Beyond the Classifier: Inspiration from Engineering Algorithms

Many data scientists work within the realm of machine learning, and their problems are often addressable with techniques such as classifiers and recommendation engines. However, at Tapad, they have often had to look outside the standard machine learning toolkit to find inspiration from more traditional engineering algorithms. This has enabled them to solve a scaling problem with their Device Graph’s connected component, as well as maintaining time-consistency in cluster identification week over week.

In this talk Yael Elmatad, Data Scientist at Tapad, will discuss two algorithms they use frequently for these problems, namely the Hash-to-Min connected component algorithm and the Stable Marriage algorithm.

Bio - Yael Elmatad is a Data Scientist at Tapad. Prior to Tapad, Dr. Elmatad was a Faculty Fellow and Assistant Professor at NYU Physics Department, specializing in the use of high-performance computing to study model space parameter optimization. Ms. Elmatad holds a PhD in Physical Chemistry from University of California, and BS in Mathematics, Computer Science and Hebrew Language from New York University.

This talk was given at the NYC Data Engineering meetup in July 2016.