The Michigan Institute for Computational Discovery & Engineering (MICDE) seeks proposals for 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.

Click here to read about the proposals that were funded during the first two rounds of awards.

Review Criteria

  1. Is the proposed work sufficiently novel, relative to the field, rather than an incremental extension of existing work?
  2. Have the ideas being proposed demonstrated a likelihood of success?
  3. Is there a plan for specific external funding agencies to be approached as an outcome of the proposed project, and a justification for why those agencies may find the work compelling?
  4. For full proposals: has a plan been formulated for how the infrastructure of Advanced Research Computing (ARC) at U-M will be leveraged in a proposal to external funding agencies?

Budget and Justification

A maximum of $100,000 may be requested. Graduate student/post-doc salaries, PI/Co-PI summer salaries, travel, and cost for accessing ARC computing resources are allowable expenses. At least 80% of the funds should support post-doctoral fellows and/or graduate students. Projects must include a detailed work plan for all involved personnel, including PIs/co-PIs. Indirect costs will not be levied on the funds and no cost sharing is required.

Who may apply

PIs/co-PIs should be tenure track or research track faculty at the University of Michigan, Ann Arbor Campus. Collaborative projects, especially those that show potential to seed center-level external proposals, are encouraged.

How to apply


To submit a proposal, please complete this google form. You must be logged in into your umich google account to open the form. Note that you cannot save the form without submitting it, so check that you have all the documents and information ready. These include:

  • Title and all investigators’ names and emails. You may include postdocs, and non U-M collaborators, but not graduate students.
  • Two pages including project description, plans for follow-up funding, personnel, and budget outline, in a pdf file. Please include the corresponding author’s last name in the file name.
  • References (no page limit). Please include the corresponding author’s last name in the file name.
  • 2-3 suggested expert reviewers from U-M (full name and umich email address).
  • Deadline to submit was March 18, 2019.

Full proposals:

  • By invitation only
  • Proposal content (a single PDF file):
    • 6 pages with project description, plans for follow up funding, leverage of ARC resources and personnel.
    • References (no page limit).
    • 2-page NSF or NIH-style CV for PI and co-PIs.
    • A detailed budget and budget justification.

Full proposals will be due at the end of April.

Q1: Should the proposal include mathematics, computer science, and/or cyberinfrastructure collaborators?

A1: Such collaborations are by no means required, but encouraged. Cyberinfrastructure collaborations could come by engaging closely with ARC-TS or CSCAR.

Q2: Must projects have new developments in computational methods, or are innovative applications of existing computational methods also acceptable?

A2: Innovative applications of existing computational methods are also sought.

Q3: Can a proposal have a single PI?

A3: Yes.

Q4: Should PIs/co-PIs be MICDE affiliated faculty?

A4: No, affiliation to MICDE is not required.

Q5: Will other research areas not listed be considered?

A5: Yes! Every research project with a relevant computational component will be considered.

Q6: If my project is chosen, is there a deadline for the start of the project?

A6: All projects should start by September 1, 2019 to give PIs a chance to recruit students/post-docs.

Q7: Does the $100,000 have to be used up in 1 year, or could it be extended?

A7: Extensions will be considered but PIs are strongly encouraged to have a plan to use all the money in 1 year.

Q8: Can any funds be used for lab analyses or equipment?

A8: Data-analyses are ok, but at this time in-house experiments or experimental equipment cannot be funded through this grant.

Q9: Does MICDE want software to be an output of the project?

A9: No, software is not expected as an output of the project, but it could be a component of the project.

Q10: What are the deliverables?

A10: We will require teams to meet at the end of the project to share results, and discuss what worked/didn’t work in the process.

Q11: How many pre-proposals will be invited to submit a full proposal?

A11: We expect 8-10 proposals.

Q12: Can there be external collaborators?

A12: Yes, but all the money should be spent at U-M.

Q13:  Should the project aim for one specific call for an external agency or foundation, or can it target several funding opportunities?

A13: It doesn’t have to aim for a specific call, the project can target more than one.

Q14: Can funding include PI salary (as required by, e.g., the Medical School)?

A14: PI salary can be included, but it should be accompanied by a justification of the precise role of the PI in carrying out the research, not only managing it.

Q15: Which tuition rate will be charged for students?

A15: Only candidates’ rate will be allowed.

Q16: What Flux rate will be charged for these projects?

A16: The negotiated rate for the PI/co-PI School or College.

Q17: Can a PI/co-PI participate in two pre-proposals?

A17: A researcher may be the Principal Investigator in only one proposal. There is no restriction for the number of proposals a researcher may participate as a co-PI.

 Informational Session

The informational session took place on February 7, 2019. A recording may be accessed here.  Applicants are encouraged to contact the following MICDE officials with questions:

Director – Krishna Garikipati (
Associate Directors – Annette Ostling (, Siqian Shen (, Karthik Duraisamy (
Assistant Director – Mariana Carrasco-Teja (

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.