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On the 24th, we teamed up with CORE and learned all about Stripe, one of the most exciting companies in fintech building a complete payments platform. Stripe engineers (including some Columbia alums!) introduced us to what Stripe does, and the technical challenges they tackle each day at their jobs. Machine learning engineer Sharif Corinaldi presented a talk, “White boards vs. Black boxes: A Physicist’s Grapplings with Machine Learning.”

For those of you who missed the talk, here’s the abstract:

Physicists feed off of intuition. We make theories (the fewer the better!), toy mental models, and verify the whole she-bang with reproduce-able experiment. Machine Learning is, for the most part, still an opaque, arcane art. No one really understands how the advanced techniques work; Large, proprietary datasets strain reproducibility. This talk will explore how Stripe navigates this tension. We’ll explore some of Stripe’s ML infrastructure and see how these systems facilitate our search for the holy grail: solutions complex enough to work, but simple enough to understand.

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