Sarah Olson wins 2019 Mathematical Medicine & Biology Best Paper Prize
The winning paper was co-authored with Lucia Carichino (former WPI postdoctoral Scholar, now tenure track at RIT) and is titled "Emergent three-dimensional sperm motility: coupling calcium dynamics and preferred curvature in a Kirchoff rod model".
The journal Mathematical Medicine and Biology is managed by the Institute of Mathematics and its Applications (IMA) https://ima.org.uk, which is UK's chartered professional body for mathematicians and one of the UK's learned societies for mathematics. This journal publishes original articles with a significant mathematical content addressing topics in medicine and biology. It gives particular emphasis to papers exploiting modern developments in applied mathematics.
This best paper prize recognizes the most outstanding research published in the journal over a two year period, judged by a committee drawn from the Editorial Board.
In Sarah's own words: This paper extended previous modeling techniques to examine fully three-dimensional sperm motility where internal force generation is coupled to the relevant biochemistry. We focused on understanding the role of calcium coupling on swimming patterns; emergent trajectories that can be characterized as a hypotrochoid are observed for quasi-planar beatforms, similar to experiments
Portable multiplexed chemical agent sensor for detection in obscurant-heavy environments
DTRA and CCDC-SC
Start Date: 2020 (3 years).
This project is focused on combining machine learning with chemical sensor arrays to reduce false alarm rates in challenging environments. The project leverages our groups recent work in applying machine learning techniques to problems from the physical sciences. The project is a multi-party effort between WPI, CCDC-SC, Seiksui Chemical Co., and UMass Amherst. The project is an up to $1.8 million award to the team, and up to $249,000 of that amount is expected to support our group’s work on the project over the next three years.
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DeepM&Mnet: A General Framework for Building Multiphysics & Multiscale Models using Neural Network Approximation of Functions, Functionals, and Nonlinear Operators
Brown University (Prime: Defense Advanced Research Projects Agency)
Start Date: June 15, 2020 (3 years). Amount: $149,854
The proposal focuses on mathematical analysis for the neural network approximation (NNA) as well as the design of efficient deep learning algorithms for multiphysics and multiscale systems. The key idea is to train NNA for separated systems offline and then combine all the trained networks to solve multiphysics systems online. The research will result in fast and reliable solvers for multiphysics and multiscale systems. The main goal of the WPI investigator is to analyze and to improve the performance of the NNA as universal approximations in learning functions, functionals, and nonlinear operators and fusion of all pre-trained networks to solve multiphysics systems.The project is collaborative with Brown University and John Hopkins University
Simulating Large-Scale Morphogenesis in Planar Tissues
National Science Foundation, DMS 2012330
Start Date: 6/15/2020 (3 years). Amount: $200,000
Cutting-edge developments in biotechnology and medicine involve reconstructing large-scale tissues and organs. This work can be limited by lack of knowledge in tissue morphogenesis, the process by which living tissues develop their size-and-shape characteristics. Though live-imaging techniques have enabled the observation of morphogenetic processes, progress in fundamental understanding has been slow. This project aims to improve tools for modeling a wide range of living tissues that are relatively planar and have been extensively studied experimentally. The project will develop methods for numerical simulation of morphogenesis processes and attempt to reproduce the observed large-scale morphogenesis structures in planar tissues. The project provides graduate student training through involvement in the research.
This project concerns numerical simulation of large-scale continuum models for tissue morphogenesis that involve free boundaries, bulk-interface coupling, and highly nonlinear interactions. The work centers on a new mathematical model in which the field variables are nonlinearly coupled via reaction-convection equations and non-standard spatial partial differential equations. The project will develop semi-implicit and fully implicit time-stepping methods to avoid a potential time-step restriction for explicit time-stepping methods. Due to the high nonlinearity of the system, the boundary configuration must be updated together with the velocity field as well as other field variables. For this purpose, a novel interface-tracking method based on reference-map techniques will be investigated. Linear analysis close to trivial solutions will be conducted to assist the design of fast-converging iterative methods for solving the nonlinear system derived from the implicit time-stepping discretization of the original model. Simulations to understand in vitro micro-tissue and in vivo epithelial-tissue morphogenesis from live-imaging data will be carried out.
For more information on this grant, please visit https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012330&HistoricalAward...