Catalyst Grants | Michigan Institute for Computational Discovery and Engineering

Research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure. MICDE Catalyst Grants are awarded to tenure track or research track faculty at the University of Michigan for innovative research projects in computational science.

2023 Grants

2022 Grants

2021 Grants

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 cyber infrastructure.

Catalyst Grant 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 usually funds 3-4 one-year projects per year at up to $100,000 each.

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

2023 Catalyst Grants

An AI Coachbot for Strengthening Self-Regulated Learning of Computational ML and AI

Von Mises effective stress field distribution of the offshore wind turbine foundation for the “without cone” case subject to ice loading at different time instants

Computational Modeling of a Coupled Aero-Hydro-Structural-Mooring Integrated Dynamic System with Deep Learning for Floating Offshore Wind Turbine Design

Overview of multi-stage diffusion model strategy for improving training and sampling efficiency

Efficient Diffusion Models for Scientific Machine Learning

Foundation Models at Quantum Mechanical Accuracy For Battery Electrolyte Design using Wafer-Scale Computing

Program Synthesis Tools for Scientific Computing

Oobleck provably guarantees fault tolerance for up to f failures without sacrificing training throughput by creating pipeline templates that can use all available training resources all the time.

Resilient Distributed Training of Large Models

Simulating the Impact of Early Outbreak Uncertainty on Pandemic Response Intervention Policies

2022 Catalyst Grants

MICDE CATALYST GRANTS ENABLES JUNIOR FACULTY INNOVATIONS IN COMPUTATIONAL SCIENCE

Four projects lead by outstanding junior faculty have been awarded the 2022 MICDE Catalyst Grant funding. “Ranging from computer vision-enabled insights to animal behavior, through fundamental mathematical frameworks to advance quantum physics and materials discovery, to probability-based computational modeling of material behavior, the 2022-23 cohort of catalyst grant awardees will continue MICDE’s tradition of uncovering new frontiers of computational science”, said MICDE Director Krishna Garikipati, Professor of Mechanical Engineering, College of Engineering, and Professor of Mathematics, LSA.