Jessica Forde

Jessica is a Machine Learning Researcher specializing in Bayesian Machine Learning and Reinforcement Learning. Her projects at Columbia include clustering patients based on electronic medical record data, recommendation systems for sustainable smart building technology, and classification of legal documents for freedom of information act requests. She contributes to Density, an open ­source tool for understanding behavior on ­campus using wireless router data. Jessica was a data scientist at McKinsey, advising clients on machine learning applications in human resources. She also developed the open­ source machine learning library, datamicroscopes, with Qadium and DARPA. Jessica has spoken at data science conferences such as Pydata New York and GHC1 in New York, where she moderated a panel on women in data science.