Graduate Studies in Computational & Data Sciences Info Session – Central Campus

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2016-06-14 11.13.52Learn 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.
  • The Graduate Certificate in Computational Neuroscience provides training in interdisciplinary computational neuroscience to graduate students in experimental neuroscience programs and to graduate students in quantitative science programs, such as physics, biophysics, mathematics and engineering. The curriculum includes required core computational neuroscience courses and coursework outside of the student’s home department research focus, i.e. quantitative coursework for students in experimental programs, and neuroscience coursework for students in quantitative programs.

Graduate Studies in Computational & Data Sciences Info Session – North Campus

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2016-06-14 11.13.52Learn 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.
  • The Graduate Certificate in Computational Neuroscience provides training in interdisciplinary computational neuroscience to graduate students in experimental neuroscience programs and to graduate students in quantitative science programs, such as physics, biophysics, mathematics and engineering. The curriculum includes required core computational neuroscience courses and coursework outside of the student’s home department research focus, i.e. quantitative coursework for students in experimental programs, and neuroscience coursework for students in quantitative programs.

MICDE announces 2017-2018 Fellowship recipients

By | Educational, General Interest, Happenings, News

MICDE is pleased to announce the recipients of the 2017-2018 MICDE Fellowships for students enrolled in the PhD in Scientific Computing or the Graduate Certificate in Computational Discovery and Engineering. We had 91 applicants from 25 departments representing 6 schools and colleges. Due to the extraordinary number of high quality applications we increased the number of fellowships from 15 to 20 awards. See our Fellowship page for more information.

AWARDEES

Diksha Dhawan, Chemistry
Negar Farzaneh, Computational Medicine & Bioinformatics
Kritika Iyer, Biomedical Engineering
Tibin John, Neuroscience
Bikash Kanungo, Mechanical Engineering
Yu-Han Kao, Epidemiology
Steven Kiyabu, Mechanical Engineering
Christiana Mavroyiakoumou, Mathematics
Ehsan Mirzakhalili, Mechanical Engineering
Colten Peterson, Climate and Space Sciences & Engineering
James Proctor, Chemical Engineering
Evan Rogers, Biomedical Engineering
Longxiu Tian, S. Ross School of Business
Jipu Wang, Nuclear Engineering and Radiological Sciences
Yanming Wang, Chemistry
Zhenlin Wang, Mechanical Engineering
Alicia Welden, Chemistry
Anna White, Industrial & Operations Engineering
Chia-Nan Yeh, Physics
Yiling Zhang, Industrial & Operations Engineering

HONORABLE MENTIONS

Geunyeong Byeon, Industrial & Operations Engineering
Ayoub Gouasmi, Aerospace Engineering
Joseph Kleinhenz, Physics
Jia Li, Physics
Changjiang Liu, Biophysics
Vo Nguyen, Computational Medicine & Bioinformatics
Everardo Olide, Applied Physics
Qiyun Pan, Industrial & Operations Engineering
Pengchuan Wang, Civil & Environmental Engineering
Xinzhu Wei, Ecology & Evolutionary Biology

U-M students invited to apply for MICDE fellowships — May 19 deadline

By | Educational, Funding Opportunities, General Interest, News

University of Michigan students are invited to apply for Michigan Institute for Computational Discovery and Engineering (MICDE) Fellowships for the 2017-2018 academic year. These $4,000 fellowships are available to students in both the Ph.D in Scientific Computing and the Graduate Certificate Program in Computational Discovery and Engineering. Applicants should be graduate students enrolled in either program, although students not yet enrolled but planning to do so may simultaneously submit program and fellowship applications.

Fellows will receive a $4,000 research fund that can be used to attend a conference, to buy a computer, or for any other approved activity that enhances the Fellow’s graduate experience. We also ask that Fellows attend at least 8 MICDE seminars between Fall 2017 and Winter 2018, attend one MICDE students’ networking event, and present a poster at the MICDE Symposium on March 22, 2018. For more details and to apply please visit http://micde.umich.edu/academic-programs/micde-fellowships/.

Interested students should download and complete the application form, and submit it with a one-page resume as a SINGLE PDF DOCUMENT to MICDE-apps@umich.edu. The due date for applications is May 19, 2017, 5:00 E.T. We expect to announce the awardees onJune 5, 2017.

We encourage applications from all qualified candidates, including women and minorities.

MICDE awards four Catalyst Grants

By | General Interest, News, Research

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.

MICDE Seminar: Ann Almgren, Lawrence Berkeley National Lab

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AnnAlmgrenBio: Ann S. Almgren is an applied mathematician who works as a staff scientist and acting group leader of the Center for Computational Sciences and Engineering at the Lawrence Berkeley National Laboratory. Her research interests involve the computational simulation of problems in astrophysics including the behavior of supernovae and white dwarfs. She earned a bachelor’s degree in physics from Harvard University in 1984 and master’s and doctoral degrees in mechanical engineering from the University of California, Berkeley in 1987 and 1991 respectively. After visiting the Institute for Advanced Study, she joined the applied mathematics group of the Lawrence Livermore National Laboratory in 1992, and moved to the Lawrence Berkeley Lab in 1996.

In 2015 she became a fellow of the Society for Industrial and Applied Mathematics “for contributions to the development of numerical methods for fluid dynamics and applying them to large-scale scientific and engineering problems.”[1]

 

[1] Biographical information taken from https://en.wikipedia.org/wiki/Ann_S._Almgren

MICDE Seminar: Andrea Lodi, Ecole Polytechnique, Montreal

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AndreaLodiBio: Dr. Andrea Lodi is a professor in the department of Mathematical and Industrial Engineering at the Polytechnique Montreal. He is the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal, Canada’s main chair in operations research.

Before joining the Polytechnique, Lodi was a professor in operations research in the faculty of electrical and information engineering at Italy’s University of Bologna. He earned his doctorate in systems engineering from this same university in 2000.

Lodi is interested in developing new models and algorithms that would make it possible to process a large quantity of data from multiple sources both rapidly and effectively. Through his research, he is looking for solutions designed to improve the electricity market, rail transport logistics, and health-care planning.

Lodi’s innovative work has earned him several awards, including the Google Faculty Research Award in 2010 and the IBM Faculty Award in 2011. In 2005 and 2006, he was a fellow in the prestigious Herman Goldstine program at the IBM Thomas J. Watson Research Center in New York.

In addition to co-ordinating several large-scale European projects in operations research, Lodi has also acted as a consultant for the IBM CPLEX research and development team since 2006. He has authored more than 70 publications in top mathematical programming journals; and has served as associate editor for several of these journals.[1]

 

This seminar is co-sponsored by the U-M Department of Industrial Operations & Engineering

 

[1] Biographical information taken from http://www.cerc.gc.ca/chairholders-titulaires/lodi-eng.aspx

MICDE Seminar: Anthony Wachs, University of British Columbia

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cropped-Anthony_Wachs_photo2Bio: Anthony Wachs is an assistant professor with a joint appointment in the departments of Mathematics and of Chemical and Biological Engineering at the University of British Columbia, Vancouver, Canada.

He received his B. Sc. and M. Sc. from the University Louis Pasteur of Strasbourg and his PhD from the Institut National Polytechnique of Grenoble in 2000. Right after, he was hired in 2001 as a Fluid Mechanics research engineer at IFP Energies nouvelles (IFPEN, at that time Institut Français du Pétrole) in Paris.

In 2009, he spent a one-year sabbatical at the nuclear research center of Cadarache in the south of France, where he worked for IRSN (the french national safety administration for nuclear energy). In 2010, he got his HDR (French Habilitation to Supervise Research) and was later promoted Scientific Advisor at IFPEN in Multiphase Flows and Scientific Computing. He then moved to IFPEN-Lyon where he supervised a group of researchers (including PhD and post-doc students) on the numerical simulation of reactive particulate flows (www.peligriff.com).

His main research interests are non-Newtonian Flows, Multiphase Flows and High Performance Computing. He collaborates extensively with academic groups in Canada, Brazil, France and Germany.

Research highlights: Running climate models in the cloud

By | General Interest, News, Research

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: http://dx.doi.org/10.1016/j.cageo.2016.09.014).

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.

Top image: http://www.cesm.ucar.edu/