Research teams at the University of Michigan will share more than $370,000 in awards to explore high-impact projects with novel advancement in computational science across a breadth of research areas.

The Michigan Institute for Computational Discovery and Engineering, a unit within the Office of the Vice President for Research, recently awarded funding to five research teams as part of its Catalyst Grants program. The program, launched in 2017, has funded a wide spectrum of cutting-edge research that combines science, engineering, mathematics and computer science.

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

MICDE awarded grants for the following projects:

Scalable Inference of Spatially-varying Graphical Models with Applications in Genomics

Salar Fattahi, assistant professor of industrial and operations engineering; Arvind Rao, associate professor of computational medicine and bioinformatics, radiation oncology and biostatistics, and their groups will revisit the standard maximum-likelihood estimation as the “Holy Grail” of the inference methods for graphical models, and precisely pinpoint and remedy the scenarios where it breaks down. This project aims to be the first systematic inference framework that can achieve the best of both worlds — computational efficiency and favorable statistical performance — in a unified fashion.

Fast Linear Algebra in the Noisy Intermediate-scale Quantum Era

Shravan Veerapaneni, associate professor of mathematics, and his group aim to develop a probabilistic computing paradigm that can potentially be applied to a wide range of linear algebraic tasks. In addition to providing a toolkit for performing high-dimensional linear algebra, which is of intrinsic interest, the proposed solver provides a quantum-inspired classical benchmark for assessing the quantum computational advantage of the recently developed variational quantum linear solver.

Cancer Cells: Greedy Individuals or Team Players?

Gary Luker, professor of microbiology and immunology, and of biomedical engineering; Nikola Banovic, assistant professor of electrical engineering and computer science; Jennifer Linderman, professor of biomedical engineering and chemical engineering; Xun Huan, assistant professor of mechanical engineering; and Kathryn Luker associate research scientist in radiology, will develop a physics/chemistry-aware inverse reinforcement learning computational framework to discover how heterogeneous cancer cells function singly or collectively to drive cancer progression. The long-term goal of this research centers on understanding single-cell and cooperative decision-making that drive tumor growth, metastasis, and recurrence.

Computational Modeling of Household Level Damage and Resilience Based on Location Data

Seth Guikema professor of industrial and operations engineering, and civil and environmental engineering; Jeremy Bricker, associate professor of civil and environmental engineering, and their groups will develop an integrated approach for assessing household-level resilience and inequities in resilience during coastal flooding events. Specifically, they will study improving building-level flood and fragility estimates for coastal flooding events. They also plan to develop a new approach for 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.

Next Generation Computational Tools for Particle-laden Biological Flows in Subject-specific Geometries

Jesse Capecelatro, assistant professor of mechanical engineering and aerospace engineering; Alberto Figueroa, professor of biomedical engineering and vascular surgery, and their groups will develop a physics-driven and computationally efficient framework to study human diseases related to blood flow in the circulatory system. Fluid mechanics plays a crucial role in many physiological processes on health and disease. Given recent advances in medical imaging, computational power, and mathematical algorithms, real-time patient-specific computational fluid dynamics is now becoming possible. Yet, many problems involve complex interactions between fluid and biological particles in which existing models are either too expensive to simulate at full scale or unable to properly capture important hydrodynamics taking place at the smallest scales. This project will develop a versatile and massively parallel framework to bridge this gap. The numerical framework will be designed 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.

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