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

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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

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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.

Designing optimal shunts for newborns with heart defects using computational modeling

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shuntFor babies born with hypoplastic left heart syndrome, several open-heart surgeries are required. During Stage I, a Norwood procedure is performed to construct an appropriate circulation to both the systemic and the pulmonary arteries. The pulmonary arteries receive flow from the systemic circulation, often by using a Blalock-Taussig (BT) shunt between the innominate artery and the right pulmonary artery. This procedure causes significantly disturbed flow in the pulmonary arteries.

A group of researchers led by U-M Drs. Ronald Grifka and Alberto Figueroa used computational hemodynamic simulations to demonstrate its capacity for examining the properties of the flow through and near the BT shunt. Initially, the researchers constructed a computational model which produces blood flow and pressure measurements matching the clinical magnetic resonance imaging (MRI) and catheterization data. Achieving this required us to determine the level of BT shunt occlusion; because the occlusion is below the MRI resolution, this information is difficult to recover without the aid of computational simulations. The researchers determined that the shunt had undergone an effective diameter reduction of 22% since the time of surgery. Using the resulting geometric model, they showed that we can computationally reproduce the clinical data. The researchers then replaced the BT shunt by with a hypothetical alternative shunt design with a flare at the distal end. Investigation of the impact of the shunt design revealed that the flare can increase pulmonary pressure by as much as 7%, and flow by as much as 9% in the main pulmonary branches, which may be beneficial to the pulmonary circulation.

Read more in Frontiers in Pediatrics.

MICDE awards four Catalyst Grants

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The Michigan Institute for Computational Discovery and Engineering has awarded its first round of Catalyst Grants, providing $75,000 each to four innovative projects in computational science. The proposals were judged on novelty, likelihood of success, potential for external funding, and potential to leverage ARC’s existing computing resources.

The funded projects are:

Title: From Spiking Patterns to Memory formation — Tools for Analysis and Modeling of Network-wide Cognitive Dynamics of the Brain
Researchers: Sara Aton, Department of Molecular, Cellular and Developmental Biology and Michal Zochowski, Department of Physics, Biophysics Program
Description: The aim of the research is to develop models as well as analysis tools to understand network-wide spatio-temporal patterning underlying experimentally observed neural spiking activity. The research team has developed novel tools to analyze dynamics of neuronal representations across time, before during and after learning. These tools, for the first time, compare the stability of network dynamics before and after memory encoding.

Title: Integral Equation Based Methods for Scientific Computing
Researcher: Robert Krasny, Department of Mathematics
Description: This project expands the application of numerical methods in which the differential equation is first converted into an integral equation by convolution with the Green’s function, followed by discretization and linear solution. Recent advances in numerical analysis and computing resources make this expansion possible, and the research team believes that integral equation-based numerical methods are superior to traditional methods in both serial and parallel computations. The project will attempt to apply these numerical methods to studies of viscous fluid flow, protein/solvent electrostatics, and electronic structure.

Title: Computational Energy Systems
Researchers: Pascal Van Hentenryck, Industrial and Operations Engineering (IOE); E. Byon, IOE; R. Jiang, IOE; J. Lee, IOE; and J. Mathieu, Electrical Engineering and Computer Science
Description: The research team aims to develop new algorithms for the U.S. electrical power grid that integrate renewable energy sources, electrification of transportation systems, the increasing frequency of extreme weather events, and other emerging contingencies.

Title: Black Swans, Dragon Kings, and the Science of Rare Events: Problems for the Exascale Era and Beyond
Researchers: Venkat Raman, Aerospace Engineering; Jacqueline Chen, Sandia National Laboratory; and Ramanan Sankaran, Oak Ridge National Laboratory.
Description: The purpose of the project is to develop the computational frameworks for exploring the tails of distributions, which lead to rare but consequential (and often catastrophic) outcomes. Two such rare events are “Black Swans” (occurring from pre-existing but unencountered events) and “Dragon Kings (occurring due to an external shock to the system). The methods developed are expected to have application in aerospace sciences, power generation and utilization, chemical processing, weather prediction, computational chemistry, and other fields.

Another round of Catalyst Grants will be awarded next year.

U-M, Toyota Research Institute partner in $2.4M battery project

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With a $2.4 million investment from the Toyota Research Institute, University of Michigan researchers will develop computer simulation tools to predict automotive battery performance.

The project is part of a four-year, $35 million investment with research entities, universities and companies on research that uses artificial intelligence to help accelerate the design and discovery of advanced materials, TRI has announced.

Initially, the program will aim to help revolutionize materials science and identify new advanced battery materials and fuel cell catalysts that can power future zero-emissions and carbon-neutral vehicles.

“Toyota recognizes that artificial intelligence is a vital basic technology that can be leveraged across a range of industries, and we are proud to use it to expand the boundaries of materials science,” said Eric Krotkov, TRI chief science officer.

“Accelerating the pace of materials discovery will help lay the groundwork for the future of clean energy and bring us even closer to achieving Toyota’s vision of reducing global average new-vehicle CO2 emissions by 90 percent by 2050.”

The project, under the auspices of the Michigan Institute for Computational Discovery and Engineering at U-M, will combine mathematical models of the atomic nature and physics of materials with artificial intelligence.

“At the University of Michigan, we look forward to collaborating with TRI to advance computational materials science using machine learning principles,” said principal investigator Krishna Garikipati, professor of mechanical engineering and mathematics.

Also involved from U-M are Vikram Gavini, associate professor of mechanical engineering and materials science and engineering, and Karthik Duraisamy, assistant professor of aerospace engineering.

“The timing and goals of this program are well-aligned with the paradigm of data-enabled science that we have been promoting via the Michigan Institute for Computational Discovery and Engineering, and the Center for Data-Driven Computational Physics,” Duraisamy said.

The U-M project will use the ConFlux cluster, an innovative, new computing platform that enables computational simulations to interface with large datasets.

In addition to U-M, TRI’s newly funded research projects include collaborations with Stanford University, the Massachusetts Institute of Technology, University at Buffalo, University of Connecticut and the U.K.-based materials science company Ilika. TRI is also in ongoing discussions with additional research partners.

Research will merge advanced computational materials modeling, new sources of experimental data, machine learning and artificial intelligence in an effort to reduce the time scale for new materials development from a period that has historically been measured in decades.

Research programs will follow parallel paths, working to identify new materials for use in future energy systems as well as to develop tools and processes that can accelerate the design and development of new materials more broadly, according to TRI.

In support of these goals, TRI will partner on projects focused on areas including:

  • The development of new models and materials for batteries and fuel cells.
  • Broader programs to pursue novel uses of machine learning, artificial intelligence and materials informatics approaches for the design and development of new materials.
  • New automated materials discovery systems that integrate simulation, machine learning, artificial intelligence or robotics.

Accelerating materials science discovery represents one of four core focus areas for TRI, which was launched in 2015 with mandates to also enhance auto safety with automated technologies, increase access to mobility for those who otherwise cannot drive and help translate outdoor mobility technology into products for indoor mobility.

Workshop co-chaired by MIDAS co-director Prof. Hero releases proceedings on inference in big data

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The National Academies Committee on Applied and Theoretical Statistics has released proceedings from its June 2016 workshop titled “Refining the Concept of Scientific Inference When Working with Big Data,” co-chaired by Alfred Hero, MIDAS co-director and the John H Holland Distinguished University Professor of Electrical Engineering and Computer Science.

The report can be downloaded from the National Academies website.

The workshop explored four key issues in scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models.

The workshop brought together statisticians, data scientists and domain researchers from different biomedical disciplines in order to identify new methodological developments that hold significant promise, and to highlight potential research areas for the future. It was partially funded by the National Institutes of Health Big Data to Knowledge Program, and the National Science Foundation Division of Mathematical Sciences.

Combining simulation and experimentation yields complex crystal nanoparticle

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The most complex crystal designed and built from nanoparticles has been reported by researchers at Northwestern University and the University of Michigan. The work demonstrates that some of nature’s most complicated structures can be deliberately assembled if researchers can control the shapes of the particles and the way they connect using DNA.

The U-M researcher is Sharon C. Glotzer, the John W. Cahn Distinguished University Professor of Engineering and the Stuart W. Churchill Collegiate Professor of Chemical Engineering. The work is published in the March 3 issue of Science. ARC’s computational resources supported the work.

Research highlights: Running climate models in the cloud

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Xianglei Huang

Can cloud computing systems help make climate models easier to run? Assistant research scientist Xiuhong Chen and MICDE affiliated faculty Xianglei Huang, from Climate and Space Sciences and Engineering (CLASP), provide some answers to this question in an upcoming issue of Computers & Geoscience (Vol. 98, Jan. 2017, online publication link:

Teaming up with co-authors Dr. Chaoyi Jiao and Prof. Mark Flanner, also in CLASP, as well as Brock Palen and Todd Raeker from U-M’s Advanced Research Computing – Technology Services (ARC-TS), they compared the reliability and efficiency of Amazon’s Web Service – Elastic Compute 2 (AWS EC2) with U-M’s Flux high performance computing (HPC) cluster in running the Community Earth System Model (CESM), a flagship climate model in the U.S. developed by the National Center for Atmospheric Research.

The team was able to run the CESM in parallel on an AWS EC2 virtual cluster with minimal packaging and code compiling effort, finding that the AWS EC2 can render a parallelization efficiency comparable to Flux, the U-M HPC cluster, when using up to 64 cores. When using more than 64 cores, the communication time between virtual EC2 exceeded the distributed computing time.

Until now, climate and earth systems simulations had relied on numerical model suites that run on thousands of dedicated HPC cores for hours, days or weeks, depending on the size and scale of each model. Although these HPC resources have the advantage of being supported and maintained by trained IT support staff, making them easier to use them, they are expensive and not readily available to every investigator that needs them.

Furthermore, the systems within reach are sometimes not large enough to run simulations at the desired scales. Commercial cloud systems, on the other hand, are cheaper and accessible to everyone, and have grown significantly in the last few years. One potential drawback of cloud systems is that the user needs to provide and install all the software and the IT expertise needed to run the simulations’ packages.

Chen and Huang’s work represents an important firstxiangleihuangpost2016 step in the use of cloud computing in large-scale climate simulations. Now, cloud computing systems can be considered a viable alternate option to traditional HPC clusters for computational research, potentially allowing researchers to leverage the computational power offered by a cloud environment.

This study was sponsored by the Amazon Climate Initiative through a grant awarded to Prof. Huang. The local simulation in U-M was made possible by a DoE grant awarded to Prof. Huang.

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