Artificial Intelligence in your Pocket: Designing Machine Learning Algorithms for Smartphone Sensor Data

In this talk we will explore different approaches for training machine learning algorithms to draw insight from smartphone sensor data. We will also discuss the best practices and most common tools for gathering, managing and using smartphone sensor data, as well as specific examples of companies that have used smartphone sensor data to do incredible things.

Dan is a Data Scientist at TrueMotion, where he develops machine learning algorithms that use smartphone sensors to understand and score driving behaviors. Dan leads TrueMotion's efforts on developing smartphone IMU algorithms to detect hard brakes and distracted driving. Dan is also a deep learning researcher with the Serre lab and a guest speaker at the NYC Data Science Academy. In the past, he has worked as a neurosurgery researcher at Rhode Island Hospital, as a Digital Humanities Programmer at the Brown University Library, and as a Computational Biology Software Consultant for the Weinreich Lab at Brown University. Dan graduated from Brown University in 2015.