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Winning posters announced for MICDE 2018 Symposium

By | Events, General Interest, Happenings, News

Approximately 50 posters from post-docs and graduate students across campus entered the Poster Competition at the 2018 MICDE Symposium on March 22, 2018. We’re proud to announce the winners:

  • First Place ($500): “Modeling and Enhanced Sampling of Protein-Protein Recognition,” Yanmin Wang, Chemistry
  • Second Place ($300): “Non-Newtonian Computational Model of Thrombosis Initiation,” Sabrina Lynch, Biomedical Engineering
  • Third Place ($200): “Computational Modeling of Particle-Laden Flows,” Gregory Shallcross, Sarah Beetham, and Yuan Yao, Mechanical Engineering
  • Honorable Mention:UM/LISA: Efficient Linear and Nonlinear Guided Wave Simulation,” Hui Zhang, Aerospace Engineering
  • Honorable Mention:Temperature-Dependent Green’s Function Methods for Electronic Structure Calculations,” Alicia Welden, Chemistry
  • Honorable Mention:Non-invasive Diagnostics of Coronary Artery Disease using Machine Learning and Computational Fluid Dynamics,” Kritika Iyer, Biomedical Engineering
  • Honorable Mention:Automated Diagnosis and Prognosis System for Traumatic Brain Injury Patients with Subdural Hematoma,” Negar Farzaneh

ConFlux cluster expands

By | General Interest, Happenings, HPC, News

ARC-TS has installed 15 new compute nodes into the ConFlux cluster. These nodes have the same 20 cores CPU as the original set, but with 256 GB of RAM instead of 128 GB. Neither the original nodes nor the newly added ones contain any GPUs

As a result, jobs should spend less time in queue, and users can be more liberal in their memory requirements.

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)

2016-2017 Education Snapshot

By | Educational, General Interest, News

Over the past year, MICDE’s educational programs and activities have experienced tremendous growth. The Graduate Certificate in Computational Discovery and Engineering currently has 50 students enrolled, spanning 19 departments from 5 different schools and colleges. Sixteen students graduated within the last academic year, and 44 have graduated since the Graduate Certificate was established in 2013. Even further, the number of women in the program went from zero in 2014 to 15 currently enrolled.

The Ph.D. in Scientific Computing has experienced extraordinary growth, with 74 students enrolled from 20 departments, and four schools or colleges. We added a section to our web site with both programs’ alumni information.

We are working to broaden as well as to deepen the activities and resources available to students in both programs. Twenty MICDE fellowships were awarded this academic year to students in our programs. We continued to sponsor student software teams at competitions, as well as individual students presenting their work at leading conferences. On-campus, MICDE student activities include networking lunches, and the Scientific Computing Student Club (SC2). On the programmatic front, our non-engineering students now have access to a CAEN account that gives them more options to connect and use U-M High Performance Computing resources. Relevant grant opportunities for students are tracked and updated in MICDE’s grant webpage

2016-2017 MICDE Fellow Yuxi Chen (ClaSp) presenting his work at the MICDE Annual Symposium

Several educational projects and initiatives are afoot at MICDE, including a Massively Open Online Class (MOOC) in Computational Thinking targeting both high school students and their teachers. This MOOC aims to introduce learners to algorithmic approaches to problems. This initiative is being developed in collaboration with the School of Education, the office of Academic Innovation, and with input from a number of high schools in the Detroit Metropolitan Area.The two new courses launched by MICDE faculty last year, Methods and Practices of Scientific Computing, and Data-Driven Analysis and Modeling of Complex Systems, were successful in their first offerings during the 2016-2017 academic year, and are being offered again in 2017-2018. Other teams of MICDE faculty are at work across campus to develop new courses in computational science.

2016-2017 Outreach and Industrial Engagement Snapshot

By | General Interest

2017 miRcore’s GIDAS Biotechnology Summer Camp participants

Community Outreach

MICDE remains committed to advancing the understanding of science in general, and computational science in particular, in the community. To this end we have continued our support of internal and external organizations. Externally, our ongoing support of the non profit science outreach group, miRcore, included running MICDE sponsored compute cycles on Flux for high school students participating in miRcore’s computational biology summer camps through their student network called GIDAS. We also continued to support the undergraduate Biosoftware Team that has competed in the International Genetically Engineered Machine (iGEM) year competition for the past five years. The team participates in the software track aimed for computer scientists and developers to nurture their knowledge of biology, and for computational biologists, bioinformaticians and biologists to enhance their aptitude for building software. Over the past couple of years, the team has been developing ProtoCat, a software developed to address the issue of reproducibility in synthetic biology. It is a collaborative platform on which researchers share their experiment protocols and users can customize them to meet their own needs. For the third year in a row, the team returned with a gold medal.

2017 BioSoftware Team

Internally, less than two years since its inception, the Scientific Computing Student Club (SC2) has established several activities that complement the formal training in computational science available at U-M, including through MICDE’s PhD in Scientific Computing, and Graduate Certificate in Computational Discovery and Engineering. Over the past year, the SC2 had his own invited speakers, organized tours to the Flux facility and the U-M 3D Lab, organized the first Visualization Challenge, co-sponsored by NVIDIA, and just recently added a section on its web page for academic and non-academic job opportunities. During the 2017 Fall Term, SC2 students ran a weekly Machine Learning Collaborative Workshop, and the group is planning a hands-on series on code parallelization.

Industrial Engagement

We continue working towards increasing our engagement with industry. Over the last two years, in addition to NVIDIA, MICDE has established partnerships with IBM, through the joint design and development of our computer cluster, ConFlux, and with Toyota Research Institute, through a funded project on scientific software for materials research. We are now working in partnership with the U-M Business Engagement Center to create an MICDE industrial affiliates program, which will provide many additional avenues for interaction between our students or faculty and industry.

 

 

 

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether this notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)

ARC Director Sharon Broude Geva re-elected vice-chair of Coalition for Academic Scientific Computing

By | General Interest, News

Sharon Broude Geva, the Director of Advanced Research Computing at the University of Michigan, has been re-elected vice-chair of the Coalition for Academic Scientific Computation (CASC).

Founded in 1989, CASC advocates for the use of advanced computing technology to accelerate scientific discovery for national competitiveness, global security, and economic success. The organization’s members represent 84 institutions of higher education and national labs.

The vice-chair position is one of four elected CASC executive officers. The officers work closely as a team with the director of CASC. The vice-chair also leads CASC meeting program committees, is responsible for recruitment of new members, substitutes for the chair in his or her absences, and assists with moderating CASC meetings.

Geva served as CASC secretary in 2015 and 2016, and one term as vice-chair in 2017. Her next term as vice-chair is effective for the 2018 calendar year.

The other executive officers for 2017 are are Rajendra Bose, Chair, Columbia University; Neil Bright, Secretary, Georgia Institute of Technology; and Andrew Sherman, Treasurer, Yale University. Curt Hillegas of Princeton University is immediate past chair.

The 2018 CASC brochure is available online.

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.

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.