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Sep 18
0

MICDE to provide data analysis and dissemination support for $18 million tobacco research center

By | General Interest, Happenings, News, Research

The University of Michigan School of Public Health will house a new, multi-institutional center focusing on modeling and predicting the impact of tobacco regulation, funded with an $18 million federal grant from the National Institutes of Health and the Food and Drug Administration.

The Center for the Assessment of the Public Health Impact of Tobacco Regulations will be part of the NIH and FDA’s Tobacco Centers of Regulatory Science, the centerpiece of an ongoing partnership formed in 2013 to generate critical research that informs the regulation of tobacco products.

The Michigan Institute for Computational Discovery and Engineering (MICDE) will support the center’s Data Analysis and Dissemination core by collecting national and regional survey data, conducting analysis of the use of tobacco products including vaping and e-cigarettes, and disseminate the resulting tobacco modeling parameters to other research centers and the Food and Drug Administration.

The center is led by MICDE affiliated faculty member Rafael Meza, associate professor of Epidemiology, and David Levy, professor of Oncology at Georgetown University.

For more on the center, see the press release from the U-M School of Public Health: https://sph.umich.edu/news/2018posts/tcors-091718.html

Sep 10
0

U-M part of new software institute on high-energy physics

By | General Interest, Happenings, News, Research

The University of Michigan is part of an NSF-supported 17-university coalition dedicated to creating next-generation computing power to support high-energy physics research.

Led by Princeton University, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will focus on developing software and expertise to enable a new era of discovery at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland.

Shawn McKee, Research Scientist in the U-M Department of Physics, is a co-PI of the institute. His work will focus on integrating and extending the Open Storage Grid networking activities with similar efforts at the LHC.

For more information, see Princeton’s press release, and the NSF’s announcement.

May 07
1

MICDE awards seven Catalyst Grants

By | General Interest, Happenings, News, Research

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum Computing
Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.
Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swim
Researchers: Silas Alben, Mathematics; Robert Deegan, Physics; Alex Gorodetsky, Aerospace Engineering
Description: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification
Researchers: Valeriy Ivanov, Civil and Environmental Engineering; Nikolaos Katopodes, Civil and Environmental Engineering; Darren McKague Climate and Space Sciences and Engineering; Khachik Sargsyan, Sandia National Labs.
Description: The research team will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets
Researchers: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic Inference
Researchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

For more on the Catalyst Grants, see http://micde.umich.edu/catalyst/.

Apr 10
4

Eric Parish, Aero Ph.D student, wins Von Neumann Fellowship from Sandia National Labs

By | Happenings, News, Research

Eric Parish

Eric Parish, who will graduate this spring with a Ph.D in Aerospace Engineering, is the 2018 recipient of the prestigious John von Neumann Postdoctoral Research Fellowship from Sandia National Laboratories (SNL). The highly competitive fellowship offers the opportunity to establish his own program at SNL to conduct innovative research in computational mathematics and scientific computing on advanced computing architectures.

Parish came to U-M from the University of Wyoming, and has developed innovative methodologies of computational math and physics with Prof. Karthik Duraisamy.

Parish said two of his accomplishments in his doctoral work have been developing data-driven solutions to computational physics problems using the NSF-funded ConFlux computing cluster, and bringing together ideas from statistical mechanics to develop efficient numerical solutions of complex partial differential equations.

“It was bridging a gap between communities,” he said of the latter effort.

“Eric came up with a particularly clever way of generalizing concepts from physics to develop a foundation to solve complex equations at a low cost in a mathematically rigorous fashion,” Duraisamy said. “He is one of the rare students who commands an exceptional grasp of applied mathematics, computing and physics, while being well-rounded in his organizational and communication skills. It has been a pleasure and a privilege to work with him.”

Parish said this research could eventually help usher the next generation of flight, for example, “hypersonic” aircraft that can travel at speeds of Mach 8-10. To help get there, his work moves the field toward a better understanding of the underlying physical phenomena via accurate numerical simulations.

At Sandia’s labs in Livermore, Calif., Parish said he plans to continue the work he started at U-M to develop “reduced order models”, which can process past simulation data to greatly reduce the computational cost of future simulations.

Parish said that conducting research at U-M, with the availability of high performance computing resources and a community of computational scientists to bounce ideas off of, helped push his research to a higher level.

“Within Aero, there are five or six very strong computational groups, which really helps me understand the fundamental aspects of what we’re doing, and what the addition of my small little delta means,” he said. “It’s very exciting to do computational research in that environment; it motivates me to come up with better code.”

In 2016, Parish received a $4,000 fellowship from the Michigan Institute for Computational Discovery and Engineering (MICDE). He used some of the funds to attend the International Workshop on Variational Multiscale Methods in Spain last year, where he met a few dozen people from around the world working on similar problems.

“It was fantastic to network and learn from them,” he said.

Parish grew up in Laramie, Wyo., before attending the University of Wyoming, where he played Division 1 golf. He said there was a small but active computational science community at U-W.

“For its size, there was a lot of good computational stuff there,” he said, adding that 10 years ago he would never have predicted the current direction of his career.

Golf played a significant role in his development as well, Parish said: “Being a successful student-athlete takes an extraordinary amount of work. The successes and failures I had … played an integral part in the development of my work ethic, time management skills, mental attitude, and overall growth as a person…I believe that the experience I gained as a student-athlete gave me a unique perspective and skill set that I was able to use to my advantage.”

As far as his future goes after Sandia, Parish said he plans to either continue in the national lab environment or to explore faculty positions so that he can teach and motivate students as his professors at Wyoming and Michigan did for him.

“I’m grateful for everyone’s help,” he said. “The doors that Michigan can open and the quality of people here are very apparent.”

A simulation of magnetohydrodynamic turbulence done on the ConFlux cluster with roughly 1 billion degree of freedom computation generating about 4TB of data.

Jan 11
0

2016-2017 MICDE Research Snapshot

By | Research

2016-2017 has been a year of sustained growth for MICDE’s research portfolio. The number of faculty affiliated with the institute stands at 130, spanning 30 departments and eight schools and colleges. The Center for Scientific Software Infrastructure was established to bring together the U-M community engaged in developing open scientific software. It will focus on establishing best practices for developing, disseminating and documenting scientific software in the public domain. Led by Prof. Emanuel Gull (Physics), the Center aims to provide training and support for researchers that are ready to transform their research codes into well-engineered software. It offers grant support in the form of programmers, consultants, and administrative assistance. It includes a portal to share your code with the research community at large.

MICDE’s two established centers, the Center for Network and Storage-Enabled Collaborative Computational Science (CNSECCS) and the Center for Data-Driven Computational Physics (DaCoP), each held their first symposium, showcasing their first year of research activities. This included evidence of the growing reach of OSiRIS, the open framework for storage, computation and collaboration against big scientific data, and the first results from ConFlux, U-M’s groundbreaking computing cluster for data-driven computational physics. These results have been presented at several conferences, and are appearing in the leading computational journals.

Vorticity field at a late time in the evolution of an elliptic vortex patch computed by a Lagrangian particle method with remeshing and treecode-accelerated evaluation of the Biot-Savart integral. (source: Ling Xu)

MICDE also funded its first round of Catalyst Grants, that are supporting four innovative computational science research projects. Research funded by the Catalyst Grants is breaking new ground, while helping define the future of computational science. This research consists of:

  • studies of the neuronal dynamics of learning and memory formation;
  • new algorithms for the complex, nonlinear dynamics of power grids;
  • novel integral equations methods using recent advances in numerical analysis;  
  • and probabilistic computational frameworks for rare but often catastrophic events.

The past academic year MICDE hosted 14 external speakers with backgrounds and research concentrations that span the breadth of computational science of today and the future. The series culminated in MICDE’s annual symposium: “The New Era of Data-Enabled Computational Science,” which featured talks by worldwide leaders in computational science, including U-M faculty. The symposium included a student poster competition with over 50 entries.

Dr. Ann Almgren from the Lawrence Livermore National Lab speaking about Next Generation AMR, part of the 2016-2017 MICDE Seminar Series

MICDE faculty are committed to growing the already strong U-M community of computational scientists. Over the past year, as before, we have organized a number of workshops to foster collaboration and put together interdisciplinary teams in response to funding calls from federal agencies and foundations.   MICDE offers faculty teams institutional support and direct links to our excellent educational programs as well as cyberinfrastructure, all of which strengthen faculty proposals. With the backing of our parent unit, Advanced Research Computing (ARC), and its technical and consulting services (ARC-Technology Services, and Consulting for Statistics, Computing and Analytics Research), this effort has raised over $22M in external funding over the past 2 years. This includes support from federal agencies (NSF, NIH, and DOD), as well as from industry.  We also work with the academic units at U-M to identify compelling new areas for recruiting the type of faculty members who will drive computational science in the future.

 

Dec 15
0

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.)

Oct 12
0

The University of Michigan is live on IBM.com

By | News, Research

IBM is showcasing the current research developed with ConFlux, our ground-breaking cluster that uses IBM’s HPC and storage technology to enable scientists to draw on huge volumes of bid data and use machine learning to create reliable models for compute-intensive research.

“ With IBM hardware boosting our HPC environment, we can offer scientists the tools to conduct research that could revolutionize entire industries. ”
Todd Raeker, Research Technology Consultant for the University of Michigan.

To learn more please visit http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=A323848E50678F66

 

Sep 20
0

U-M joins NSF-funded SLATE project to simplify scientific collaboration on a massive scale

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

From the Cosmic Frontier to CERN, New Platform Stitches Together Global Science Efforts

SLATE will enable creation of new platforms for collaborative science

Today’s most ambitious scientific quests — from the cosmic radiation measurements by the South Pole Telescope to the particle physics of CERN — are multi-institutional research collaborations requiring computing environments that connect instrumentation, data, and computational resources. Because of the scale of the data and the complexity of this science,  these resources are often distributed among university research computing centers, national high performance computing centers, or commercial cloud providers.  This can cause scientists to spend more time on the technical aspects of computation than on discoveries and knowledge creation, while computing support staff are required to invest more effort integrating domain specific software with limited applicability beyond the community served.  

With Services Layer At The Edge (SLATE), a $4 million project funded by the National Science Foundation, the University of Michigan joins a team led by the Enrico Fermi and Computation Institutes at University of Chicago to provide technology that simplifies connecting university and laboratory data center capabilities to the national cyberinfrastructure ecosystem. The University of Utah is also participating. Once installed, SLATE connects local research groups with their far-flung collaborators, allowing central research teams to automate the exchange of data, software and computing tasks among institutions without burdening local system administrators with installation and operation of highly customized scientific computing services. By stitching together these resources, SLATE will also expand the reach of domain-specific “science gateways” and multi-site research platforms.  

“Science, ultimately, is a collective endeavor. Most scientists don’t work in a vacuum, they work in collaboration with their peers at other institutions,” said Shawn McKee, a co-PI on the project and director of the Center for Network and Storage-Enabled Collaborative Computational Science at the University of Michigan. “They often need to share not only data, but systems that allow execution of workflows across multiple institutions. Today, it is a very labor-intensive, manual process to stitch together data centers into platforms that provide the research computing environment required by forefront scientific discoveries.”

SLATE works by implementing “cyberinfrastructure as code”, augmenting high bandwidth science networks with a programmable “underlayment” edge platform. This platform hosts advanced services needed for higher-level capabilities such as data and software delivery, workflow services and science gateway components.  

U-M  has numerous roles in the project including:

  • defining, procuring and configuring much of the SLATE hardware platform
  • working on the advanced networking aspects (along with Utah) which includes Software Defined Networking (SDN) and Network Function Virtualization (NFV),
  • developing the SLATE user interface and contributing to the core project design and implementation.

The project is similar to the OSiRIS project led by McKee, which also aims to remove bottlenecks to discovery posed by networking and data transfer infrastructure.

SLATE uses best-of-breed data center virtualization components, and where available, software defined networking, to enable automation of lifecycle management tasks by domain experts. As such, it simplifies the creation of scalable platforms that connect research teams, institutions and resources, accelerating science while reducing operational costs and development time. Since SLATE needs only commodity components, it can be used for distributed systems across all data center types and scales, thus enabling creation of ubiquitous, science-driven cyberinfrastructure.

slateAt UChicago, the SLATE team will partner with the Research Computing Center and Information Technology Services to help the ATLAS experiment at CERN, the South Pole Telescope and the XENON dark matter search collaborations create the advanced cyberinfrastructure necessary for rapidly sharing data, computer cycles and software between partner institutions.  The resulting systems will provide blueprints for national and international research platforms supporting a variety of science domains.  

For example, the SLATE team will work with researchers from the Computation Institute’s Knowledge Lab to develop a hybrid platform that elastically scales computational social science applications between commercial cloud and campus HPC resources. The platform will allow researchers to use their local computational resources with the analytical tools and sensitive data shared through Knowledge Lab’s Cloud Kotta infrastructure, reducing cost and preserving data security.

“SLATE is about creating a ubiquitous cyberinfrastructure substrate for hosting, orchestrating and managing the entire lifecycle of higher level services that power scientific applications that span multiple institutions,” said Rob Gardner, a Research Professor in the Enrico Fermi Institute and Senior Fellow in the Computation Institute. “It clears a pathway for rapidly delivering capabilities to an institution, maximizing the science impact of local research IT investments.”

Many universities and research laboratories use a “Science DMZ” architecture to balance security with the ability to rapidly move large amounts of data in and out of the local network. As sciences from physics to biology to astronomy become more data-heavy, the complexity and need for these subnetworks grows rapidly, placing additional strain on local IT teams.

That stress is further compounded when local scientists join multi-institutional collaborations, often requiring the installation of specialized, domain-specific services for the sharing of compute and data resources.

With SLATE, research groups will be able to fully participate in multi-institutional collaborations and contribute resources to their collective platforms with minimal hands-on effort from their local IT team. When joining a project, the researchers and admins can select a package of software from a cloud-based service — a kind of “app store” — that allows them to connect and work with the other partners.

“Software and data can then be updated automatically by experts from the platform operations and research teams, with little to no assistance required from local IT personnel,” said Joe Breen, Senior IT Architect for Advanced Networking Initiatives at the University of Utah’s Center for High Performance Computing. “While the SLATE platform is designed to work in any data center environment, it will utilize advanced network capabilities, such as software defined overlay networks, when the devices support it.”

By reducing the technical expertise and time demands for participating in multi-institution collaborations, the SLATE platform will be especially helpful to smaller universities that lack the resources and staff of larger institutions and computing centers. The SLATE functionality can also support the development of “science gateways” which make it easier for individual researchers to connect to HPC resources such as the Open Science Grid and XSEDE.

“A central goal of SLATE is to lower the threshold for campuses and researchers to create research platforms within the national cyberinfrastructure,” Gardner said.

Initial partner sites for testing the SLATE platform and developing its architecture include New Mexico State University and Clemson University, where the focus will be creating distributed  cyberinfrastructure in support of large scale bioinformatics and genomics workflows. The project will also work with the Science Gateways Community Institute, an NSF funded Scientific Software Innovation Institute, on SLATE integration to make gateways more powerful and reach more researchers and resources.

###

The Computation Institute (CI), a joint initiative of the University of Chicago and Argonne National Laboratory, is an intellectual nexus for scientists and scholars pursuing multi-disciplinary research and a resource center for developing and applying innovative computational approaches. Founded in 1999, it is home to over 100 faculty, fellows, and staff researching complex, system-level problems in such areas as biomedicine, energy and climate, astronomy and astrophysics, computational economics, social sciences and molecular engineering. CI is home to diverse projects including the Center for Robust Decision Making on Climate and Energy Policy, Knowledge Lab, The Urban Center for Computation and Data and the Center for Data Science and Public Policy.

For more information, contact Dan Meisler, Communications Manager, Advanced Research Computing at U-M: dmeisler@umich.edu, 734-764-7414

Sep 07
0

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:

Aug 11
0

Siqian Shen (IOE) to receive an Early Career Award from the Department of Energy

By | News, Research

siqian-shen-featuredMICDE Associate Director Siqian Shen has been selected to receive an Early Career Award for the Department of Energy Office of Science by the DoE Office of Advanced Scientific Computing Research. The objective of her proposal titled “Extreme‐Scale Stochastic Optimization and Simulation via Learning‐Enhanced Decomposition and Parallelization” is to develop an efficient and unified framework that integrates machine learning with discrete optimization and risk‐averse modeling. The models considered represent a broad class of complex decision‐making problems, where 0‐1 or continuous decisions are made before and/or after knowing multiple and potentially correlated sources of uncertainties. This research will shed new light on the traditional decomposition algorithms for high‐performance computing.

Prof. Shen was recently promoted to Associate Professor of Industrial and Operations Engineering. To learn more about her research please visit http://micde.umich.edu/faculty-member/siqian-shen/.

The Early Career Award program from the US Department of Energy is a funding opportunity for researchers in universities and DOE national laboratories to support the development of individual research programs of outstanding scientists early in their careers. For the past 8 years this program has helped stimulate research careers in the disciplines supported by the DOE Office of Science. These include Advanced Scientific Computing Research (ASCR); Biological and Environmental Research (BER); Basic Energy Sciences (BES), Fusion Energy Sciences (FES); High Energy Physics (HEP), and Nuclear Physics (NP).