PODS Grants Awards

Drug combinations are increasingly used to combat drug resistance in cancer and infections, but the current trial-and-error approach to choosing them often leads to ineffective or unsafe combinations that can worsen clinical outcomes. While several computational models have been developed to predict optimal drug combinations, they often assume homophily (i.e., similarity in drug-target and drug-drug ...

This project develops new artificial intelligence tools to better understand how brain development relates to learning, sleep, and life experiences in children and adolescents. Using data from the nationwide ABCD Study, we will create open-source software to predict developmental outcomes from brain scans while ensuring results are accurate, interpretable, and reproducible. The tools we build ...

This project will develop an AI powered Python toolkit that will revolutionize the analysis of histology samples, by introducing quantitative, physics-informed metrics developed in the domain of materials science to histological analysis. Our toolkit boosts diagnostic accuracy by integrating spatially-resolved, single cell information into novel, sequence-aware machine learning models, circumventing the ubiquitous dearth of data ...

Numerous AI tools have been developed, but few are currently used in routine clinical practice. We will compare the ability of machine learning and large language models to identify which patients with potentially inappropriate medication prescriptions are most likely to be recommended for a prescription change by clinical pharmacists. The findings from this project will ...

Hospital readmission rates are a publicly reported hospital quality measure; hospitals use transitional care interventions, like follow-up phone calls, to lower readmission rates. However, these interventions are often resource-intensive, and current methods for deciding who should receive them may not be effective. This study aims to analyze which patients actually benefit from these interventions using ...

This project will test and try to understand the quality and value of artificial intelligence for analyzing text-based data and for examining patterns, such as how processes differ between higher vs. lower performing healthcare systems. Traditionally, this analysis is computer assisted, but AI has the potential to save time and resources while maintaining quality.

This project will empirically develop and validate: 1) a framework to reason about the context-specific norms around appropriate reliance on GenAI tools to support defining and measuring appropriate reliance; 2) a framework outlining factors and their contributions to users’ reliance decisions to inform approaches that identify inappropriate reliance to mitigate the negative impacts.

This project addresses the societal and environmental impacts of generative AI by targeting the opaque and inconsistent zoning regulations that govern data centers and renewable energy facilities. It proposes a human-AI workflow to extract structured zoning information across six Midwest states, creating a searchable, transparent, and replicable database. The resulting tools aim to support sustainable, ...

Non-consensual intimate content, including sexual deepfakes and “revenge porn”, is a large-scale and growing societal crisis. Victim-survivors face relentless violations of their privacy and dignity and must manually report content across hundreds of websites, often with little success. This project builds web-based AI agents that act on behalf of victim-survivors to locate, report, and monitor ...