Speaker "Dan Shiebler" Details Back



Real World Data Science Strategy


In this talk we will explore how to solve a poorly formed machine learning/data science problem in a business setting. Among other things, we will discuss:

- Defining meaningful performance metrics that correlate strongly with business goals

- Handling a lack of labeled data and properly managing label uncertainty

- Thinking about user experience and legal constraints

- Managing novel unstructured data


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.