business analytics
  • The natural and built world, including water, food and energy; digital agriculture; transportation; and advanced manufacturing
  • Health care and biomedical research

Other universities involved in the Midwest Hub are Illinois, Indiana, North Dakota and Iowa State. Partners include the city of Detroit, Ford Motor Co., General Motors, Domino’s Pizza, TechTown Detroit, Quicken Loans and the Henry Ford Health System.

The NSF award provides $1.25 million to set up the framework for bringing partners together to develop, plan and support regional big data partnerships and activities to address regional challenges.

“The Big Data Hubs program represents a unique approach to improving the impact of data science by establishing partnerships among like-minded stakeholders,” said Jim Kurose, NSF’s head of Computer and Information Science and Engineering. “In doing so, it enables teams of data science researchers to come together with domain experts, with cities and municipalities, and with anchor institutions to establish and grow collaborations that will accelerate progress in a wide range of science and education domains with the potential for great societal benefit.”

For more information:

Midwest Big Data Hub

Michigan Institute for Data Science

Midwest Big Data Hub press release from the University of Illinois

NSF press release

RFPs available for MIDAS Challenge Thrust awards

By | Happenings, News | No Comments

The Michigan Institute for Data Science (MIDAS) is pleased to announce the first competition for MIDAS Challenge Thrust awards. These awards are intended to stimulate research in key areas identified at the recent symposium and will lay the foundation for future funding from government, private foundations, or industry.

Requests for Proposals (RFPs) are available for awards in Learning Analytics and Data Science for Transportation. Up to two projects will be funded at a level of approximately $1.25 million each in both of these Challenge Thrust areas.

View the Requests for Proposals here.

White papers describing project goals and teaming arrangements are due November 30, 2015, and full proposals are due January 18, 2016. Awards will be announced on February 15, 2016.

Successful research projects will cut across disciplines, have the potential for disruptive impact in the field, and hold promise for advancing the methodological foundations of data science. Interested researchers can learn more about these two MIDAS Challenge Thrust areas and connect with potential collaborators at four upcoming town hall meetings.

Learning Analytics

  • Wednesday, October 21, 2015, 5:00 p.m. – 6:30 p.m., 1109 François-Xavier Bagnoud  (FXB) Building
  • Tuesday, November 17, 2015, 5:00 p.m. – 6:30 p.m., Kalamazoo Room, Michigan League

Data Science for Transportation

  • Thursday, October 22, 2015, 5:00 p.m. – 6:30 p.m., 1311 EECS
  • Tuesday, November 10, 2015, 5:00 p.m. – 6:30 p.m., Rackham Amphitheater

RFPs for the MIDAS Challenge Thrust awards in the Social Science and Health Science areas will be released in early 2016.

For more information, email midas-rfp@umich.edu or visit midas.umich.edu/rfp.

 

Michigan State University hosting two-day workshop on data science and computation — Sept. 16-17

By | Happenings, News | No Comments

To inaugurate its new Department of Computational Mathematics, Science and Engineering (CMSE), Michigan State University is holding a two-day workshop titled “Frontiers in Data Science and Computation.”

The workshop will take place at the Kellogg Center at MSU in East Lansing on Sept. 16 and 17. The workshop will bring together speakers who are intellectual leaders in computational science and their application to interesting scientific problems. The areas of focus will be topics in scientific computing and Data Science, their applications, challenges and open problems

Scheduled speakers on Sept. 16, focusing on scientific computing:

  • James Amundson, Head, Scientific Software Infrastructure Department; Fermi National Accelerator Laboratory
  • George Biros, Professor, Mechanical Engineering; W. A. “Tex” Moncrief, Jr. Simulation-Based Engineering Science Chair; University of Texas (UT)
  • Richard Brower, Professor, Physics, and Electrical & Computer Engineering; Boston University
  • Keith Cartwright, Research Physicist; Sandia National Laboratories
  • Bjorn Engquist, Director, ICES Center for Numerical Analysis; UT
  • Jeff Hittinger, Group Leader, Center for Applied Scientific Computing; Lawrence Livermore National Laboratory
  • George Em Karniadakis, Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics; Brown University
  • Eric Michielssen, Professor, Electrical Engineering; Assoc. VP for Advanced Research Computing; University of Michigan

Scheduled speakers on Sept. 17, focusing on data science:

  • Chidanand (Chid) Apte, Director, Mathematical Sciences and Analytics; IBM Research Division
  • Gunnar Carlsson, Anne and Bill Swindells Professor of Mathematics; Stanford University; co-founder, Ayasdi
  • Vanja Dukic, Associate Professor, Applied Mathematics; University of Colorado Boulder
  • Piotr Indyk, Professor, Theory of Computation Group, Computer Science and Artificial Intelligence Laboratory; MIT
  • Mario Juric, Washington Research Foundation Data Science Professor of Astronomy; University of Washington
  • Mauro Maggioni, Professor, Mathematics and Computer Science, and Electrical and Computer Engineering; Duke University
  • Jianchang (JC) Mao, Distinguished Engineer, Head of Advertising Relevance & Revenue and Marketplaces Development; Microsoft
  • Wotao Yin, Professor, Department of Mathematics; UCLA

For more information and to register, visit www.egr.msu.edu/fcds2015/