Cargo Transport by Myosin Va and Kinesin-1 Molecular Motors: In Vitro Model Systems that Build Complexity in 3-Dimensions
Walcott, S., (co-PI), Warshaw, D. (Pi)
In Sam's own words: The Warshaw lab performs molecular-scale biophysical measurements, and my group develops multi-scale mathematical models. The overall objective of this project is to build a mechanistic understanding of how molecular motor-based intracellular transport is both achieved and regulated. My role is to build predictive mathematical models of the experimental system developed by the Warshaw lab, including myosin Va and kinesin molecular motors, actin and microtubule protein filaments, liposome cargos, adapter proteins and microtubule- and actin-binding proteins.
For more information, please see: https://reporter.nih.gov/project-details/10204620
Award Period: May 2021 - April 2022
Fully Latent Principal Stratification: A New Framework for Big, Complex Implementation Data from Education RCTs
Department of Education by the Institute of Education Sciences (IES)
Sales, A. (co-PI), Khang, H, (co-PI), Whittaker, T., (PI)
In Adam's own words: Randomized field trials of interventions in education, health, and other areas often gather complex, rich data on how the intervention is carried out. For instance, in interventions involving educational technology, researchers gather computer log data of students' actions within the program. There is broad agreement that implementation data is important, but little guidance on how best to use it to understand treatment effects. This project develops a framework for using modern measurement models to summarize complex implementation data, and then to estimate different average treatment effects for groups of subjects who implement (or would implement) the intervention in different ways.
For further information on this award, please visit https://ies.ed.gov/funding/grantsearch/details.asp?ID=4544
Award Period: 2021-2024 Award Amount: $891,895
Synoptic Engineering (Prime: DARPA)
In this project WPI is working with Synoptics Engineering (Prime: DARPA) on using machine learning to study electromagnetic scattering problems. The key idea is to train deep neural networks on far-field scattering patterns and use these deep neural networks to infer properties of the scattering medium.
Award Date: May 2021
CRII: AF: Optimization and sampling algorithms with provable generalization and runtime guarantees, with applications to deep learning
Division of Computing and Communication Foundations (CCF) at the National Science Foundation
In Professor Mangoubi's own words: "The aim of this project is to design novel optimization and sampling algorithms for training deep learning and other machine learning models, and to prove guarantees on the running time, generalization error and related robustness properties of these algorithms. Training algorithms with good generalization properties can lead to machine learning models which are more robust to changes in the dataset, allow for robust predictions, and help mitigate algorithmic bias when the training dataset may not be fully representative of the diversity of the population dataset. Guaranteeing a low generalization error is especially challenging in deep learning, since the number of trainable parameters is oftentimes much larger than the size of the dataset, and the loss function used to train the model is nonconvex. To prove stronger generalization and related robustness guarantees, we will use ideas from manifold learning and differential geometry to model the low-dimensional structure of datasets which arise in many machine learning applications."
For more information, please see:
Award Period: 2021-2023 Award Amount: $174,187
Bayesian Models for Cash Rents by Agricultural Practice
IPA Agreement with USDA
US Dept. of Agriculture
Award Period: 2020-2021 Award Amount: $149,574
Bayesian nonignorable nonresponse and selection models for small areas
Award Period: 2015-2021 Award Amount: $35,000
Data Sciences, AI and Machine Learning for Army Applications
Army Research Lab (ARL)
This project is focused on research in several disciplines from material science to data science and statistics to support the missions of the Aviation & Missile Center Technology Development Directorate. Our aim is to provide sound statistical and data science methodologies and data analysis for their research development and applications. The project is a two-year grant totaling $854,000 awarded to PI Prof. Rundensteiner, and Co-PIs Profs. Zou, Emdad, Zhang, and Kong.
Award Period: 2019-2021 Award Amount: $854,000
NRT-HDR: Data Driven Sustainable Engineering for a Circular Economy
National Science Foundation
This project is focused on training graduate students in disciplines from chemical science to data sciences to advance and support the future of circular economies. Our aim is to produce students versed in data-driven sustainable engineering that can have an impact on society. The project is a five-year traineeship grant totaling $2,999,289 awarded to PI Prof. Rundensteiner, and Co-Pis Profs. Paffenroth, Titova, Timko, and Deskins. For more information on this award, please visit https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021871
Award Period: 9/1/2020 - 8/31/2025 Award Amount: $2,999,289
Modeling the dynamics of spindle behavior in cells with supernumerary centrosomes
NIH (R01 GM140465-01)
In Professor Olson's own words: Mitosis is the process of cell division, involving an intricate balance of forces to ensure a successful result—two genetically identical daughter cells. In normal cells, the mitotic spindle contains two spindle poles (bipolar), each having microtubules nucleated from a centrosome. Cells in disease states may have extra centrosomes, leading to either formation of a multipolar spindle and multiple daughter cells with poor viability, or formation of a pseudo-bipolar spindle with daughter cells that are viable. A hallmark of cancer cells is the ability to successfully divide with extra centrosomes. Through a combination of live-cell imaging and model simulations, we will provide new fundamental knowledge and insight into how the normal mitotic machinery has been co-opted to allow for bipolar division in cells with extra centrosomes. The developed modeling frameworks for fluid-structure interactions will lead to new computational methods that will leverage high performance computing architectures to simulate centrosome movement and stochastic MT dynamics.
Award Period: 9/5/2020 - 6/30/2023 Award Amount: $916,956
Valid time-series analyses of satellite data to obtain statistical inference about spatiotemporal trends at global scales
NASA/University of Wisconsin-Madison
Start Date: 2/21/2020 (2 years). Amount: $174,762.00
As remote sensing has matured, there is a growing number of datasets that have both broad spatial extent and repeated observations over decades. These datasets provide unprecedented ability to detect broad-scale changes in the world through time and to forecast changes into the future. However, rigorously testing for patterns in these datasets, and confidently making forecasts, require a solid statistical foundation that is currently lacking. The challenge presented by remotely sensed data is the same as its remarkable value: remotely sensed datasets consist of potentially millions of time series that are non-randomly distributed in space. We propose to develop new statistical tools to analyze big, remotely sensed datasets that will add rigor to the conclusions about patterns of past changes and confidence to forecasts of future trends. Our focus is providing statistical tests for regional scale hypotheses using pixel-scale data, thereby harnessing the statistical power contained within all of the information in remotely sensed time series.
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.
DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.
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...