Faculty awarded funding

Tufts University’s Office of the Provost and the Office of the Vice Provost for Research (OVPR) recently announced award decisions for two inaugural seed funding programs:

• Tufts Springboard
• Data Intensive Studies Center (DISC) Seed Grant Program

These intramural grant programs will fund 31 proposals for a total distribution of around $650,000.

Recipients of the Tufts Springboard Award include:

Transforming Civic Meetings through Online Remote Participation

Team Lead: Fahad Dogar (School of Engineering)
Team Member: Peter Levine (Tisch College of Civic Life)

According to Assistant Professor Fahad Dogar, “we are looking at designing technologies that can better support "hybrid" meetings -- with a mix of in-person and remote participants -- with a focus on improving participation in civic meetings (e.g., local town hall meetings). The Springboard project will involve doing a need assessment survey, forming a multidisciplinary team…and submitting a proposal to the National Science Foundation Smart and Connected Communities Program (or another similar program).”

Demonstrating the Value of a Proposed Tufts-led Predictive Analytics and Comparative Effectiveness Research Network during COVID Epidemic

Team Lead: Michael Hughes (School of Engineering)
Team Members: David Kent (Tufts Medical Center) and Jessica Paulus (Tufts Medical Center)

According to Ann W. Lambertus and Peter Lambertus Assistant Professor Michael Hughes, the “goal is to establish a Tufts-led national research network ("PACER-Net") that would bring together collaborators across 3 other institutions - Mayo Clinic, University of Michigan and Duke University - to lead development and evaluation of new methods to predict individual treatment effects from readily-available personal health data. We want to answer the practicing doctor's key question: "will this treatment help this individual patient?" This Springboard award will directly fund efforts to demonstrate how working together as a network can help respond to COVID-19, by using multi-institutional knowledge to help answer "which hospitalized patients might need to be intubated?" based on available vitals and laboratory measurements from the electronic health record (EHR).”

Recipients of the DISC Seed Grant Program include:

Data Science Methods to Enable Label-free, Morphofunctional Imaging in Human Tissue

Team Lead: Irene Georgakoudi (School of Engineering)
Team Member: Liping Liu (School of Engineering)

“Machine learning methods can play a critical role in non-destructive and label-free microscopic imaging, which has the transformative potential of providing significant insights regarding key structural and functional features of cells. For example,” says Schwartz Family Assistant Professor in Computer Science Liping Lui, “we will apply image-denoising methods in order to significantly reduce the time of the imaging procedure but without sacrificing the image quality. I am excited to push the boundary of machine learning in real applications so as to reduce others' labor work.”

A Systems Biology Approach to Study Dysfunctional Neural Network Connectivity in Brain Disorders

Team Lead: Thomas Nieland (School of Engineering)
Team Member: Donna Slonim (School of Engineering)

Professor Donna Slonim reports that “in this project…we will look for molecular biomarkers for diagnosis, classification, and precision treatment of neurological disorders.   Using existing but unanalyzed expression data from cortical neurons exposed to pharmacological agents that modulate neurotransmitters relevant to psychiatric illnesses, we plan to construct gene regulatory networks of neural circuit defects that can be used as biomarkers.   Candidate causative genes will be identified through this network analysis, and we will then knock down or perturb these genes in differentiated neural cells to assess and validate their influence on network structure and function.  Our ultimate goal is to develop gene regulatory network models to improve personalized treatment options for patients with psychiatric and neurodegenerative disorders.”  

Using Agent-Based Models to Investigate Countermeasures for False Information Spread

Team Lead: Matthias Scheutz (School of Engineering)
Team Member: Jan P. de Ruiter (School of Arts and Sciences)

According to Professor and Bernard M. Gordon Faculty Fellow Matthias Scheutz, “We will develop an agent-based model to simulate a network of agents whose belief-driven behavior of taking precautions in the pandemic is mediated via a simple physical layer similar to susceptible-infected-removed models in epidemiology, a social network layer including friends and family, and a media layer broadcasting messages to portions of the population.  We will try to reproduce the spread of misinformation through conspiratorial messages (leading to careless behaviors) and investigate possible countermeasures."