MICDE funds wide-ranging computational discovery in galactic formation, drug discovery, bacterial biofilm colonies and turbulence simulations

By | News, Research

Since 2017 the Michigan Institute for Computational Discovery & Engineering (MICDE) Catalyst Grants program has funded a wide spectrum of cutting-edge research that combines science, engineering, mathematics and computer science. This year the program will fund four new projects that continue this tradition: Prof. Aaron Frank (Chemistry) and his group will spearhead efficient strategies to rapidly develop treatments for emerging diseases– a need made more compelling by the current COVID-19 Pandemic. Their approach combines generative artificial intelligence models and molecular docking to rapidly explore the space of chemical structures and generate target-specific virtual libraries for drug discovery. Prof. Marisa Eisenberg (Epidemiology, Mathematics, and Complex Systems) and Prof. Alexander Rickard’s (Epidemiology) groups will develop novel computational techniques to study biofilm architectures.  Biofilms are complex assemblages of microbial cells that form on almost any natural and man-made surface. They cause several debilitating diseases, and can even damage machinery and equipment, elevating the understanding of their behaviour to a critical need. Prof. Oleg Gnedin (Astronomy) will develop novel techniques to tailor the mathematical initial conditions from which to simulate chosen regions of the universe. The resulting insights will help uncover the origins of our own galaxy, the Milky Way. Finally, Prof. Aaron Towne (Mechanical Engineering) will advance the modeling of complex, turbulent flows and other large-scale systems in engineering science. His research will enable orders of magnitude of acceleration in the computation of extremely large scale flows in a number of engineering systems.

“These four projects have the potential to catalyze and  reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science”, said Krishna Garikipati, Professor of Mechanical Engineering and of Mathematics, and MICDE’s Director. “MICDE’s mission is to lead the advances in computational science research by bringing together interdisciplinary teams at U of M, and these projects embody that vision.” 

More about MICDE’s catalyst grant program and the projects can be found at micde.umich.edu/catalyst.

Fabricio Vasselai wins the Irving Louis Horowitz Award from the Horowitz Foundation for Social Policy

By | News

Fabricio Vasselai, a dual Ph. D. candidate in Political Science and Scientific Computing is a recipient of this year’s Horowitz Foundation awards from the Horowitz Foundation for Social Policy. His proposal titled “Elections in the AI era: using Machine Learning and Multi-Agent Systems to detect and study menaces to election integrity” won the Irving Louis Horowitz Award, given to the overall most outstanding project of the year, as well as the Joshua Feigenbaum Award as the most outstanding project on Arts, Popular Culture and Mass Communication.

The proposal develops Artificial Intelligence tools to detect and to study threats to election integrity. First, novel Multi-agent simulations of elections (MASE) are derived and implemented to be the data-generating process of synthetic data. Then these data is used to train Supervised Machine Learning (SML) to detect fraud in real election result counts. He uses such ability to create simulated training data to properly bootstrap the SML classifications, allowing for the novel estimation of uncertainty around election fraud detection. He also uses MASE to perform virtual experiments on the spread of fake news, showing that biased misinformation is critical for political polarization to flourish in majoritarian elections.

Fabricio Vasselai is an MICDE Fellow (awarded on 2018), and he is currently a Researcher at U-M’s Center for Political Studies and Center for Complex Systems.

Established in 1998, the Horowitz Foundation awards grants to scholars to assist them in completing their dissertations. It is highly competitive, with less than 3 percent of applicants receiving an award this year.

Microsoft AI for Health Program to support an AI-facilitated Optimization Framework for Improving COVID-19 Testing

By | News, Research

With the recent resurgence of COVID-19 infections, testing has become central to an integrated, global response to the pandemic. Accurate, effective, and efficient testing can lead to early detection and prompt an agile response by public health authorities. Strategic testing systems are critical for providing data that will inform disease prevention, preparation, and intervention. MICDE Associate Director and Associate Professor of Industrial and Operations Engineering and of Civil and Environmental Engineering, Siqian Shen, has recently published an article pin-pointing a number of pivotal operations research and industrial engineering tools that can be brought to  the fight against COVID-19. One of the key lessons from her research is the importance of expanding the availability of COVID-19 testing and making the resulting data transparent to the public as anonymized, summary statistics. This enables informed decision making by individuals, public health officials, and governments.  

Based on these high-impact findings, Professor Shen is striding ahead to design a comprehensive COVID-19 testing framework to efficiently serve the urgent needs of diverse population groups . A grant from Microsoft’s AI for Health program, part of the AI for Good initiative, will provide credits to use Microsoft’s Azure service.  With this cyber resource, Professor Shen and her team will integrate and coordinate decision-making models and data analytics tools that they have developed for testing on a Cloud-based platform. In addition, their AI framework is dynamic, and collects daily infection data to improve testing-related decisions. Such a platform could have significant impacts on three major problems that exist with current testing design strategies:

1) Where to locate testing facilities and how to allocate test kits and other resources.
2) How to effectively triage different population groups through effective appointment scheduling.
3) How to visualize real-time testing capacities to better inform the public and serve ad-hoc needs of patients. 

Prof. Shen’s research will integrate AI techniques with optimization to dynamically refine existing testing design methods for gathering and analyzing data from unexplored populations and regions around the globe. The development and refinement of these new models with the support of Microsoft Azure will create a transparent, data-informed testing system that will allow public health and government authorities to make agile, data-driven decisions to aid in the prevention, preparation, intervention, and management of COVID-19 and other outbreaks of infectious diseases.

Siqian Shen is a  Professor of Industrial and Operations Engineering, and of Civil and Environmental Engineering at the University of Michigan, an Associate Director of the Michigan Institute for Computational Discovery & Engineering, and an affiliated faculty member in the Michigan Institute for Data Science. Her research group works on both theoretical and applied aspects of problems by combining stochastic programming, integer programming, network optimization,  machine learning and statistics.

Seeking Student for Fall 2020 Research Project in GPU Programming for HPC Simulations of Quantum Systems!

By | News, SC2 jobs

Seeking Student for Fall 2020 Research Project Position with the U-M Computational Quantum Many-Body Physics Group

The U-M Computational Quantum Many-Body Physics Group, led by Professor Emanuel Gull,  is seeking a master’s student to contribute to its work on a research project in graphics processing unit (GPU) programming for high-performance computing (HPC) simulations of quantum systems. Qualified undergraduate students may also be considered for this position. Don’t miss out on this great opportunity!

Position Details: 

  • Knowledge of physics and quantum mechanics is not required for this position
  • The estimated workload for this position is 10-20 hours per week
  • Tentative start date: Fall 2020 term on the University of Michigan’s Ann Arbor campus

Required Qualifications:

  • Experience working with CUDA parallel computing platform and related techniques
  • Familiarity with HPC, scaling, and optimization strategies


  • Compensation range for this position is $20-$25, commensurate with experience and qualifications

Apply Today!

Please send a brief (no longer than 2-page) CV or resume to Professor Emanuel Gull at egull@umich.edu with subject, “Fall 2020 Project Research Assistant Position”.

U-M modeling epidemiologists helping navigate the COVID-19 pandemic

By | Feature, News, Research

[top] Screenshoot of the Michigan COVID-19 Modeling Dashboard (epimath.github.io/covid-19-modeling/); [bottom left] Marisa Eisenberg (Epidemiology, Complex Systems and Mathematics); [bottom right] Jonathan Zelner (Epidemiology).

The COVID-19 pandemic is producing massive amounts of information that more often than not lead to different interpretations. The accurate analysis of this daily input of data is crucial to predict possible outcomes and design solutions rapidly. These can only be achieved with expertise in modeling infectious diseases, and with the power of computational science theory and infrastructure. U-M’s Epidemiology Department, in the School of Public Health, has a very strong cohort of researchers who work on mathematically modeling the dynamics of infectious diseases, the analysis of these models, and large scale computer simulations — all to understand the spread and mitigation of pandemics. They are applying their long experience and expertise to the current COVID-19 outbreak, aiding the government make informed decisions, and helping media outlets produce accurate reports for the general public. Marisa Eisenberg, Associate Professor of Epidemiology, of Complex Systems, and of Mathematics, and her colleagues are using a differential equation transmission modeling approach to analyze scenarios and generate short-term forecasts for the COVID-19 epidemic in State of Michigan. They are communicating directly with the Michigan Department of Health and Human Services and providing critical tools, like the Michigan COVID-19 Modeling Dashboard, to inform decision-making. Prof. Eisenberg’s team is helping to forecast the numbers of laboratory-confirmed cases, fatalities, hospitalized patients, and hospital capacity issues (such as ICU beds needed), and examining how social distancing can impact the spread of the epidemic. Prof. Jonathan Zelner, whose research is focused on using spatial and social network analysis and dynamic modeling to prevent infectious diseases, is part of a group helping map the outbreak in Michigan. He also has provided valuable insights to journalists contributing to a better understanding of the situation, including what made New York City so vulnerable to the coronavirus (NYT), the role of wealth inequality during epidemics (CNBC) and what professions and communities are particularly vulnerable to infection (NG). 

Professors Eisenberg and Zelner are not alone in this fight. Many more researchers from U-M’s School of Public Health and throughout campus have risen to the challenges posed by this pandemic. 

Combat COVID-19 using newly available HPC resources: COVID-19 High Performance Computing Consortium

By | Feature, HPC, News, Research

COVID-19 High Performance Computing Consortium

On March 23, 3030 the White House announced the launch of a new partnership that aims to unleash U.S. supercomputing resources to fight COVID-19: the COVID-19 High Performance Computing Consortium. The goal of the Consortium is to bring together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. The access to these resources has the potential to significantly advance the pace of scientific discovery in the fight to stop the virus.

To request access to resources of the COVID-19 HPC Consortium, you must prepare a description, no longer than two pages, of your proposed work. To ensure your request is directed to the appropriate resource(s), your description should include the following sections. Do not include any proprietary information in proposals, since your request will be reviewed by staff from a number of consortium sites. It is expected that teams who receive Consortium access will publish their results in the open scientific literature.

Learn more at https://covid19-hpc.mybluemix.net .





Learn more about the COVID-19 outbreak through a panel of experts from the Society of Risk Analysis

By | Feature, Happenings, News

Seth Guikema, Professor of Industrial & Operations Engineering, MICDE affiliated faculty, and President of the Society of Risk Analysis moderated the webinar on Coronavirus: Risk Analysis Perspectives on COVID-19 Outbreak on Thursday, March 12, 2020. The webinar featured a panel of risk experts from the Society of Risk Analysis. If you missed the webinar yesterday you can still watch a recording of the panel discussion online.


MICDE Operations Plan During the COVID-19 Outbreak

By | News

MICDE operations plan during the COVID-19 outbreak

You are all aware of the cancellation of in-class instruction and the suspension of most symposia, seminars and other events, as a consequence of the COVID-19 outbreak in Michigan. MICDE’s leadership has arrived at the following plan for our activities in coordination with the University of Michigan Office of Research:

As with other aspects of the University’s operations, our goal, first and foremost, is to ensure the health and safety of our community. By suspending our operations, and proceeding only with those that can function entirely in remote mode, we aim to return to full strength at the earliest opportunity.


The MICDE Team

46 Peta-FLOPS computation of defects in solid crystals is a finalist in the highest prize for scientific computing

By | HPC, News, Research

From left: Sambit Das, Phani Motamarri and Vikram Gavini

A team led by Prof. Vikram Gavini (Professor of Mechanical Engineering and MICDE affiliate) and including Dr. Sambit Das (MICDE Fellow) and Dr. Phani Motamarri (Assistant Research Scientist and MICDE affiliate), is one of two finalists nominated for this year’s Gordon Bell Prize. The award, generally considered to be the highest honor of its kind, worldwide, recognizes outstanding achievement in high-performance computing. Gavini’s team has developed a methodology that combines advanced finite-element discretization methods for Density Functional Theory (DFT)1 with efficient computational methodologies and mixed precision strategies to achieve a 46 Peta-FLOPS2 sustained performance on 3,800 GPU nodes of the Summit supercomputer. Their work titled “Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation3 system” also involved Dr. Bruno Turcksin and Dr. Ying Wai Li from Oak Ridge National Laboratory, and Los Alamos National Laboratory, and Mr. Brent Leback from NVIDIA Corporation.

Electron density contour of pyramidal II screw dislocation system in Mg with 61,640 electrons (6,164 Mg atoms).

First principle calculation methods4 have been immensely successful in predicting a variety of material properties.  These calculations are prohibitively expensive as the computational complexity scales with the number of electrons in the system. Prof. Gavini’s research work is focussed on developing fast and accurate algorithms for Kohn-Sham5 density functional theory, a workhorse of first principle approaches that occupies a significant fraction of the world’s supercomputing resources. In the current work, Dr. Das, Dr. Motamarri and Prof. Gavini used recent developments in the computational framework for real-space DFT calculations using higher-order adaptive finite elements, and pioneered algorithmic advances in the solution of the governing equations, along with a clever parallel implementation that reduced the data access costs and communication bottlenecks. This resulted in fast, accurate and scalable large-scale DFT calculations that are an order of magnitude faster than existing widely used DFT codes. They demonstrated an unprecedented sustained performance of 46 Peta-FLOPS on a dislocation system containing ~100,000 electrons, which is the subject of the Gordon Bell nomination.

Past winners of the Gordon Bell Prize have typically been large teams working on grand challenge problems in astrophysics, climate science, natural hazard modeling, quantum physics, materials science and public health. The purpose of the award is to track the progress over time of parallel computing, with particular emphasis on rewarding innovation in applying high-performance computing to applications in science, engineering, and large-scale data analytics. If you are attending the SuperComputing’19 conference this year in Denver, you can learn more about Dr. Das, Dr. Motamarri and Dr. Gavini’s achievement at the Gordon Bell Prize finalists’ presentations on Wednesday, November 20, 2019, at 4:15 pm in rooms 205-207

Related Publication: S. Das, P. Motamarri, V. Gavini, B. Turcksin, Y. W. Li, and B. Leback. “Fast, Scalable and Accurate Finite-Element Based Ab initio Calculations Using Mixed Precision Computing: 46 PFLOPS Simulation of a Metallic Dislocation System.” To appear in SC’19 Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, Denver, CO, November 17–22, 2019.

[1] Density functional theory (DFT) is a computational quantum mechanical modeling method used in physics, chemistry and materials science to investigate the electronic structure (or nuclear structure) (principally the ground state) of many-body systems, in particular atoms, molecules, and the condensed phases. https://en.wikipedia.org/wiki/Density_functional_theory.
[2] A PETAFLOP is a unit of computing speed equal to one thousand million million (1015) floating-point operations per second.
[3] In materials science, dislocations are line defects that exist in crystalline solids.
[4] First principle calculation methods use the principle of quantum mechanics to compute properties directly from basic physical quantities such as, e.g., mass and charge.
[5] W. Kohn, L. J. Sham, Self-consistent equations including exchange and correlation effects, Phys. Rev. 140(4A) (1965) A1133.

Research Highlight: Improving aircraft aeropropulsive performance with multidisciplinary design optimization

By | News, Research

Anil Yildirim, Ph.D. Candidate, Aerospace Engineering

MICDE fellow Anil Yildirim, a Ph.D. candidate in the department of Aerospace Engineering, is working towards improving the overall efficiency of commercial tube-and-wing aircraft. The current commercial aircraft design with underwing engines have been the norm since the introduction of the Boeing 707 in the late 50’s [1]. With technological progress in composite materials and electric propulsion, as well as advancement of computational methods and computer power, researchers are developing more energy efficient systems to replace this legacy design. Working with the MDO Lab, lead by Prof. Joaquim R.R.A. Martins, and a team from NASA, Anil is studying the boundary layer ingestion (BLI) system on the STARC–ABL concept, introduced by NASA in 2016 [2] . BLI is an aeropropulsive concept, where a propulsion system is used to ingest the boundary layer generated by the aircraft. This increases propulsive efficiency and reduces the energy dissipated in the wake, effectively improving the overall aeropropulsive performance of the aircraft. Anil and his colleagues in the MDO Lab are using multidisciplinary analysis and optimization tools to study similar technologies, where design intuition is limited and interdisciplinary trades are important. Watch this video to learn more about his work (Authors: Anil Yildirim, Justin S. Gray, Charles A. Mader, Joaquim R. R. A. Martins, DOI: https://doi.org/10.2514/6.2019-3455)


[1] “707/720 Commercial Transport: Historical Snapshot,” 2015, http://www.boeing.com/history/
[2] https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160007674.pdf