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

By | Al Hero, Educational, General Interest, Research

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.

Michigan Biological Software Team to compete at iGEM with MICDE support

By | Educational, General Interest, News, SC2

MICDE is pleased to announce its support of the Michigan Biological Software Team (MiBioSoft), for its attendance at the 2017 International Genetically Engineered Machine (iGEM) competition in Boston.

Founded in 2014, MiBioSoft is a student-run organization at the University of Michigan that develops software for use in scientific research, with a focus on synthetic biology. It seeks to provide its members with opportunities to not only improve their skills as software designers, but also to improve their communication and management skills by bringing together students from a variety of backgrounds including Biology, Mathematics, Computer Science, and Chemistry.

MiBioSoft competes annually in the software track of the iGEM competition, where research teams from around the world present their results over the course of a three-day conference. During the first two years at the competition, the team was awarded bronze medals. In 2016, MiBioSoft received Best Software Project award as well as a gold medal for their protocol catalog, ProtoCat, in a competition that featured over 300 teams from more than 40 countries, with more than 5,000 participants in total.

About Protocat

Protocat is protocol catalog software developed by MiBioSoft students to address the issue of reproducibility in synthetic biology. Like many innovative ideas, it began because of a problem. Studies have estimated that only 10-25% of published scientific results are reproducible. A 2014 survey conducted by the Michigan Software team confirmed that the repeatability problem exists in synthetic biology, with every scientist surveyed reporting prior struggles with replicating protocols.

ProtoCat 3.0 is a free database of crowd-sourced protocols designed to make existing protocols more repeatable and enable more accurate computational models of biological systems. MiBioSoft believes this can most efficiently be accomplished with a commitment to open source protocols and a broader more active community of digital troubleshooters. ProtoCat 3.0 works to establish such a community by giving anyone with an internet connection or smartphone access to a repository of synthetic biology protocols collected from all over the world. Additionally, ProtoCat 3.0 encourages the development of higher quality, more repeatable protocols by allowing users to document, rate, review, and edit existing methods.

Graduate Studies in Computational & Data Sciences Info Session — Jan 9 & 11

By | Educational, Events, General Interest, News

Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources

There will be two sessions in January 2017:

Info Session: Data Science Services at U-M — Nov. 1

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

Representatives of Consulting for Statistics, Computing and Analytics Research (CSCAR) and the U-M Library (UML) will give an overview of services that are now available to support data-intensive research on campus.  As part of the U-M Data Science Initiative, CSCAR and UML are expanding their scopes and adding capacity to support a wide range of research involving data and computation.  This includes consulting, workshops, and training designed to meet basic and advanced needs in data management and analysis, as well as specialized support for areas such as remote sensing and geospatial analyses, and a funding program for dataset acquisitions.  Many of these services are available free of charge to U-M researchers.  

This event will begin with overview presentations about CSCAR and Library system data services.  There will also be opportunities for researchers to discuss individualized partnerships with CSCAR and UML to advance specific data-intensive projects.  Faculty, staff, and students are welcome to attend.  

Time/Date: 4-5 p.m., November 1,
Location: Earl Lewis Room, Rackham Building

[SC2] HPC resources available to U-M students

By | Educational, Flux, SC2

Brock Palen, Associate Director of Advanced Research Computing-Technology Services, joined the SC2 to talk about all the high performance computing (HPC) resources available to U-M graduate and undergraduate students. A summary of his presentation is here.

Resources:

Available at/through Michigan

  1. Flux for Undergraduates: Undergraduates can use the local flux computing cluster FOR FREE! Please visit the page for more information
    • ARC-Connect: use for Jupyter notebooks and VNC (remote desktop) access of flux resources, useful for remote visualization of big data or just getting a feel for working on linux and flux.
  2. Amazon Web Services: Michigan students get $100/year in amazon web services. While not as cost-effective for some things, very good resource to be aware of.
  3. Hadoop: Michigan’s Hadoop cluster is available for free (I believe you have to apply/demonstrate a need, but you don’t have to pay if it’s accepted). This upcoming workshop will go over the basics, read more if you are interested.

Available via Grant

Brock has an up-to-date webpage linking to and detailing various resources you can apply for.

Highlights:
  1. XSEDE:
    • Startup and teaching allocations are available continuously
    • Research allocations accepts 4x/year
  2. Great Lakes Consortium:
    • Alternate way to get some time on Blue Waters
  3. Amazon/Microsoft/Google:

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2015-2016 Education Snapshot

By | Educational

XSEDESummerBootCamp2016

Students at the U-M satellite site of the XSEDE 2016 Summer Bootcamp

We have over 80 students between our Ph.D. in Scientific Computing and the Graduate Certificate in Computational Discovery and Engineering. The students come from five different schools and colleges, and 30 percent are women. We also have partnered with the Multidisciplinary Design Program to offer our Masters students the experience to work on industrial projects and gain practicum credits.

Our faculty have designed two courses that are being offered for the first time: Methods and Practices of Scientific Computing in Fall 2016 and Data-Driven Analysis and Modeling of Complex Systems in Winter 2017. Methods and Practices of Scientific Computing has gathered a tremendous amount of interest, and very quickly was over-subscribed. Data-Driven Analysis and Modeling of Complex Systems is a fast paced research area that combines scientific computing with big data to improve the existing models’ accuracy and representation of physical and biological systems.

Scientific Computing Student Club social gathering

Scientific Computing Student Club social gathering

We have brought a large community of students together by sponsoring and helping found the Scientific Computing Student Club. Its goal is to become a place for all students that use or want to use high performance computing to meet, share ideas, and find peer-to-peer help. It started in February, with social gatherings, talks from expert speakers, and more. The club has nearly 200 members, including undergraduates, graduate students, and postdocs from six U-M schools and colleges.

2015-2016 Education Snapshot

By | Educational

XSEDESummerBootCamp2016

Students at the U-M satellite site of the XSEDE 2016 Summer Bootcamp

We have over 80 students between our Ph.D. in Scientific Computing and the Graduate Certificate in Computational Discovery and Engineering. The students come from five different schools and colleges, and 30 percent are women. We also have partnered with the Multidisciplinary Design Program to offer our Masters students the experience to work on industrial projects and gain practicum credits.

Our faculty have designed two courses that are being offered for the first time: Methods and Practices of Scientific Computing in Fall 2016 and Data-Driven Analysis and Modeling of Complex Systems in Winter 2017. Methods and Practices of Scientific Computing has gathered a tremendous amount of interest, and very quickly was over-subscribed. Data-Driven Analysis and Modeling of Complex Systems is a fast paced research area that combines scientific computing with big data to improve the existing models’ accuracy and representation of physical and biological systems.

Scientific Computing Student Club social gathering

Scientific Computing Student Club social gathering

We have brought a large community of students together by sponsoring and helping found the Scientific Computing Student Club. Its goal is to become a place for all students that use or want to use high performance computing to meet, share ideas, and find peer-to-peer help. It started in February, with social gatherings, talks from expert speakers, and more. The club has nearly 200 members, including undergraduates, graduate students, and postdocs from six U-M schools and colleges.

Graduate programs in computational and data science — informational sessions Sept. 19 & 21

By | Educational, Events, News

Students interested in computational and data science are invited to learn about graduate programs that will prepare them for success in computationally intensive fields. Pizza and pop will be provided.

Two sessions are scheduled:

Monday, Sept. 19, 5 – 6 p.m.
Johnson Rooms, Lurie Engineering Center (North Campus)

Wednesday, Sept. 21, 5 – 6 p.m.
2001 LSA Building (Central Campus)

The sessions will address:

  • The Ph.D. in Scientific Computing, which is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering, which trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
  • The Graduate Certificate in Data Science, which is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

MICDE Fall 2016 Seminar Series speakers announced

By | Educational, Events, General Interest, News

The Michigan Institute for Computational Discovery and Engineering (MICDE) is proud to announce its fall lineup of seminar speakers. In cooperation with academic departments across campus, the seminar series brings nationally recognized speakers to campus.

This fall’s speakers are:

Sept. 13: Nathan Kutz, Professor of Applied Mathematics, University of Washington

Sept. 22: Rob Gardner, Senior Scientist at the Computation Institute, University of Chicago

Sept. 29: Jeremy Lichstein, Assistant Professor of Biology, University of Florida

Oct. 6: Jonathan Freund, Professor of Mechanical Science and Engineering and of Aerospace Engineering, University of Illinois, Urbana-Champaign

Oct. 14: Anthony Wachs, Assistant Professor of Mathematics and of Chemical and Biological Engineering, University of British Columbia

Oct. 26: Andrea Lodi, Professor of Mathematical and Industrial Engineering, Polytechnique Montreal

Nov. 11: David Higdon, Professor of the Biocomplexity Institute, Virginia Tech

Dec. 9: Ann Almgren, Staff Scientist at the Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratories

For more information, including links to bios and abstracts as available, please visit micde.umich.edu/seminar-series/.

Students in the Graduate Certificate in Computational Discovery and Engineering program are required to attend at least half of the seminars.