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The 2018 MICDE Symposium: Summary by Bradley Dice, Ph.D student in Physics and Computational Science

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This piece was first published in LinkedIn by Bradley Dice, U-M Ph.D student in Physics and Computational Science.

MICDE Symposium 2018: Computation, A Pillar of Science and a Lens to the Future

High-performance computing (HPC) is becoming an increasingly powerful tool in the hands of scientists, driving new discoveries in physical sciences, life sciences, and social sciences. The development of new (frequently domain-specific) approaches to machine learning and faster, smarter processing of sets of Big Data allows us to explore questions that were previously impossible to study. Yesterday, I presented a poster at the Michigan Institute for Computational Discovery & Engineering (MICDE) annual Symposium and attended a number of talks by researchers working at the intersection of high-performance computing and their domain science. The theme for the symposium was “Computation: A Pillar of Science and a Lens to the Future.”

Collaborative Computational Science with signac

My scientific work, and the work of my colleagues in the Glotzer lab, has been made vastly more efficient through the use of tools for collaborative science, particularly the signac framework. I presented a poster about how the signac framework (composed of open-source Python packages signacsignac-flow, and signac-dashboard) enables scientists to rapidly simulate, model, and analyze data. The name comes from painter Paul Signac, who, along with Georges Seurat, founded the style of pointillism. This neo-impressionist style uses tiny dots of color instead of long brushstrokes, which collectively form a beautiful image when the viewer steps back. This metaphor fits the way that a lot of science works: given only points of data, scientists aim to see the whole picture and tell its story. Since our lab studies materials, our “points” of data fit into a multidimensional parameter space, where quantities like pressure and temperature, or even particles’ shapes, may vary. Using this data, our lab computationally designs novel materials from nanoparticles and studies the physics of complex crystalline structures.

The core signac package, which acts as a database on top of the file system, helps organize and manage scientific data and metadata. Its companion tool signac-flow enables users to quickly define “workflows” that run on supercomputing clusters, determining what operations to perform and submitting the jobs to the cluster for processing. Finally, signac-dashboard (which I develop) provides a web-based data visualization interface that allows users to quickly scan for interesting results and answer scientific questions. These tools include tutorials and documentation, to help users acquaint themselves and get on to doing science as quickly as possible. Importantly, the tools are not specific to materials science. Many scientific fields have similar questions, and the toolkit can easily be applied in fields where exploration or optimization within parameter spaces are common, ranging from fluid mechanics to machine learning.

During the symposium, I learned a lot about how others are using scientific computing in their own work. The symposium speakers came from a wide range of fields, including biology, mathematics, and fluid dynamics. Some of my favorite talks are described below.

The Past: Phylogeny and Uncovering Life’s Origins

High-performance computing is enabling scientists to look in all sorts of directions, including into the past. Stephen Smith, Assistant Professor of Ecology and Evolutionary Biology at the University of Michigan, talked about his lab’s research in detecting evolutionary patterns using genomic data. From the wealth of genetic data that scientists have collected, the Smith lab aims to improve our understanding of the “tree of life”: the overarching phylogenetic tree that can explain the progress of speciation over time. Projects like Open Tree of Life and PHLAWD, an open-source C++ project to process data from the National Center for Biotechnology Information’s GenBank data source, are just two of the ways that open science and big data are informing our understanding of life itself.

The Present: From Algebra to Autonomy

Cleve Moler, the original author of the MATLAB language and chief mathematician, chairman, and cofounder of MathWorks, spoke about his career and how the tools MATLAB has provided for numerical linear algebra (and many other computational tasks) have been important for the development of science and engineering over the last 34 years. MATLAB is taught to STEM students in many undergraduate curricula, and is used widely across industry to simulate and model the behavior of real systems. Features like the Automated System Driving Toolbox are poised to play a role in autonomous vehicles and the difficult computational tasks inherent in their operation.

The Future: Parallel-in-Time Predictions and Meteorology

A significant challenge in weather and climate modeling is that supercomputer architectures are highly parallel, while many simulations of fluids are inherently serial: each timestep must be computed before the next timestep can begin. Beth Wingate, Professor of Mathematics at the University of Exeter and published poet, is developing a powerful approach that may change the way that such models work. Called “parallel-in-time,” it separates the effects of slow dynamics and fast dynamics, enabling parallel architectures to take advantage of longer timesteps and separate the work across many processors.


Computational science is growing rapidly, improving our ability to address the most pressing questions and the mysteries of our world. As new supercomputing resources come online, such as Oak Ridge National Laboratories’ Summit, the promise of exascale computing is coming ever closer to reality. I look forward to what the next year of HPC will bring to our world.

Faculty search in Computational Science at U-M

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MICDE is pleased to bring to your attention a faculty search in Computational Science at University of Michigan. This position will be filled within the Mechanical Engineering Department, but the search will be carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly visible affiliate of MICDE, and will leverage its resources.

We are interested in candidates of any rank, who can bring advances in computer science, data-driven modeling and/or mathematics to bear upon the most compelling questions in engineering science. MICDE strives to define future paradigms of computational science, in collaboration with traditional disciplines in engineering and science. This is the environment that a successful candidate will have to develop a career.

All applicants should submit, in PDF format:
(1) a detailed resume,
(2) a statement of research and teaching interests,
(3) up to three representative publications, and
(4) the names and contact information of at least three references.

Applications must be submitted electronically at http://me.engin.umich.edu/facultysearch.

The University of Michigan is a non-discriminatory/affirmative action employer and is responsive to the needs of dual career families.

For more information please visit https://me.engin.umich.edu/about/positions/faculty.

Computational Science around U-M: Ph.D. Candidate Shannon Moran (Chemical Engineering) has won an ACM SIGHPC Intel Fellowship

By | Happenings, HPC, Uncategorized

Moran_HighRes_SqShannon Moran, a Ph.D. Candidate in the department of Chemical Engineering, has won a 2017 SIGHPC Intel Fellowship. Shannon is a member of the Glotzer Group. They use computer simulation to discover the fundamental principles of how nanoscale systems of building blocks self-assemble, and to discover how to control the assembly process to engineer new materials.

ACM’s Special Interest Group on High Performance Computing is an international group with a major professional society that is devoted to the needs of students, faculty, researchers and practitioners in high performance computing. This year they awarded 12 fellowships around the country with the aim of increasing the diversity of students pursuing graduate degrees in data science and computational science, including women as well as students from racial/ethnic backgrounds that have been historically underrepresented in the computing field. The fellowship provides $15,000 annually for study anywhere in the world.

The fellowship is funded by Intel and is presented at the annual Super Computing conference that this year will take place in November 13-16 in Denver, Colorado.

MICDE sponsored miRcore Biotechnology Summer Camp for the second year in a row

By | Happenings, HPC, Uncategorized

miRcoreBioTec2017This year’s miRcore’s Biotechnology summer camp was a big success.  The participants had hands-on experience in a wet-lab, and with the UNIX command line while accessing U-M’s High Performance Computing cluster, Flux, in a research setting. For the second year in a row MICDE and ARC-ts sponsored the campers to access Flux as they learned the steps that are needed to run code in a computer cluster. The camp also combined theoretical thermodynamic practices that gave participants an overall research experience in nucleotide biotechnology.

miRcore’s camps are designed to expose high school students to career opportunities in biomedicine and to provide research opportunities beyond the classroom setting. For more information please visit http://www.mircore.org/summer-camps/.

SC2 presents the 2017 NVIDIA Visualization Challenge

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Courtesy of S. Alben (Mathematics)The Scientific Computing Student Club (SC2) and NVIDIA are presenting a Visualization Challenge, with prizes including two NVIDIA and sponsorship to enter present your results at the Scientific Visualization Showcase at Supercomputing ’17. If you are an U-M student doing research that involves coding, simulations, or data analysis, chances are you have a lot of data to show. But what is the best way to do it? What is the best way to reach your audience and convey your message? As the old adage says “a picture is worth a thousand words”, so making your data interactive, showing it in 3D, or making a video are a few options.

More information and registration at micde.umich.edu/sc2/challenge2017. Registration deadline is March 1, 2017.


MIDAS announces second round of Data Science Challenge Initiative awards, in health and social science

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Five research projects — three in health and two in social science — have been awarded funding in the second round of the Michigan Institute for Data Science Challenge Initiative program.

The projects will receive funding from MIDAS as part of the Data Science Initiative announced in fall 2015.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science. For more information, visit midas.umich.edu/challenges.

The projects, determined by a competitive submission process, are:

  • Title: Michigan Center for Single-Cell Genomic Data Analysis
    Description: The center will establish methodologies to analyze sparse data collected from single-cell genome sequencing technologies. The center will bring together experts in mathematics, statistics and computer science with biomedical researchers.
    Lead researchers: Jun Li, Department of Human Genetics; Anna Gilbert, Mathematics
    Research team: Laura Balzano, Electrical Engineering and Computer Science; Justin Colacino, Environmental Health Sciences; Johann Gagnon-Bartsch, Statistics; Yuanfang Guan, Computational Medicine and Bioinformatics; Sue Hammoud, Human Genetics; Gil Omenn, Computational Medicine and Bioinformatics; Clay Scott, Electrical Engineering and Computer Science; Roman Vershynin, Mathematics; Max Wicha, Oncology.
  • Title: From Big Data to Vital Insights: Michigan Center for Health Analytics and Medical Prediction (M-CHAMP)
    Description: The center will house a multidisciplinary team that will confront a core methodological problem that currently limits health research — exploiting temporal patterns in longitudinal data for novel discovery and prediction.
    Lead researchers: Brahmajee Nallamothu, Internal Medicine; Ji Zhu, Statistics; Jenna Wiens, Electrical Engineering and Computer Science; Marcelline Harris, Nursing.
    Research team: T. Jack Iwashyna, Internal Medicine; Jeffrey McCullough, Health Management and Policy (SPH); Kayvan Najarian, Computational Medicine and Bioinformatics; Hallie Prescott, Internal Medicine; Andrew Ryan, Health Management and Policy (SPH); Michael Sjoding, Internal Medicine; Karandeep Singh, Learning Health Sciences (Medical School); Kerby Shedden, Statistics; Jeremy Sussman, Internal Medicine; Vinod Vydiswaran, Learning Health Sciences (Medical School); Akbar Waljee, Internal Medicine.
  • Title: Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology
    Description: Using an app platform that integrates signals from both mobile phones and wearable sensors, the project will collect data from over 1,000 medical interns to identify the dynamic relationships between mood, sleep and circadian rhythms. These relationships will be utilized to inform the type and timing of personalized data feedback for a mobile micro-randomized intervention trial for depression under stress.
  • Lead researchers: Srijan Sen, Psychiatry; Margit Burmeister, Molecular and Behavioral Neuroscience.
    Research team:  Lawrence An, Internal Medicine; Amy Cochran, Mathematics; Elena Frank, Molecular and Behavioral Neuroscience; Daniel Forger, Mathematics; Thomas Insel (Verily Life Sciences); Susan Murphy, Statistics; Maureen Walton, Psychiatry; Zhou Zhao, Molecular and Behavioral Neuroscience.
  • Title: Computational Approaches for the Construction of Novel Macroeconomic Data
    Description: This project will develop an economic dataset construction system that takes as input economic expertise as well as social media data; will deploy a data construction service that hosts this construction tool; and will use this tool and service to build an “economic datapedia,” a compendium of user-curated economic datasets that are collectively published online.
    Lead researcher: Matthew Shapiro, Department of Economics
    Research team: Michael Cafarella, Computer Science and Engineering; Jia Deng, Electrical Engineering and Computer Science; Margaret Levenstein, Inter-university Consortium for Political and Social Research.
  • Title: A Social Science Collaboration for Research on Communication and Learning based upon Big Data
    Description: This project is a multidisciplinary collaboration meant to introduce social scientists, computer scientists and statisticians to the methods and theories of engaging observational data and the results of structured data collections in two pilot projects in the area of political communication and one investigating parenting issues. The projects involve the integration of geospatial, social media and longitudinal data.
    Lead researchers: Michael Traugott, Center for Political Studies, ISR; Trivellore Raghunathan, Biostatistics
    Research team: Leticia Bode, Communications, Georgetown University; Ceren Budak, U-M School of Information; Pamela Davis-Keane, U-M Psychology, ISR; Jonathan Ladd, Public Policy, Georgetown; Zeina Mneimneh, U-M Survey Research Center; Josh Pasek, U-M Communications; Rebecca Ryan, Public Policy, Georgetown; Lisa Singh, Public Policy, Georgetown; Stuart Soroka, U-M Communications.

For more details, see the press releases on the social science and health science projects.

MIDAS to host faculty meeting on NSF BIGDATA solicitation

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The Michigan Institute for Data Science (MIDAS) will hold a faculty meeting at noon on Thursday, January 19 (Suite 7625, School of Public Health I, 1415 Washington Heights) for the NSF 17-534 “Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)” solicitation.

The meeting will include an overview of the NSF solicitation, U-M Data Science Resources (MIDAS, CSCAR, ARC-TS) available to faculty responding to the NSF call, and an opportunity to network with other faculty.

MIDAS has also arranged for Sylvia Spengler, NSF CISE Program Director, to be available at 1:30 pm to answer questions regarding the BIGDATA solicitation.

We invite you to participate in the faculty meeting to share your ideas and interest in responding to this BIGDATA solicitation as well as interact with other faculty looking to respond to this funding mechanism.

For those unable to participate in person, you can join virtually using GoToMeeting:

A box lunch will be provided at the faculty meeting.  Your RSVP (https://goo.gl/forms/OYAuB8mWCOlx3fw73) is appreciated.

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

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Sharon Broude Geva, the Director of Advanced Research Computing at the University of Michigan, has been 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 83 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. Her term as vice-chair is effective for the 2017 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.

Video, slides available from U-M presentations at SC16

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Several University of Michigan researchers and research IT staff made presentations at the SC16 conference in Salt Lake City Nov. 13-17. Material from many of the talks is now available for viewing online:

  • Shawn McKee (Physics) and Ben Meekhof (ARC-TS) presented a demonstration of the Open Storage Research Infrastructure (OSiRIS) project at the U-M booth. The demonstration extended the OSiRIS network from its participating institutions in Michigan to the conference center in Utah. Meekhof also presented at a”Birds of a Feather” session on Ceph in HPC environments. More information, including slides, is available on the OSiRIS website.
  • Todd Raeker (ARC-TS) made a presentation on ConFlux, U-M’s new computational physics cluster, at the NVIDIA booth. Slides and video are available.
  • Nilmini Abeyratne, a Ph.D student in computer science, presented her project “Low Design-Risk Checkpointing Storage Solution for Exascale Supercomputers” at the Doctoral Showcase. A summary, slides, and poster can be viewed on the SC16 website.
  • Jeremy Hallum (ARC-TS) presented information on the Yottabyte Research Cloud at the U-M booth. His slides are available here.

Other U-M activity at the conference included Sharon Broude Geva, Director of Advanced Research Computing, participating in a panel titled “HPC Workforce Development: How Do We Find Them, Recruit Them, and Teach Them to Be Today’s Practitioners and Tomorrow’s Leaders?”; Quentin Stout (EECS) and Christiane Jablonowski (CLASP) teaching the “Parallel Computing 101” tutorial.

NVIDIA accepting applications for Graduate Fellowship Program

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NVIDIA has launched its 16th Annual Graduate Fellowship Program, which awards grants and technical support to graduate students who are doing outstanding GPU-based research.

This year NVIDIA is especially seeking doctoral students pushing the envelope in artificial intelligence, deep neural networks, autonomous vehicles, and related fields. The Graduate Fellowship awards are now up to $50,000 per student. These grants will be awarded in the 2017-2018 academic year.

Since its inception in 2002, the NVIDIA Graduate Fellowship Program has awarded over 130 Ph.D. graduate students with grants that have helped accelerate their research efforts.

The NVIDIA Graduate Fellowship Program is open to applicants worldwide. The deadline for submitting applications is Jan. 16, 2017. Eligible graduate students will have already completed their first year of Ph.D. level studies in the areas of computer science, computer engineering, system architecture, electrical engineering or a related area. In addition, applicants must also be engaged in active research as part of their thesis work.

For more information on eligibility and how to apply, visit http://research.nvidia.com/relevant/graduate-fellowship-program or email fellowship@nvidia.com.