Grants & Awards

This 3-year award from the Air Force Office of Scientific Research is titled "Reduced Order Model Algorithms for SAR Imaging in Multi-Scattering Dispersive Environment". Vladimir Druskin is the PI on this collaborative award with Mike Zaslavsky at Southern Methodist University (co-PI). In Vladimir's own words: The project scope includes the development and theoretical foundation of data-driven model order reduction imaging algorithms for the cases when conventional linearized approaches fail.

The award is titled REU Site: Research Experiences for Undergraduates in Industrial Mathematics and Statistics. Additional WPI Mathematical Sciences Faculty involved as Senior Personnel are Francesca Bernardi, Marcel Blais, Randy Paffenroth, Stephan Sturm, Burt Tilley, Sam Walcott, Fangfang Wang, and Frank Zou.

From the official abstract "The Worcester Polytechnic Institute (WPI) REU in Industrial Mathematics and Statistics provides a distinctive educational experience for students in the mathematical sciences by introducing them to the ways in which mathematicians and statisticians use math in the "real world". Students work in teams on research problems of industrial and mathematical significance that come directly from industry and that are of immediate interest to the companies involved in the program. Students work closely with company representatives to define the problem and to develop solutions. They work closely with faculty advisors to maintain a clear focus on the mathematics and statistics at the core of the project. This summer research provides challenges not faced in traditional undergraduate programs and helps to develop skills usually not addressed in academic programs. It also provides a glimpse of some of the many career possibilities that are open to students with a strong mathematical background."

For more information about this aware please visit https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244306&HistoricalAwards=false

Learn more about Professor Peiris and Professor Yakovlev, 

Award amount: $412,937

Award Dates: 5/2023 - 4/2026

Francesca Bernardi awarded new Simons Foundation Collaboration Grant for Mathematicians titled "Investigating diffusion and transport at the microscale." This 5-year grant is designed to enhance research through travel and inviting collaborators to WPI.

In Francesca's own words: "This funding will support my ongoing collaboration with the Harris Lab of PI Daniel M. Harris in the School of Engineering at Brown University. We developed a model based on the Fick-Jacobs equation suitable to describe diffusion in microchannels with dead-end pores of non-uniform cross-sections; experiments benchmarking this model are currently undergoing in my lab at WPI LEAP as part of an MQP project. Next, we are getting ready to consider the "reverse problem" to what we are currently working on, i.e. dislodging trapped particles from micropores, as well as diffusion in microchannels with wavy, absorbing, or superhydrophobic walls."

Sam Walcott received the award from the Integrative Research in Biology (IntBIO) Program within the Directorate for Biological Sciences. This 3-year NSF award is titled "IntBIO: Linking genome to phenome to understand function of an ancient muscle myosin". This award is joint with Nicolai Konow (UMass Lowell PI), Jeffrey R. Moore (UMass Lowell), and Matt Gage (UMass Lowell). Sam is the PI on the WPI portion of the award and this collaborative research builds on a previously awarded WPI and UMass Lowell seed grant for interdisciplinary research.

In Sam's own words: Many animals have unique muscle proteins that occur in their jaw muscles.  This muscle appears to be both strong and fast, violating the usual trade-off between muscle strength and speed where strong muscles are slow and fast muscles are weak.  Rodents provide a unique system to examine these muscles since, of the two common types of squirrels around here, gray squirrels have these muscle proteins while red squirrels do not.  In this project, my collaborators at UMass Lowell will collect data from red and gray squirrels at the scale of whole animals, isolated muscles, isolated muscle cells, and purified and engineered proteins.  I will develop and use mathematical models to tie the experimental results together.  In this way, we will determine how this muscle protein works and the trade-offs that have driven squirrel jaw muscle evolution.

For more information, please see this NSF page.

National Institutes of Health

Walcott, Sam

The NIH award is titled Collaborative Research: A Predictive Theory of Muscle Energy Consumption. This work will develop mathematical models to predict the amount of chemical energy that muscles need to contract, an advance that could lead to improved medical treatments and the creation of better prosthetic devices. This is in collaboration with Walter Herzog at the University of Calgary, Manoj Srinivasan at The Ohio State University, and Edward Debold at the University of Massachusetts, Amherst.

For more information, please see: https://www.wpi.edu/news/wpi-researcher-awarded-14-million-develop-model-predicts-energy-needed-muscle-movement

Award Amount: $1.4 Million

Professor Stephan Sturm received an award from SIAM to co-organize the Gene Golub SIAM Summer School (GS23) 2022. This award was a collaborative effort with F. Biagini (Munich), A. Capponi (Columbia), and S. Jaimungal (Toronto).

The summer school on "Financial Analytics: Networks, Learning, and High-Performance Computing" will be co-organized by Stephan Sturm, alongside other leaders in financial analytics. It will take place August 1-12 at the Gran Sasso Science Institute and allow advanced undergraduate and graduate students to learn about new and emerging areas while meeting faculty and students from around the world.

Professor Chris Larsen receives 3-year award from the National Science Foundation's Applied Mathematics Program. The project is titled Variational Fracture with Loads.

In Professor Chris Larsen's own words: Until recently, variational models for static and quasi-static fracture have been limited to Dirichlet boundary conditions, since there do not exist solutions to the seemingly most natural formulation that includes Neumann boundary conditions, i.e., boundary loads. The aim of the project is to improve on a recently introduced static formulation for variational fracture with boundary loads which can have solutions, and to extend this static model to the quasi-static case.

For more information, please see the NSF Award Info.

This 3-year award is titled Optimal Contracts and Optimal Stopping. Gu  Wang is the PI on this award from the Applied Mathematics Program at the National Science Foundation. 

In Gu's own words: This project analyzes how contractual compensations are optimally designed and terminated in a principal-agent relationship, and the potential moral hazard due to the information asymmetry between the two parties. The goal is to develop new tools and strengthen existing ones in the optimal contract theory to incorporate optimal stopping, jump diffusions and agents who control the diffusion coefficient of the state variable. The expected results help us understand the agents' behavior under different contracts and have concrete applications in practice, for example, the risk exposure of portfolios chosen by asset managers for their investors, and the withdrawal behavior of insurance policy holders, which help regulate and design financial products, and improve welfare distribution among market participants.

You can read more about this new award at the NSF Award Info page. 

National Science Foundation

Wu, Zheyang

This project is motivated by integrative analysis of large-scale genomic data, where an important question is how to effectively combine statistical significances, or p-values, from heterogeneous data sources. Despite recent advances in theoretical and applied studies, statistical and computational challenges remain in addressing critical data features, such as complex correlations, discreteness of data, and availability of prior knowledge that could have been utilized to boost signal detection. This project will develop novel statistical methods to address the challenges and increase the statistical power for detecting valid signals. The research will facilitate innovations in statistical theory and methodology as well as in broad applications. The research activities will leverage project-oriented education, promote multi-disciplinary interactions, and benefit STEM education for the next generation of engineers and scientists, especially members of minorities underrepresented in the statistics field.

For more information, please see: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113570&HistoricalAwards=false

Award Period: August 2021 - July 2024 Award Amount: $200,000

Assistant Professor Francesca Bernardi received a new award from the National Science Foundation's Division of Chemical, Bioengineering, Environmental, and Transport Systems Program.

The 3-year NSF award is titled "Collaborative Research: Prechlorination, aging, and backwashing effects on spatiotemporal ultrafiltration fouling: Optimizing productivity by combining experiments and theory". This award is joint with Nicholas Cogan (Florida State University) and Shankar Chellam (Texas A&M). Francesca is the PI on the WPI portion of the award.

In Francesca's own words: This project aims at improving hollow-fiber ultrafiltration processes for wastewater reclamation. Numerous municipal facilities in the United States (and worldwide) utilize this mechanism to purify water for potable reuse. Typically, as wastewater flows through these hollow-fiber membranes, impurities are captured and occlude the pores in a process called "fouling;" this reduces the filtration efficiency and water production. Our project takes a synergistic approach by combining experimental investigations (at Texas A&M) with modeling and optimization efforts (at WPI and at FSU) to build theoretical/computational models of these membrane operations, which includes the spatiotemporal dependence of fouling and the effects of fiber geometry and packing density.

For more information, please see this NSF page.

National Science Foundation

William Martin, professor of mathematical sciences, has received a three-year, $150,000 grant from the National Science Foundation for a research project titled, “Association Schemes and Configurations in Real and Complex Space.”

The project is aimed at taking on questions that have stumped the best mathematical minds in the world for decades. Martin is looking to advance mathematicians’ understanding of association schemes, which are finite combinatorial structures that can be viewed algebraically, geometrically, or as highly symmetric networks.

Finding new association schemes, or even better understanding them, could enable researchers to use one quantum computer to simulate another. They also could be used to discover new error-correcting codes for digital communications, new spherical designs for estimating solutions to calculus problems in high-dimensional space, or new secure scrambling components for symmetric key encryption.

Martin is working on underlying algebraic theorems that will lead to a stronger mathematical theory, though he said he is focused on making advances that other researchers can then use in their own work, to solve problems in various applied areas.

At least one PhD student and multiple MQP teams will be working on the project.

Further information on this award can be found at https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808376&HistoricalAwards=false

Total Award Period: 2018 - 2024

National Science Foundation

Druskin, V.,

The 3-year NSF award is titled “Collaborative Research: Multiscale Simulations and Imaging of Viscoelastic Media in Reduced Order Model Framework” and is joint with Elena Cherkaev (University of Utah) and Murthy Guddati (NCSU).  Vladimir is the PI on the WPI part of the award ($159,552).  In Vladimir's own words:  The foundation of this project is the Stieltjes-Krein (elastic) network realization of data-driven reduced order models that we previously developed for imaging in oil exploration and defense applications.  We plan to extend this approach to non-Stieltjes viscoelastic networks and apply it to non-destructive testing and medical imaging.

For more information, please see:

https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110773&HistoricalAwards=false

Award Period: August 2021 - July 2024 Award Amount: $159,552

Direct adjustment in combination with robust or nonlinear regression: software and methods for RDDs, RCTs and matched observational studies

Department of Education/Institute of Education Sciences (IES)

Sales, A., (Co-PI, WPI PI), Hansen, B (PI) (University of Michigan), Bowers, J., (Co-PI), Errickson, J., (Co-PI)

Adam is a co-PI (WPI PI) with a team of researchers (Profs. Hansen, Bowers, and Errickson) on this grant funded through the Department of Education by the Institute of Education Sciences (IES). In Adam's own words: This grant is to develop a suite of methods and software for estimating treatment effects in a wide range of randomized experimental designs and observational studies common in education research. The tools we develop will allow researchers to separate covariate models--models of outcomes and treatment assignment, conditional on covariates--from effect estimation, while ensuring that standard errors are valid.  For more information, please see:

https://ies.ed.gov/funding/grantsearch/details.asp?ID=4541

Award Period: March 2021 - February 2024 Award Amount: $785,482

Improving the Power of Education Experiments with Auxiliary Data

Department of Education/Institute of Education Sciences (IES)

Sales, A., (Co-PI), Gagnon-Bartsch, J. (University of Michigan), Heffernan, N. (WPI).

Professor Sales is a co-PI with a team of researchers (Prof. Gagnon-Bartsch at University of Michigan and Prof. Neil Heffernan at WPI). This is a three-year grant funded through the Department of Education by the Institute of Education Sciences (IES). In Adam's own words: Data for analyzing randomized experiments are often drawn from larger databases, such as state longitudinal data systems for educational experiments or log data for A/B tests run on technology platforms. This project investigates ways to incorporate covariate and outcome data from outside the randomized experiment to improve the power and precision of experimental causal estimates, without sacrificing the advantages of experimentation. For more information, please see: 

https://ies.ed.gov/funding/grantsearch/details.asp?ID=4542

Award Period: March 2021 - February 2024 Award Amount: $576,429

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

Fata Morgano

Synoptic Engineering (Prime: DARPA)

Paffenroth, R.,

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

Mangoubi, O.

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:
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104528

Award Period: 2021-2023 Award Amount: $174,187

NRT-HDR: Data Driven Sustainable Engineering for a Circular Economy

National Science Foundation

Rundensteiner, E. (PI), Paffenroth, R. (co-PI), Deskins, A. (co-PI), Timko, M.(co-PI), Titoval, L.(co-PI)

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

Portable multiplexed chemical agent sensor for detection in obscurant-heavy environments

DTRA and CCDC-SC

Paffenroth, R

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.