Reinforcement Learning has received an enormous amount of attention in the machine learning community recently, with milestones such as the defeat of the world champion Go player to Google's AI making headlines, and companies like OpenAI promoting research. In this context, it makes sense to explore the role that Reinforcement Learning can play in a data scientist toolbox.
Brian Farris completed his PhD in theoretical astrophysics at University of Illinois Urbana Champaign. Subsequently he worked as a postdoctoral researcher at Columbia University and NYU. His research work was in supermassive black-holes mergers, gravitational wave astrophysics, numerical relativity and fluid dynamics. He performed large scale simulations using distributed computing techiques which he now employs in data science. Following his posdoctoral work, Brian began his transition out of academia by participating in the Data Incubator Fellowship. He was subsequently hired as a data scientist at Capital One Labs in NYC, where he currently works on a range of topics including reinforcement learning, artificial intelligence and marketing analytics.
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