Every year, The Michigan Institute for Computational Discovery & Engineering (MICDE) Catalyst Grants fund innovative research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure. Topics of interest include, but are not limited to:

  • Computational science approaches, algorithms, frameworks, etc.
  • Emerging paradigms in computing (exascale computing, quantum computing, FPGA computing, etc.)
  • Applications in emerging areas (neuroscience, ecology, evolutionary biology, human-made complex systems, mobility etc.)
  • Extensions of traditional computational sciences to complex decision making (reinforcement learning, transfer learning, neuromorphic computing, etc.)
  • Artificial Intelligence informing and informed by science

Generic big data problems that do not fundamentally advance computational science algorithms are not suitable for MICDE Catalyst Grants. Priority will be given to high-impact projects with potential to eventually attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100,000 each.

Funded projects are listed below. Calls for proposals will be listed on this page when they are opened.

2019 Funded Projects

Determining the 3D shape of Milky Way’s Dark Matter Halo

Researchers: Monica Valluri (Astronomy), August Evrard (Physics), Kohei Hattori (Astronomy), Eugene Vasiliev (University of Cambridge), Pablo Fernández de Salas (University of Stockholm), and Katherine Freese (UT Texas and University of Stockholm)
Description: The objectives of this project are to use data from the European Space Agency’s Gaia satellite to determine the shape of the Milky Way’s dark matter halo and how it this shape changes with distance from the center. The background image shows Gaia’s multi-color map of the Milky Way and two nearby satellite galaxies, the Large and Small Magellanic Clouds (bottom right).  This image is not a photograph but a map comprised of billions of individual stars in the Milky Way and its neighborhood. For this project the team will use data obtained by Gaia that gives positions and velocities for stars in the Milky Way’s halo along with the new computational modeling tools they are developing. They will use the fact that most halo stars travel through the halo on regular trajectories (like the one pictured by the blue curve) to compute conserved quantities (“actions”) for each of hundreds of thousands of stars. Modeling the motions of halo stars will allow the team to set constrains on the density profile and the shape of the Milky Way’s dark matter halo. By comparing their derived dark matter distribution with results from simulations they hope to shed light on the nature of the elusive dark matter particle.

The background image is a multi-color image of the Milky Way disk, its halo and nearby satellite galaxies obtained with the European Space Agency’s Gaia Satellite (http://sci.esa.int/gaia/) . The blue curve shows an example of (half) of a regular trajectory that a star in the halo of the Milky Way might follow

Enabling Tractable Uncertainty Quantification for  High-Dimensional Predictive AI Systems in Computational Medicine

Researchers: Xun Huan (Mechanical Engineering), and Arvind Rao (Computational Medicine and Bioinformatics and Radiation Oncology )
Description:  Artificial intelligence (AI) systems are powerful tools in healthcare and medicine. However, it is crucial to understand how much one can trust the AI analyses and predictions, especially when adopting them for decision-making where inappropriate choices may result in dire consequences.  In this project, we start by developing the computational and algorithmic foundations for performing uncertainty quantification (UQ) in machine learning (ML) models. We tackle this by creating new computational methods and leveraging high-performance computing, to capture and construct uncertainty distributions for high dimensional deep neural networks (of tens of millions of weight parameters). We focus on medical AI models used for detecting IDH (isocitrate dehydrogenase) gene mutation from MRI (magnetic resonance imagine) brain tumor images. The resulting product will be ML models that produce not only a single output, but a spread of predictions which also reflects its predictive quality and uncertainty.

The DNN model takes in MRI image and predicts the probability of IDH gene mutation. e.g. while predicted probability is 80%, the uncertainty surrounding this estimate is typically never reported.

Real-Time Phase-Resolved Ocean Wave Forecast with Data Assimilation Enabled by GPU-accelerated Computation

Researcher: Yulin Pan (Naval Architecture and Marine Engineering)
Description: The purpose of this project is to develop a new computational framework for the onboard real-time forecast of phase-resolved ocean wave field with data assimilation capability. The prediction and assimilation algorithms will be accelerated on a CPU-GPU hardware architecture for real-time applications. Integrated with the remote wave sensing technology, this work will lead to enhanced safety, efficiency and autonomy in marine operations.

Envisaged application in autonomous path planning.

Long time-scale simulations using exponential time-propagators 

Researchers: Vikram Gavini (Mechanical Engineering)
Description:  This effort is aimed at developing scalable and efficient algorithms for long-time scale simulations of dynamical phenomena in materials. The approaches to be developed are expected to enhance our ability to study a wide range of time-dependent phenomena from electron dynamics to elastic response of materials.

Algorithmic solutions to manage power consumption on exascale systems

Researchers: Eric Johnsen (Mechanical Engineering), Henry Hoffman (University of Chicago), and Jeffrey Hittinger (Lawrence Livermore National Lab)
Description: Our objective is to develop a quantitative strategy for power management at the exascale, given a desired solution accuracy. For this purpose, our approach integrates high-order methods development with mixed-precision computing, lossy data compression, and applications monitoring power consumption.

Convergence rate of Recovery DG vs. BR2 (the current gold standard) for linear diffusion on a Cartesian mesh. For the same p=2, Recovery achieves 8th order for Recovery, while BR2 is 3rd order.

Simulation-based discovery of robust algorithms for targeting of infectious disease screening and intervention

Researcher: Jon Zelner (Epidemiology), and Seth Guikema (Industrial and Operations Engineering and Civil and Environmental Engineering)
Description: In settings with a high burden of infectious diseases, such as Tuberculosis (TB), there is a growing need for tools that can help public health professionals find and treat cases more quickly and effectively. In this project, we will be continuing development of a spatiotemporal simulation model of coupled household and community TB in a high-incidence setting. In our initial work on this project, we have used this model to compare different intervention scenarios that take advantage of information on the spatial location and contact networks of TB cases to target interventions. In the next phase of this project, we plan to use this simulation platform as a tool for developing adaptive interventions that can respond to changing epidemiological conditions, i.e. a sharp rise in incidence indicative of an outbreak, and modify screening and intervention strategies to deal with the types of heterogeneity that makes tackling real-world infectious disease problems highly challenging.

Spatial hotspot of strongly elevated multi-drug resistant (MDR-TB) incidence in Lima, Peru illustrated in red. (Figure from Zelner et. al., JID 2016 [2])

Hierarchical computing for dynamic evolutionary inference of complexity

Researchers: Stephen Smith (Ecology and Evolutionary Biology)
Description:  We propose to develop new tools grounded in emerging techniques for accommodating heterogeneity in genomic and trait datasets to enable statistical comparison of the formation and evolution of modules across many taxa that efficiently handle multivariate datasets consisting of multiple sources including morphology, genomes, biochemical data, and gene expression. These new methods will identify common patterns of evolutionary rate and mode across multiple genes and traits, allow for lineage specific heterogeneity, scale to the large dimensions common in modern datasets, and break the false dichotomy of genes vs traits. Given the size of the datasets, and the necessity to explore model complexity, these methods will require significant computation and a hierarchical computational approach: distributed computing for independent analyses (e.g., individual genes), multi-core parallel computing of individual analyses where likelihoods are calculated in parallel on shared memory resources, and GPU computing for more extensive model explorations that require extensive matrix calculations. The methodological developments will be implemented in, gophy, a package developed by the participants of the proposal.

2018 Funded Projects

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.

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.

Caption: Chemical waves and elastic deformations in thin gel sheets.
(a & b) Target and spiral waves in the BZ (Belousov-Zhabotinsky) chemical reaction.
(c) Numerical simulations of the BZ reaction. (d) Resulting reference metrics and shapes for the gel sheet.(e) Experimental images of oscillating gels.

Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification

Researchers:  Valeriy Ivanov, Civil and Environmental Engineering; Nick Katopodes, Civil and Environmental Engineering; Khachik Sargsyan, Sandia National Labs
Description:  The research team will enhance urban flood monitoring and prediction using NASA Cyclone Global Navigation Satellite System (CYGNSS) data, taking advantage of state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

The problem of flood prediction incurs multiple uncertainties (a) and is of high computational complexity (b). New remote sensing data from the CYGNSS mission (c) can inform complex physically-based simulations (d) but in order to achieve feasibility of real-time solutions and uncertainty quantification, novel approaches are required. This project will develop reduced-order modeling tools (e) as innovative, parsimonious representation of rigorous hydrologic and hydrodynamic model formulations to efficiently obtain probability density distributions of one or many quantities of interest (f).

Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets

Researcher: 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.

Polar plots showing the rhythmic phases of spikes fired by human neurons, revealing systematic variations across space and time.

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.

Structure of a block-structured adaptive grid that overlays a so-called ‘cubed-sphere’ base grid. The atmospheric vortices (in color) resemble idealized tropical cyclones that are captured by the refined grid patches. The adaptations are guided by the location and strength of the rotational motion.

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.

Embedded sensing and machine learning to distinguish pollinators (Bombus impatiens pictured) from other sound sources in natural environments.

Deep Learning for Phylogenetic Inference

Researcher: 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.

Molecular sequence data are input into layers of neural network structures to produce a score for each possible tree topology describing how well the tree fits the data.

2017 Funded Projects

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.

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.

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.

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.