Understanding How the Brain Processes Music Through the Bach Trio Sonatas

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This event is open to the public.

Daniel Forger, Professor of Mathematics and Computational Medicine and Bioinformatics
James Kibbie, Professor of Music and Chair of the Organ Department, University Organist
Caleb Mayer, Graduate Student Research Assistant (Mathematics)
Sarah Simko, Graduate Student Research Assistant (Organ Performance)

With support from the Data Science for Music Challenge Initiative through MIDAS, the team is taking a big data approach to understanding the patterns and principles of music. The project is developing and analyzing a library of digitized performances of the Trio Sonatas for organ by Johann Sebastian Bach, applying novel algorithms to study the music structure from a data science perspective. Organ students from the School of Music, Theatre & Dance will demonstrate how the Frieze Memorial Organ in Hill Auditorium is used to create big data files of live performances. The team will discuss how its analysis compares different performances to determine features that make performances artistic, as well as the common mistakes performers make. The digitized performances will be shared with researchers and will enable research and pedagogy in many disciplines, including data science, music performance, mathematics and music psychology.

Hadoop and Spark Workshop

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Overview

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. Computers will be provided to follow along with hands-on examples; users can also bring laptops.

Prerequisites

Intro to the Linux Command Line or equivalent. This course assumes familiarity with the Linux command line.

A user account on Flux. If you do not have a Flux user account, click here to go to the account application page at: https://arc-ts.umich.edu/fluxform/

Duo authentication.

Duo two-factor authentication is required to log in to the cluster. When logging in, you will need to type your UMICH password as well as authenticate through Duo in order to access Flux.

If you need to enroll in Duo, follow the instructions at Getting Started: How to Enroll in Duo.

click here to register

Instructor

Brock Palen
Director
ARC-TS

Brock has over 10 years of high performance computing and data intensive computing experience in an academic environment. He currently works with the team at ARC-TS to provide HPC, Data Science, storage, and other research computing services to the University. Brock also is the NSF XSEDE projects Campus Champion representing the schools to this and other national computing infrastructures and organizations.

Materials

Course Preparation

In order to participate successfully in the class exercises, you must have a Flux user account. The user account allows you to log in to the cluster, create, compile, and test applications, and transfer data into Hadoop’s filesystem for processing.

Flux user account

A single Flux user account can be used to prepare and submit jobs using various allocations. If you already already possess a user account, you can use it for this course, you can skip to “Flux allocation” below. If not, please visit https://arc-ts.umich.edu/fluxform to obtain one. A user account is free to members of the University community. Please note that obtaining an account requires human processing, so be sure to do this at least two business days before class begins.

Duo Authentication

Duo two-factor authentication is required to log in to the cluster. When logging in, you will need to type your UMICH password as well as authenticate through Duo in order to access Flux.

If you need to enroll in Duo, follow the instructions at Getting Started: How to Enroll in Duo.

More help

Please email hpc-support@umich.edu for questions, comments, or to seek further assistance.

2017 U-M Data Science Research Forum

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Forum Highlights

  • Oral and poster presentations on
    • Theoretical foundations of data science
    • Data science methodology
    • Data science applications in any research domain
    • Social impact of data science research
  • Networking Reception

All presentations will come from submissions in response to our call for abstracts
Abstract Submission Deadline: October 23, 2017
We welcome submission from all U-M data science researchers (faculty, staff, trainees)

Please register for this event.  Please also see the call for abstracts for instruction, and submit through the Abstract Submission Form.

Preliminary Schedule

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.”

Info sessions on graduate studies in computational and data sciences — Sept. 21 and 25

By | Educational, Events, General Interest, News, Research

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.

Times / Locations:

U-M, SJTU research teams share $1 million for data science projects

By | Data, General Interest, Happenings, News, Research

Five research teams from the University of Michigan and Shanghai Jiao Tong University in China are sharing $1 million to study data science and its impact on air quality, galaxy clusters, lightweight metals, financial trading and renewable energy.

Since 2009, the two universities have collaborated on a number of research projects that address challenges and opportunities in energy, biomedicine, nanotechnology and data science.

In the latest round of annual grants, the winning projects focus on data science and how it can be applied to chemistry and physics of the universe, as well as finance and economics.

For more, read the University Record article.

For descriptions of the research projects, see the MIDAS/SJTU partnership page.

New Data Science Computing Platform Available to U-M Researchers

By | General Interest, Happenings, HPC, News

Advanced Research Computing – Technology Services (ARC-TS) is pleased to announce an expanded data science computing platform, giving all U-M researchers new capabilities to host structured and unstructured databases, and to ingest, store, query and analyze large datasets.

The new platform features a flexible, robust and scalable database environment, and a set of data pipeline tools that can ingest and process large amounts of data from sensors, mobile devices and wearables, and other sources of streaming data. The platform leverages the advanced virtualization capabilities of ARC-TS’s Yottabyte Research Cloud (YBRC) infrastructure, and is supported by U-M’s Data Science Initiative launched in 2015. YBRC was created through a partnership between Yottabyte and ARC-TS announced last fall.

The following functionalities are immediately available:

  • Structured databases:  MySQL/MariaDB, and PostgreSQL.
  • Unstructured databases: Cassandra, MongoDB, InfluxDB, Grafana, and ElasticSearch.
  • Data ingestion: Redis, Kafka, RabbitMQ.
  • Data processing: Apache Flink, Apache Storm, Node.js and Apache NiFi.

Other types of databases can be created upon request.

These tools are offered to all researchers at the University of Michigan free of charge, provided that certain usage restrictions are not exceeded. Large-scale users who outgrow the no-cost allotment may purchase additional YBRC resources. All interested parties should contact hpc-support@umich.edu.

At this time, the YBRC platform only accepts unrestricted data. The platform is expected to accommodate restricted data within the next few months.

ARC-TS also operates a separate data science computing cluster available for researchers using the latest Hadoop components. This cluster also will be expanded in the near future.

2017 MICDE Annual Symposium

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Please join us for the Michigan Institute for Computational Discovery and Engineering 2017 Symposium. The event features eminent scientists from around the world and the U-M campus. The symposium this year focuses on the “New Era of Data-Enabled Computational Science.”

Speakers:

  • Frederica Darema — Director, Air Force Office of Scientific Research
  • George Karniadakis —  Professor of Applied Mathematics, Brown University
  • Tinsley Oden Director of the Institute for Computational Engineering and Sciences, V.P. for Research, University of Texas at Austin
  • Karen Willcox — Professor of Aerospace and Aeronautics, Massachusetts Institute of Technology, co-Director of MIT Center for Computational Engineering
  • Jacqueline H. Chen — Distinguished Member of Technical Staff at the Combustion Research Facility, Sandia National Laboratories
  • Laura Balzano — Assistant Professor, Electrical Engineering and Computer Science, U-M
  • Emanuel Gull — Assistant Professor, Physics

The symposium features a poster competition and more. For more information and to register go to http://micde.umich.edu/symposium17/

Past Symposia

2016 MICDE Annual Symposium

Research Computing Symposium Fall 2014 

 

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 Services at U-M

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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.