See our Featured Projects


Cycle 1

You can read about our first results here.

Cycle 2


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.


Cycle 3

harnessing big data to predict the impact of automation with the future of work commission.


Work is at the centre of people’s lives, communities and the economy. Yet the world of work is changing at a faster pace than policy makers can keep up with. The Institute for the Future of Work (IFOW) was set up to better understand and pilot solutions to the rapid changes occurring in UK labour markets. RAIL worked with IFOW to comprehensively map out ways in which the IFOW could harness machine learning and big data to improve its work. RAIL then helped the IFOW to design a number of machine learning projects to identify those most at risk of displacement and to develop tools to better support their successful transition.


improving online conversations with alphabet’s jigsaw.


A number of organizations have taken on the challenge of trying to mitigate online abuses. Jigsaw’s Conversation AI team has developed machine learning models and tools for assisting moderators to filter toxic comments which are already helping to foster healthier engagements online. RAIL worked to identify the more subtle indicators of problematic online conversations, such as condescension or hostility, which are more ambiguous and difficult to pin down.