See our Featured Projects
You can read about our first results here.
Improving homeless service delivery with New York City.
Nearly 60,000 New Yorkers reside in homeless shelters. Using information collected through surveys at intake, RAIL worked to predict how long homeless families would stay in shelter and which families would return to shelter. RAIL’s deep learning and penalized regression-based algorithms predicted whether a family would return to shelter. With better information, New York City can more efficiently plan shelter capacity and more effectively deliver aftercare services to families who have moved to permanent housing.
Predicting diabetes diagnosis with a developing country's largest health insurance scheme.
During RAIL’s first multi-term project, we used terabytes of data to predict whether or not millions of individuals would develop diabetes in the next 12 months. RAIL built a start-to-finish machine learning pipeline that incorporated sample rebalancing, feature engineering, and ensembling. RAIL also built custom deep neural networks and employed state-of-the-art prediction models. Results are expected in the next week.
Predicting renewable energy market prices with an energy markets information group.
Renewable energy makes energy prices more uncertain in the short term; reducing the variation can help make renewables purchases more economical and more stable. RAIL built several multidimensional, time-series-based deep learning models that outperformed traditional models at 24-hour-ahead prediction. RAIL also delivered a survey of the landscape of state-of-the-art models and provided an implementation plan moving forward.
Applying AI to antibiotic drug discovery with Public Health England.
RAIL surveyed how machine learning can aid in the search through thousands of proteins and hundreds of thousands of drug targets for Tuberculosis. RAIL also built several proof-of-concept machine learning models. New drug discovery is one of PHE's critical tactics towards combatting antimicrobial resistance.