“Get non-Real”: Department of Energy grant funds novel research in High-Performance Algorithms at U-M

By | Feature, Research

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,

Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science
Source: www.energy.gov/science/articles/department-energy-invests-28-million-novel-research-high-performance-algorithms

New research from the University of Michigan will help revolutionize the data processing pipeline with state-of-the-art algorithms to optimize the collection and processing of any kind of data. Algorithms available now are built for real data, meaning real numbers, however, most of the data we see on the internet is non-real, like discrete data, or categorical. This project is part of a $2.8 million grant from the Department of Energy on algorithms research, which is the backbone of predictive modeling and simulation. The research will enable DOE to set new frontiers in physics, chemistry, biology, and other domains. 

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”

The U-M project, led by associate professor Laura Balzano and assistant professor Hessam Mahdavifar, both of electrical engineering and computer science, is one of six chosen by DOE to cover several topics at the leading-edge of algorithms research. According to the DOE, researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.

Laura Balzano and Hessam Mahdavifar portraits

Laura Balzano, associate professor of electrical engineering and computer science (left); Hessam Mahdavifar assistant professor of electrical engineering and computer science (right)

Balzano and Mahdavifar, both Michigan Institute for Computational Discovery and Engineering (MICDE) affiliated faculty members, will use a $300,000 portion of the overall grant to study randomized sketching and compression for high-dimensional non-real-valued data with low-dimensional structures.

“Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information,” said Balzano. “Sketches work well because scientific data are generally highly redundant in nature, often following a perturbed low-dimensional structure. Hence, low-rank models and sketching that preserves those model structures are ubiquitous in many machine learning and signal processing applications.” 

Even though a lot of the data used and processed in scientific and technological applications are best modeled mathematically as discrete, categorical or ordinal data, most state-of-the art randomized sketching algorithms focus on real-valued data. To add to this, in practical applications, treating high-dimensional data can be challenging in terms of computational and memory demands. Thus, the proposed project will significantly expand the applicability of randomized sketching.

“A key to data-driven modeling is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying mathematical structure that is often common across application areas,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics.”This research and the work that Laura and Hessam are doing is critically important to the advancement of computational discovery.”

Fall 2021 Information Sessions

By | Educational, Events

Fall 2021 information sessions on graduate programs in computational and data sciences at U-M

U-M graduate students interested in computational and data sciences are invited to learn about joint programs that will prepare them for success in computationally intensive fields. The programs are organized by the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science. Both institutes offer vast training and networking opportunities, including webinar series, symposia and student centered events.

Two sessions are scheduled

The sessions will address:

  • The Graduate Certificate in Computational Discovery and Engineering: trains students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments.

  • The Graduate Certificate in Data Science: focuses on developing core proficiencies in data analytics: modeling, technology and practice.

  • The Graduate Certificate in Computational Neuroscience: provides training in interdisciplinary computational neuroscience to students in experimental neuroscience programs, and to students in quantitative science programs, such as physics, biophysics, mathematics and engineering.

  • The  Ph.D. in Scientific Computing: 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.”

MICDE catalyst grant leads to new NSF funding to study cascade “ecohydromics” in the Amazonian headwater system

By | Feature, News, Research

The Amazon Basin cycles more water through streamflow and evaporation than any other contiguous forest in the world, and transpiration by trees is a critical part of this cycle. Understanding how plant roots, stems, and leaves interact with soil water to regulate forest transpiration across landscapes is a critical knowledge gap, especially as climate changes. Professor Valeriy Ivanov, from the Department of Civil and Environmental Engineering at U-M, is the lead investigator in a newly NSF funded project that links diverse disciplines – plant ecophysiology, ecology, and hydrology – and will build a unique modeling framework to characterize landscape variation in physiological and hydrological processes in the Amazon Basin. The framework will integrate a wide array of field observations with detailed watershed modeling for hypothesis testing. The team includes Tyeen Taylor, research fellow also from the Civil and Environmental Engineering Department at U-M, and many collaborators in the U.S. at the University of Arizona, University of West Virginia, University of Nebraska, as well as Brazilian researchers at the Federal University of Eastern Para, and Federal University of Amazonas, National Institute for Amazonian Research, and Eastern Amazon Agricultural Agency. Detailed, physical models of ecophysiology and above- and below-ground hydrology will be informed by observations of leaf physiology, tree morphological traits, soil moisture, groundwater, and streamflow. Data and models will be integrated employing novel tools in probabilistic learning and uncertainty quantification. The computational framework tools to be used in this project were developed in part with the support from MICDE Catalyst grant program for the 2018 project “Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification” led by Prof. Ivanov. 

Given (a) a mechanistic model M (e.g., a stomatal conductance model), (b) one can treat its inputs 𝛏 (e.g., parameters) as random variables. These inputs are sampled and model simulations are carried out. Using (c) PCEs, we construct a surrogate model that best approximates the model output – left-hand-side of (c). The surrogate is then evaluated with Monte Carlo simulations and used for (d) parameter inference. (d.1) is the flow of outputs from the surrogate model into a likelihood function L (D | 𝛏) to compare the surrogate model output and observed data D. This inference produces the posterior distribution for 𝛏. This pdf can then be sent back to the surrogate in (d.2) to reduce the uncertainty in the inputs and to obtain pdf for a quantity of interest (e).

“The reduced-ordered modeling approach developed during the MICDE Catalyst grant project is a key element of the new project,” said Prof. Ivanov, “the MICDE seed funding has allowed us to build a general framework that is applicable to a wide range of computational applications in earth-system science, and thus made our project proposal more competitive”.

The MICDE Catalyst Grants program funds projects that have the potential to catalyze and reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science. This new NSF project is an example of the reach of the program.

Read more.

We welcome 20 students to the 2021-22 class of MICDE graduate fellows

By | Feature, News

MICDE is proud to announce the recipients of the 2021 MICDE graduate fellowships. The fellows’ research projects involve the use and advancement of scientific computing techniques and practices. “This year, MICDE awarded fellowships in a wide array of disciplines ranging from chemistry to biostatistics and interdisciplinary mathematics to applied physics,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics. “Engineering is also well represented with fellows focused on disciplines such as aerospace, biomedical, civil and environmental, climate and space, industrial and operations, materials science, mechanical, and naval architecture and marine engineering.”

For the past seven years, MICDE has awarded fellowships to over 135 graduate students from our large community of computational scientists. “I am so excited and honored to be a part of the MICDE Fellowship program. My research interest is in an interdisciplinary field between healthcare and data science. This fellowship symbolizes my core value for career development as a data scientist in healthcare,” said 2021 MICDE Fellowship recipient Hyeon Joo, Ph.D. pre-candidate in health infrastructure and learning systems and scientific computing. The MICDE graduate student top-off fellowship provides students with a stipend to use for supplies, technology, and other materials that will further their graduate education and research. Among other things, awards have helped many to travel to conferences and meetings around the world to share the rich and diverse research in computational science being carried out at U-M.

Yifu An, Climate and Space Sciences Engineering
Andre Antoine, Applied Physics
Shreyas Bhat, Industrial and Operations Engineering
Erin Burrell, Mechanical Engineering and Scientific Computing
Alanah Cardenas-O’Toole, Climate and Space Sciences Engineering
Brian Chen, Applied and Interdisciplinary Mathematics
Xinyu Fei, Industrial and Operations Engineering and Scientific Computing
Nicholas Galioto, Aerospace Engineering
Vishwas Goel, Materials Science and Engineering and Scientific Computing
Min-Chun Han, Civil and Environmental Engineering
Dalia Hassan, Chemistry and Scientific Computing
Alexander Hrabski, Naval Architecture and Marine Engineering and Scientific Computing
Javiera Jilberto Vallejos, Biomedical Engineering and Scientific Computing
Hyeon Joo, Learning Health Sciences and Scientific Computing
Timothy Jugovic, Chemistry and Scientific Computing
Ismael Mendoza, Physics and Scientific Computing
Aagnik (Nick) Pant, Applied Physics and Scientific Computing
Hardik Patil, Civil and Environmental Engineering
Amanda Wang, Materials Science and Engineering
Wenbo Wu, Biostatistics and Scientific Computing

Learn more about the fellows and the MICDE Fellowship program

Postdoc Position in Mechanical/Biomedical Engineering at the University of Michigan

By | News, SC2 jobs

Job Description:

Example of an image-based geometric model of a human aorta, discretized using an unstructured linear tetrahedral mesh.

Prof. Jesse Capecelatro in the Department of Mechanical Engineering and Prof. Alberto Figueroa of Biomedical Engineering are currently seeking a post-doctoral scholar for a one-year position.

The project aims at developing a numerical framework to simulate a large number of particles within the human body, from deformable red blood cells within arteries to better understand stroke, to rigid calcite particles in the ear canal responsible for vertigo. This will be performed within the CRIMSON (CardiovasculaR Integrated Modeling and SimulatiON) software environment, a Fortran-based finite element solver that simulates fluid flow in patient-specific geometries on unstructured grids.

Required Qualifications:

  • PhD in Engineering or a related field (e.g., Physics, Mathematics, or Computer Science)
  • Experience in Scientific Computing (proficiency in MPI and Fortran/C)
  • Interest in biological fluid dynamics and multiphase flow.

How to Apply:

If you are interested in this position, please email your curriculum vitae and at
least two references to Jesse Capecelatro (jcaps@umich.edu).

2021-2022 Catalyst Grant awardees continue to forge new fronts in computational science

By | Feature, News, Research

The Michigan Institute for Computational Discovery and Engineering (MICDE) announced the awardees of the 2021-2022 round of Catalyst Grants. Since 2017 MICDE Catalyst Grants program has funded a wide spectrum of cutting-edge research, this year focusing on AI for physically-based biomedicine, quantum computing, convergence of natural hazards with economic dislocation, and computational integration across scales and disciplines in biology. The five projects awarded in this round represent these new frontiers of computational research spearheaded by the Institute through its initiatives.

Prof. Shravan Veerapaneni (Mathematics) is working on advancing quantum algorithm research. His team will develop a Variational Quantum Monte Carlo algorithm that can potentially be applied to a wide range of linear algebraic tasks, like QR and Singular Value Decomposition (SVD). 

Profs. Salar Fattahi (Industrial and Operations Engineering) and Arvind Rao (Computational Medicine and Bioinformatics, Biostatistics) are revisiting existing theoretically powerful maximum-likelihood estimation mathematical methods to identify areas of weakness and strengthen them for use in biomedical, largely genomic, applications.

Profs. Gary Luker (Microbiology and Immunology), Nikola Banovic (Electrical Engineering and Computer Science), Xun Huan (Mechanical Engineering), Jennifer Linderman (Biomedical Engineering and Chemical Engineering), and Kathryn Luker (Radiology), will develop a physics/chemistry-aware inverse reinforcement learning (IRL) computational framework that will support the understanding single-cell and cooperative decision-making that drive tumor growth, metastasis, and recurrence.

Profs. Seth Guikema (Civil and Environmental Engineering and Industrial and Operations Engineering) and Jeremy Bricker (Civil and Environmental Engineering) will develop an integrated computational modeling approach to studying equity and resilience during natural hazard events, specifically estimating what essential services are the main constraints on individuals returning to a more normal life post-hazard, and assess inequities in resilience to coastal flooding events. 

Prof. Jesse Capecelatro (Mechanical Engineering and Aerospace Engineering) and Alberto Figueroa (Biomedical Engineering and Vascular Surgery), will develop a versatile, physics-driven, computationally efficient, and massively parallel numerical framework to simulate the interaction between fluids and biological particles in patient-specific vasculature geometries. This framework will enable next-generation computer-aided diagnostics.

“This year’s cohort of MICDE Catalyst Grants range from quantum computing for engineering science, AI for the physics of cancer, and computational advances in hazards engineering, through mathematical advances in data science, and bioengineering,” said MICDE Director Krishna Garikpati, a professor of mathematics and mechanical engineering. “These projects represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Learn more about MICDE’s Catalyst Grant program and funded projects here.

“This year’s cohort of MICDE Catalyst Grants … represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Krishna Garikipati
Director, MICDE

Prof. Monica Valluri Joins MICDE Leadership Team

By | News, Uncategorized
Portrait of Monica Valluri

Monica Valluri, Astronomy

This month, MICDE welcomed Monica Valluri, Research Professor of Astronomy, as an Associate Director. Prof. Valluri’s research is on the theoretical framework of Galactic Dynamics. Dr. Valluri uses galactic dynamics to interpret and model motions of stars observed with state-of-the-art telescopes using new and powerful numerical methods. Her work has led to important insights into how these dark components influence the structure and evolution of galaxies. In 2016, she won the University of Michigan Research Faculty Achievement Award for her or her outstanding research and teaching career in theoretical galaxy dynamics. In 2019, Prof. Valluri and a team of international collaborators were awarded an MICDE Catalyst Grant, “Determining the 3D Shape of Milky Way’s Dark Matter Halo” that has lead to several federally funded grants.

Annette Ostling - Portrait

Annette Ostling, Ecology and Evolutionary Biology

Prof. Valluri’s appointment succeeds former Associate Director Annette Ostling, Associate Professor of Ecology and Evolutionary Biology. We thank Prof. Ostling for her service to MICDE. Her contributions to the growth and resiliency of MICDE have been numerous over the last five years. 

Postdoc Position at Sandia National Labs on Computational Materials and Data Science

By | News, SC2 jobs

Sandia National Labs is hiring postdoctoral researchers to support work on computational materials and data science.


What Your Job Will Be Like:

We are seeking hardworking emerging scientists interested in working at the intersection of computational materials science, data science, and materials characterization. The candidate will be part of a team of scientists developing and deploying machine-learning-based and computational materials science capabilities to (i) detect and reveal mechanisms associated with physical and chemical processes occurring during manufacturing of nanostructured materials, (ii) prognose materials behavior, and (iii) adapt manufacturing of these materials. These capabilities that are developed are expected to become enduring toolsets for both in-house and visiting researchers at the Center for Integrated Nanotechnologies (CINT) and supporting various projects across the laboratories’ complex.

 

Required Qualifications:

  • Possess, or are nearing completion of, a PhD in Applied Mathematics, Computational Sciences, Materials Science & Engineering, Physics, or related field.
  • Demonstrated research success, written communication, and presentation skills as evidenced by a record of publication in peer-reviewed journals and external presentations at professional scientific conferences
  • Experience with advanced data analysis techniques and/or computational materials science tools such as molecular dynamics, phase field, etc.

Desired Qualifications:

  • Familiarity or direct experience with machine learning/artificial intelligence
  • Proven programming skills in Python, Matlab, Mathematica, C++, or similar
  • Experience with image analysis techniques
  • Experience in direct comparison of experimental and modeling data
  • Experience with mechanical, chemical, thermal and/or irradiation properties of materials
  • Interest in developing new analysis techniques
  • Interest in developing capabilities with commercialization potential
  • Interest in collaborative team-based research

About the Team:

The Nanostructure Physics department (01881) conducts research to establish the scientific principles that govern the design, performance, and integration of nanoscale materials into microscale and macroscale systems and devices. Department members perform discovery and use-inspired basic research in support of the DOE/SC Center for Integrated Nanotechnologies (CINT) and other Sandia programs. CINT is a DOE/Office of Science National User Facility operated jointly by Sandia and Los Alamos National Laboratories with facilities at both Laboratories.

Our team is committed to nurturing environment compatible with a broad group of people and perspectives in accordance with the changing makeup of the workforce. In support of this vision, our center actively recruits applicants from diverse groups of backgrounds and fosters an inclusive community.

Join us and work towards your goals while making a difference!

 

Position Information:

This postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia’s discretion up to five additional years. The PhD must have been conferred within five years prior to employment.

Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.

 

How to Apply:

1. Select the link to access our careers site.
2. Sign In to access your account or if you are not an existing user select the New User link to create one.
3. Review the job description and select the Apply button to begin your application.

 

About Sandia:

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:
• Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
• Extraordinary co-workers
• Some of the best tools, equipment, and research facilities in the world
• Career advancement and enrichment opportunities
• Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)
• Generous vacations, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://www.sandia.gov
*These benefits vary by job classification.

 

Security Clearance:

This position does not currently require a Department of Energy (DOE) security clearance.

Sandia will conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Furthermore, employees in New Mexico need to pass a U.S. Air Force background screen for access to Kirtland Air Force Base. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause access to be denied or terminated, resulting in the inability to perform the duties assigned and subsequent termination of employment.

If hired without a clearance and it subsequently becomes necessary to obtain and maintain one for the position, or you bid on positions that require a clearance, a pre-processing background review may be conducted prior to a required federal background investigation. Applicants for a DOE security clearance need to be U.S. citizens. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted.

Members of the workforce (MOWs) hired at Sandia who require uncleared access for greater than 179 days during their employment, are required to go through the Uncleared Personal Identity Verification (UPIV) process. Access includes physical and/or cyber (logical) access, as well as remote access to any NNSA information technology (IT) systems. UPIV requirements are not applicable to individuals who require a DOE personnel security clearance for the performance of their SNL employment or to foreign nationals. The UPIV process will include the completion of a USAccess Enrollment, SF-85 (Questionnaire for Non-Sensitive Positions) and OF-306 (Declaration of for Federal Employment). An unfavorable UPIV determination will result in immediate retrieval of the SNL issued badge, removal of cyber (logical) access and/or removal from SNL subcontract.  All MOWs may appeal the unfavorable UPIV determination to DOE/NNSA immediately. If the appeal is unsuccessful, the MOW may try to go through the UPIV process one year after the decision date.

 

EEO:

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.

Job Opening: Modeling, Simulation and Optimization of Energy Systems Research Engineer

By | Feature, SC2 jobs

The Bosch Research and Technology Center in Sunnyvale, CA seeks to hire an outstanding research engineer to develop novel hybrid multiscale, cross-domain modeling, simulation, and optimization tools for Bosch products. This engineer would join a team of PhDs with a variety of competences including high fidelity CFD-based multiphysics modeling, adjoint-based optimization, high performance computing and model-based state estimation and controls. The team focuses on design and optimization of residential and transportation technology targeting high energy efficiency and reduced emissions, including fuel cells, electric vehicle components, and heating systems.

Read more.

miRcore is looking for group leaders to guide high school students in performing computational biomedical research

By | Feature, SC2 jobs

miRcore, a 501(c)(3) non-profit org., is looking for group leaders to guide groups of high school students in performing computational biomedical research. Group leaders will be paired with a younger co-lead. During our remote camps, students will finish a research project within about one week, while learning and applying new tools every day. Group leaders will be expected to assist and support these students.

2017 miRcore GIDAS Biotechnology Summer Camp participants

Ideal group leaders are:
1) Graduate or college students (professionals with PhDs welcome)
2) Experienced (or interested) in computational biomedical research
3) Have a passion to inspire young minds
4) Able to commit to a specific camp

Camps run June-August, and leaders will be compensated with $450 per camp session.
Group leaders usually describe the experience as fun, meaningful, an opportunity to learn new research skills, and reigniting their passion for science.

Learn more and apply here: https://forms.gle/6oD9cDqULebxarCE7