Explore ARCExplore ARC

U-M launches Data Science Master’s Program

By | Educational, General Interest, Happenings, News

The University of Michigan’s new, interdisciplinary Data Science Master’s Program is taking applications for its first group of students. The program is aimed at teaching participants how to extract useful knowledge from massive datasets using computational and statistical techniques.

The program is a collaboration between the College of Engineering (EECS), the College of Literature Science and the Arts (Statistics), the School of Public Health (Biostatistics), the School of Information, and the Michigan Institute for Data Science.

“We are very excited to be offering this unique collaborative program, which brings together expertise from four key disciplines at the University in a curriculum that is at the forefront of data science,” said HV Jagadish, Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science, who chairs the program committee for the program.

“MIDAS was a catalyst in bringing  faculty from multiple disciplines together to work towards the development of this new degree program,”  he added.

MIDAS will provide students in this program with interdisciplinary collaborations, intellectual stimulation, exposure to a broad range of practice, networking opportunities, and space on Central Campus to meet for formal and informal gatherings.

For more information, see the program website at https://lsa.umich.edu/stats/masters_students/mastersprograms/data-science-masters-program.html, and the program guide (PDF) at https://lsa.umich.edu/content/dam/stats-assets/StatsPDF/MSDS-Program-Guide.pdf.

Applications are due March 15.

HPC training workshops begin Tuesday, Feb. 13

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

series of training workshops in high performance computing will be held Feb. 12 through March 6, 2018, presented by CSCAR in conjunction with Advanced Research Computing – Technology Services (ARC-TS).

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.”
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Tuesday, Feb. 13, 1 – 4 p.m. (full descriptionregistration)
• Friday, Feb. 16, 9 a.m. – noon (full description | registration)

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.
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Monday, Feb. 19, 1 – 4 p.m. (full description | registration)
• Tuesday, March 6, 1 – 4 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.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Wednesday, Feb. 21, 1 – 5 p.m. (full description | registration)
• Friday, Feb. 23, 1 – 5 p.m. (full description | registration)

Hadoop and Spark workshop
Learn how to process large amounts (up to terabytes) of data using SQL and/or simple programming models available in Python, R, Scala, and Java.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Thursday, Feb. 22, 1 – 5 p.m. (full description | registration)

Peers Health and U-M begin research partnership using disability and workers’ comp healthcare data

By | General Interest, Happenings, News, Research

Peers Health and the University of Michigan are starting a two-year research project that will apply advanced learning technologies to a proprietary global database of millions of de-identified disability and workers’ compensation cases. The goals of the project include developing a prescriptive modeling framework to facilitate development of optimal return-to-work plans for injured or ill patients.

Public policy experts have begun to connect patients’ ability to perform their productive endeavors, such as their job, to their state of general health and well-being. The findings from this project, by helping define when someone objectively has returned to health, could inform decision-making in virtually every healthcare episode.

The principal investigators in the project, Dr. Brian Denton and Dr. Jenna Wiens, are both renowned experts in medical machine learning. Dr. Denton, a professor of Industrial and Operations Engineering and Urology, and Dr. Wiens, an assistant professor of Computer Science and Engineering, are both affiliated with the Michigan Institute of Data Science (MIDAS) at U-M.

Peers Health recently announced an expanded partnership with ODG, an MCG company and part of the Hearst Health Network, to aggressively acquire new data to enhance ODG functionality and to fuel this research. Jon Seymour, MD, CEO of Peers, said, “This is a new phase in medical publishing where raw data collection is the editorial function and cutting-edge machine learning is the technology factor. We turned to the University of Michigan due to its impressive data science programs spanning multiple departments, as well as the specific experience of Dr. Denton and Dr. Wiens in medical applications. We’re confident this initiative will attract many new data contributors along the way.”

“The collaboration with Peers Health is exciting because it provides data that can help build a model that will reduce the time — from both a safety and productivity perspective — for people to return to work following sickness or injury,” Denton said. “Streaming data in from existing patients will allow our model to adapt and improve over time.”

Wiens added: “These data contain a particularly interesting training label: days away from work. We hypothesize that this will be a strong signal for the type, timing, and effectiveness of the treatments and therapies.”

The U-M partnership with Peers was established by MIDAS and the university’s Business Engagement Center (BEC).

“This partnership illustrates the power of combining data from the healthcare industry with the data science expertise of U-M faculty,” said Dr. Alfred Hero, professor of Engineering and co-director of MIDAS.

“It is energizing for the BEC to be part of these innovative collaborative relationships that create real impact in the world,” added BEC Director Amy Klinke.

 

Video available from MIDAS Research Forum

By | General Interest, Happenings, News, Research

Video is now available from the MIDAS Research Forum held Dec. 1 in the Michigan League at http://myumi.ch/6vA3V

The forum featured U-M students and faculty showcasing their data science research; a workshop on how to work with industry; presentations from student groups; and a summary of the data science consulting and infrastructure services available to the U-M research community.

NOTE: The keynote presentation from Christopher Rozell of the Georgia Institute of Technology will be available in the near future.

U-M wraps up successful SC17 conference

By | General Interest, Happenings, HPC, News

Several University of Michigan researchers and professional IT staff attended the Supercomputing 17 (SC17) conference in Denver from Nov. 12-17, participating in a number of different ways, including demonstrations, presentations and tutorials.

U-M participation included:

  • Matt McLean, a Big Data systems administrator with ARC-TS, served as a panelist at a session titled “The ARM Software Ecosystem: Are We There Yet?” (Slides)
  • Jeff Sica, a research database administrator with ARC-TS, helped lead a Birds of a Feather session titled “Containers in HPC.” (Slides)
  • Quentin Stout (EECS) and Christiane Jablonowski (CLASP) taught the “Parallel Computing 101” tutorial.
  • Shawn McKee, U-M Department of Physics, and OSiRIS Principal Investigator, demonstrated Object Storage and Caching for Science (network topology diagrams)
  • Eric Boyd, Director of Research Networks, presented on Research Networking at the University of Michigan at the U-M exhibit booth.
  • Simon Adorf, Ph.D. Candidate, Chemical Engineering Department, U-M, presented on Simple Data and Workflow Management with Signac and GPU-Accelerated Predictive Material Design at the U-M exhibit booth.
  • ARC sponsored a networking and career networking reception put on by Women in HPC. ARC Director Sharon Broude Geva spoke at the event.
  • Amy Liebowitz, a network architect at ITS, worked on SCINet, a high-capacity network created every year for the conference. Liebowitz was on the routing team, which is responsible for installing, configuring and supporting the high performance conference network. The Routing Team also coordinated external connectivity with commodity Internet and R&E WAN service providers.

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

Reading and discussion group:  Data science in understanding and addressing climate change 

By | Educational, Events, General Interest, Happenings

CSCAR announces a reading and discussion group Data science in understanding and addressing climate change that will meet on the third or fourth (depending on the preferences of participants) Friday of every month between 3 and 5 pm. We will discuss reports and significant papers that illuminate fundamental issues in climate change science, policy, and management. The suggested format at this stage is that we discuss one science and one policy (or management) paper or chapter. The focus will be on the spatial (and temporal) dimensions of the issue and we will concentrate more on methods and techniques keeping the requirement for domain knowledge relatively low. We will lay emphasis on the conceptual part of the tools and techniques so that it is accessible to a wider set of participants, but will also get into the technical details.

This is an effort to bring people involved in climate change together from a data science perspective. The idea is to learn together in a fun environment and foster dialogue with a focus on how data science can provide the common ground for mutual learning and understanding.

 We will meet in Rackham, but we will be open to rotating the location. You will be able to participate remotely, if you choose to.

 If you are interested send an email to Manish Verma at manishve@umich.edu

 If you have any suggestion for discussion and reading let us know.  We will include chapters from the IPCC and US global change science programs in our discussion.

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.”