New Data Science Course – Winter 2018

By | Educational, News

Computational Data Science
(EECS 598 / BIOINF 505)

A new graduate course that provides an in-depth introduction to computational methods in data science for identifying, fitting, extracting and making sense of patterns in large data sets is now enrolling students for Winter 2018.

Lectures will typically begin with an introduction of a core data science method, followed by the student programming the method computationally with a computer assisting the student by certifying when the program is correct, interleaved with ‘just-in-time’ theory that will expose the student to the mathematics that underpin the methodology. Once the method has been correctly implemented, the students will be given a real world example or ‘success story’ to work with that illustrates when the algorithm ‘works’ as expected, followed by an instructor guided computational exploration of the various subtleties of the algorithm and its weakness.

A full course description, prerequisites and schedule are available.

Please share this announcement with students who might be interested.

U-M students make strong showing at Michigan Datathon

By | Data, Educational, Events, General Interest, Happenings, News

University of Michigan students won first and third places in the Michigan Datathon held Nov. 4, 2017 in the Michigan Union and hosted by Citadel LLC, Correlation One, and the U-M Statistics Department.

1st-place winning team from the University of Michigan:

Ruofei (Brad) Zhao, Statistics Ph.D. student

Zheng Gao, Statistics Ph.D. student

You Wu, Master’s in Applied Statistics student

Kevin Zheng, Sophomore, Computer Science

 

2nd-place team:

Zi Yi, Statistics Master’s student, University of Chicago

Tian Gu, Biostatistics Ph.D. student, University of Michigan

Shuo Zhang, Statistics Master’s student, University of Chicago

Shiyang Lu, Robotics & Naval Architecture and Marine Engineering Master’s student, University of Michigan

 

3rd-place team from the University of Michigan:

Hanbo Sun, Master’s in Applied Statistics student

Xinghui Song, Master’s in Applied Statistics student

Tuo Wang, Master’s in Applied Statistics student

Hang Yuan, Master’s in Applied Statistics student

 

For more, see https://lsa.umich.edu/stats/news-events/all-news/graduatenews/MichiganDatathonWinners0.html

U-M plans participation at SC17 conference in Denver

By | General Interest, Happenings, HPC, News

Several University of Michigan researchers and professional IT staff will attend the Supecomputing 17 (SC17) conference in Colorado from Nov. 12-17, and participate in a number of different ways, including demonstrations, presentations and tutorials.

In addition to the events and presentations listed below, Amy Liebowitz, a network architect at ITS, is working on SCINet, a high-capacity network created every year for the conference. Liebowitz is on the routing team, which is responsible for installing, configuring and supporting the high performance conference network. The Routing Team also coordinates external connectivity with commodity Internet and R&E WAN service providers.

Please visit us at Booth 471 on the exhibit floor, or at one of the following events:

Sunday, Nov. 12

8:30 a.m. – 5 p.m.: Quentin Stout (EECS) and Christiane Jablonowski (CLASP) will teach the “Parallel Computing 101” tutorial.

Tuesday, Nov. 14

10:30 a.m.:  Research Networking at the University of Michigan
Eric Boyd, Director of Research Networks, U-M
U-M Booth #471

11 a.m.: The OSiRIS Project: Open Storage Research Infrastructure
Ben Meekhof, HPC Storage Administrator, Advanced Research Computing – Technology Services (ARC-TS)
U-M Booth #471

12:30 p.m.: GPU-Accelerated Predictive Material Design
Simon Adorf, Ph.D. Candidate, Chemical Engineering Department, U-M
U-M Booth #471

1:30 p.m.: Simple Data and Workflow Management with Signac
Simon Adorf, Ph.D. Candidate, Chemical Engineering Department, U-M
U-M Booth #471

1:30 – 3 p.m.: Matt McLean, a Big Data systems administrator with ARC-TS, will serve as a panelist at a presentation titled “The ARM Software Ecosystem: Are We There Yet?

2 p.m.: The Michigan Institute for Computational Discovery and Engineering
Mariana Carrasco-Teja, Assistant Director, MICDE
U-M Booth #471

5:15 – 7 p.m.: Jeff Sica, a research database administrator with ARC-TS, will help lead a Birds of a Feather session titled “Containers in HPC.”

6:15 – 8:30 p.m.: ARC at U-M is a sponsor of a networking and career networking reception put on by Women in HPC. ARC Director Sharon Broude Geva will speak at the event.

Wednesday, November 15

10 a.m.: The OSiRIS Project: Open Storage Research Infrastructure
Shawn McKee, U-M Department of Physics, OSiRIS Principal Investigator
U-M Booth #471

11 a.m.: Research Networking at the University of Michigan
Eric Boyd, Director of Research Networks, U-M
U-M Booth #471

3 p.m.: The New Cavium ThunderX Big Data Cluster at U-M
Matt McLean, Big Data Systems Administrator, ARC-TS
U-M Booth #471

Thursday, November 16

11 a.m.: The New Cavium ThunderX Big Data Cluster at U-M
Matt McLean, Big Data Systems Administrator, ARC-TS
U-M Booth #471

 

 

U-M partners with Cavium on Big Data computing platform

By | Feature, General Interest, Happenings, HPC, News

A new partnership between the University of Michigan and Cavium Inc., a San Jose-based provider of semiconductor products, will create a powerful new Big Data computing cluster available to all U-M researchers.

The $3.5 million ThunderX computing cluster will enable U-M researchers to, for example, process massive amounts of data generated by remote sensors in distributed manufacturing environments, or by test fleets of automated and connected vehicles.

The cluster will run the Hortonworks Data Platform providing Spark, Hadoop MapReduce and other tools for large-scale data processing.

“U-M scientists are conducting groundbreaking research in Big Data already, in areas like connected and automated transportation, learning analytics, precision medicine and social science. This partnership with Cavium will accelerate the pace of data-driven research and opening up new avenues of inquiry,” said Eric Michielssen, U-M associate vice president for advanced research computing and the Louise Ganiard Johnson Professor of Engineering in the Department of Electrical Engineering and Computer Science.

“I know from experience that U-M researchers are capable of amazing discoveries. Cavium is honored to help break new ground in Big Data research at one of the top universities in the world,” said Cavium founder and CEO Syed Ali, who received a master of science in electrical engineering from U-M in 1981.

Cavium Inc. is a leading provider of semiconductor products that enable secure and intelligent processing for enterprise, data center, wired and wireless networking. The new U-M system will use dual socket servers powered by Cavium’s ThunderX ARMv8-A workload optimized processors.

The ThunderX product family is Cavium’s 64-bit ARMv8-A server processor for next generation Data Center and Cloud applications, and features high performance custom cores, single and dual socket configurations, high memory bandwidth and large memory capacity.

Alec Gallimore, the Robert J. Vlasic Dean of Engineering at U-M, said the Cavium partnership represents a milestone in the development of the College of Engineering and the university.

“It is clear that the ability to rapidly gain insights into vast amounts of data is key to the next wave of engineering and science breakthroughs. Without a doubt, the Cavium platform will allow our faculty and researchers to harness the power of Big Data, both in the classroom and in their research,” said Gallimore, who is also the Richard F. and Eleanor A. Towner Professor, an Arthur F. Thurnau Professor, and a professor both of aerospace engineering and of applied physics.

Along with applications in fields like manufacturing and transportation, the platform will enable researchers in the social, health and information sciences to more easily mine large, structured and unstructured datasets. This will eventually allow, for example, researchers to discover correlations between health outcomes and disease outbreaks with information derived from socioeconomic, geospatial and environmental data streams.

U-M and Cavium chose to run the cluster on Hortonworks Data Platform, which is based on open source Apache Hadoop. The ThunderX cluster will deliver high performance computer services for the Hadoop analytics and, ultimately, a total of three petabytes of storage space.

“Hortonworks is excited to be a part of forward-leading research at the University of Michigan exploring low-powered, high-performance computing,” said Nadeem Asghar, vice president and global head of technical alliances at Hortonworks. “We see this as a great opportunity to further expand the platform and segment enablement for Hortonworks and the ARM community.”

MDST – NFL Free Agency Value Prediction Competition Kick-Off – Nov. 9, 6pm

By | Data, Data sets, Educational, Events, Happenings, MDSTPosts, MDSTProjects, News

In this competition, student teams at the University of Michigan will use historical free agent data to predict the value of new contracts signed in the 2018 free agency period. These predictions will be evaluated against the actual contracts as they are signed. This competition is organized by the Michigan Data Science Team (MDST), in collaboration with the Baltimore Ravens and the Michigan Sports Analytics Society (MSAS).  Food will be provided. This is an initial kick-off meeting of the competition.

RSVP

Date, Time

Thursday, November 9 at 6:00 PM EST to Thursday, November 9 at 7:00 PM EST
Add To Google Calendar | iCal/Outlook

Location

Weiser Hall 10th Floor Auditorium
500 Church St, 48104, MI

Host

Michigan Data Science Team

 

 

CSCAR provides walk-in support for new Flux users

By | Data, Educational, Flux, General Interest, HPC, News

CSCAR now provides walk-in support during business hours for students, faculty, and staff seeking assistance in getting started with the Flux computing environment.  CSCAR consultants can walk a researcher through the steps of applying for a Flux account, installing and configuring a terminal client, connecting to Flux, basic SSH and Unix command line, and obtaining or accessing allocations.  

In addition to walk-in support, CSCAR has several staff consultants with expertise in advanced and high performance computing who can work with clients on a variety of topics such as installing, optimizing, and profiling code.  

Support via email is also provided via hpc-support@umich.edu.  

CSCAR is located in room 3550 of the Rackham Building (915 E. Washington St.). Walk-in hours are from 9 a.m. – 5 p.m., Monday through Friday, except for noon – 1 p.m. on Tuesdays.

See the CSCAR web site (cscar.research.umich.edu) for more information.

University of Michigan researcher contributes to NASA findings on carbon in the atmosphere showcased in the journal Science

By | General Interest, Happenings, News

 

High-resolution satellite data from NASA’s Orbiting Carbon Observatory-2 are revealing the subtle ways that carbon links everything on Earth – the ocean, land, atmosphere, terrestrial ecosystems and human activities. Scientists using the first 2 1/2 years of OCO-2 data have published a special collection of five papers today in the journal Science that demonstrates the breadth of this research. In addition to showing how drought and heat in tropical forests affected global carbon dioxide levels during the 2015-16 El Niño, other results from these papers focus on ocean carbon release and absorption, urban emissions and a new way to study photosynthesis. A final paper by OCO-2 Deputy Project Scientist Annmarie Eldering of NASA’s Jet Propulsion Laboratory in Pasadena, California, and colleagues gives an overview of the state of OCO-2 science.

Manish Verma, a Geospatial/Data Science Consultant at the University of Michigan’s Consulting for Statistics, Computing and Analytics Research (CSCAR) unit, contributed as a coauthor to an article on a new way to measure photosynthesis over time and space.

Using data from the OCO-2, Verma’s analysis helped expand the utility of measurements of solar induced fluorescence (SIF), which indicates active photosynthesis in plants. Verma’s work showed that SIF data collected from the OCO-2 satellite provides reliable information on the variability of photosynthesis at a much smaller scale — down to individual ecosystems.

This can, in turn, “lead to more reliable estimates of carbon sources — that is, when, where, why and how carbon is exchanged between land and atmosphere — as well as a deeper understanding of carbon-climate feedbacks,” according to the Science article.

For more, see the NASA press release (https://www.nasa.gov/feature/jpl/new-insights-from-oco-2-showcased-in-science) and the Science article (http://science.sciencemag.org/content/358/6360/eaam5747.full)

Real estate dataset available to researchers

By | Data, Data sets, Educational, General Interest, Happenings, News

The University of Michigan Library system and the Data Acquisition for Data Sciences program (DADS) of the U-M Data Science Initiative (DSI) have recently joined forces to license a major data resource capturing parcel-level information about the property market in the United States.  

The data were licensed from the Corelogic corporation, who have assimilated deed, tax and foreclosure information on nearly all properties in the entire US. Coverage dates vary by county, some county records go back fifty years. Coverage is more comprehensive from the 1990s to the present.

These data will support a variety of research efforts into regional economies, economic disparities, trends in land-use, housing market dynamics, and urban ecology, among many other areas.

The data are available on the Turbo Research Storage system for users of the U-M High Performance Computing infrastructure, and via the University of Michigan Library.

To access the data, researchers must first sign a MOU; contact Senior Associate Librarian Catherine Morse cmorse@umich.edu for more information, or visit https://www.lib.umich.edu/database/corelogic-parcel-level-real-estate-data.