AI & Machine Learning

Organizations delivering services through digital technology have the opportunity to use machine learning and artificial intelligence to improve their products and services.

When training and education are provided through digital technology, it is possible to customize the individual’s experience based on their characteristics as well as their prior interaction with the application. Machine learning methods can be applied to estimate what works for whom, and why. The lab specializes in adapting machine learning methods to focus on heterogeneity in the effect of interventions. Planning experiments with the goal of learning about heterogeneity is another area of interest.

Another area of focus is developing algorithms that are used to improve personalization for digital services. Innovations in machine learning in the last decade have led to improvements in algorithms for making recommendations to individuals about content, and these can be applied to, for example, recommend stories to students in an educational application. Artificial intelligence can be used to “get to know” an individual and adapt the information presented based on previous experience; the lab makes use of methods such as multi-armed bandit algorithms and reinforcement learning to accomplish this.

Many commonly used methods in artificial intelligence are designed for large systems with many users, in environments where mistakes have few consequences. The lab works to customize and improve these algorithms for the context in which we apply them, where the user base may be smaller and mistakes more costly, such as educational applications.

Project Abstracts

Read about a few of the research projects the lab is currently working on.

Do Commonly Prescribed Drugs Prevent COVID-19 Symptoms?

A variety of commonly prescribed drugs have the potential to prevent or treat COVID-19 symptoms. Using historical health claims data and methods combining machine learning and causal inference, this project identifies if these commonly prescribed drugs improved the outcome of patients hospitalized during the COVID-19 crisis. The analysis compares the patient outcomes of those who were already taking these drugs to those who were not at the time of hospitalization and uses a technique that pools information from different hospital systems from around the world to enable a larger sample to be considered together.


Evaluating the Benefits of Commonly Prescribed Drugs to Prevent Poor Outcomes from Respiratory Distress

Applying recently developed machine learning and causal inference methods to historical health claims data allows the evaluation of the impact of certain medications on outcomes for patients hospitalized with respiratory conditions. The methods incorporate new techniques to estimate causal effects across distinct, proprietary data sources without merging the datasets. The results can be used to suggest candidates for clinical trials in the fight against COVID-19.


Improving In-App Recommendations and Assessments

Lab researchers are collaborating with Stones2Milestones to design a system that recommends new in-app content for users in order to improve the students’ interest and engagement in the educational content. In addition, the lab is identifying methods to improve how users are assessed both on the learning occurring within the app and across a user’s educational journey.


Boosting Efficacy, Efficiency, and Accountability in Government Training Programs

Lab researchers are building a tool to help participants in government-funded training programs to select the programs likely to be most effective, taking into account an individual’s circumstances and qualifications. With a broad set of collaborators, the lab is using administrative data, science, and technology, and the outcomes of previous program participants to create the tool. This project will also incentivize training programs to add value to the earning capacity of program attendees.

Government Services

Examining the Social Value of Targeting Interventions in Retraining Programs

This project uses administrative data, machine learning, and causal inference methods to evaluate the effectiveness of job retraining programs in Rhode Island for different types of individuals. Understanding which segments of the population do not respond well to existing programs in turn informs the design of job retraining programs as well as which programs to recommend to different individuals.

Worker Training