Thorsten Joachims, professor and chair of Information Science, was recently one of eight researchers awarded Bloomberg Research funds to support cutting-edge work in data science.
Joachims' proposal, Counterfactual Learning with Log Data, aims to develop methods for Deep Learning with log data, which – Joachims argues – is one of the most ubiquitous forms of data available. It can be recorded with a variety of online systems, like search engines and terminal browsing, at little cost.
The funds come courtesy of The Bloomberg Data Science Research Grant Program, which supports research in areas like natural language processing, machine learning and search and ranking.