Deep Learning and Real-Time Flight Prediction

Which of the over 30 million commercial flights in the US will get actually delayed or cancelled? Freebird has built a business based on using data science to answer that question. Learn how co-founder and CTO Sam Zimmerman and his team have approached this problem by building a real-time predictive analytics engine based on dynamic data sets and deep-learning algorithms. This talk focuses on experiments the Freebird team has done to model the both point-wise and aggregative flight delay risk using various deep learning approaches and feature representation techniques.

Sam Zimmerman, CTO at Freebird

Sam is the CTO and co-founder of Freebird. As part of his role, Sam leads the data science team developing the data systems and predictive analytics that power the Freebird travel intelligence and rebooking solution. Freebird dynamically predicts the impact of flight disruptions and the expected rebooking costs, by leveraging a diverse range of data science, statistical analysis, and machine-learning techniques. Sam has extensive experience in the commercial application of machine-learning algorithms. Prior to this, Sam worked as a quantitative risk analyst in the currency markets and as a team lead automating a large-scale data classification problem for an energy intelligence company. Sam is a Duke University graduate and works on a grant with MIT’s Computational Cognitive Science group to extend decision theory using advancements in machine learning and artificial intelligence.

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