Bio

At Sentilink, we are stopping synthetic fraud and building the world's first identity bureau! Before Sentilink, I was a data scientist at Facebook. Prior to that I taught data science to companies with Metis. Projects I personally worked on include developing lessons (Power Point decks and Jupyter Notebooks) for courses on Generative Adversarial Networks and Time Series for a Fortune 100 client, and on the teaching side, have delivered multiple week-long in person trainings covering machine learning (common supervised and unsupervised algorithms), Deep Learning, and Spark to consultants at a top 10 consulting firm. In addition, I write and speak about the latest developments in Deep Learning (which I'm particularly interested in) on Medium and at leading industry conferences. Since May 2018, I've been writing a book on Deep Learning for O'Reilly (to be called Deep Learning From Scratch), targeting publication in August 2019. Finally, to scratch my engineering itch, I'm contributing engineering work to a couple of Python-based projects (see ''Projects'' below). Previously I worked at Trunk Club, where I worked with business leaders and product managers to figure out what models needed to be built (e.g. lead scoring, recommender systems), and then deployed those models as Python microservices. My formal education is in pure mathematics: I love learning through MOOCs, and have learned almost all of what I know about coding and data science through taking courses on Udacity, Coursera, and Udemy.