Predicting events using machine learning

Kira Radinsky has garnered international recognition for her work forecasting real-world events, like disease outbreaks and riots.

She helped build a predictive system that combined newspaper articles, Wikipedia, and other open data sets. Through fine-tuned semantic analysis and NLP, Radinsky and her collaborators devised new metrics of similarity between events.

Early versions of a predictive system did not yield interesting results, until Radinsky and her collaborators discovered the additional insights they could derive from correlations. “The problem was, when we were looking at only patterns of causality, we used to have only trivial things”

The techniques she developed for that predictive software system are now the foundation of applications across many areas. She’s currently the CTO and co-founder of SalesPredict, a start-up using predictive analytics to “understand who’s ready to buy, who may buy more, and who is likely to churn.”

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