Track: Groking Timeseries & Sequential Data

Location: Cyril Magnin I + II

Day of week: Wednesday

Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocessing, & crunching of related algorithms.

A time series is a series of data points indexed (or listed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Sequential data looks at data problems where the ordering of data matters. Image processing and NLP/NLU fall in this space. The Groking Timeseries and Sequential Data track looks at the role of timeseries and sequential data in modern application development.

Track Host: Jendrik Jördening

Data Scientist @Nooxit

Jendrik is Head of Data Science at a stealth startup. He formerly worked at Aurubis and Akka Germany on Data Science and Deep Learning in the field of industry 4.0 and autonomous machines.

At the same time he took part in the Udacity Self-Driving Car Nanodegree, participating with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California.

9:00am - 9:40am

Uber Marketplace: Spatio-Temporal Data

Chintan Turakhia, Engineering Manager @Uber

10:00am - 10:40am

Forecasting with Prophet

Sean Taylor, Core Statistics Team @Facebook

11:00am - 11:40am

Modern NLP for Pre-Modern Practitioners

Joel Grus, Senior Research Engineer @AllenInstitute

12:00pm - 12:40pm

Deep Learning with Audio Signal: Prepare, Process, Design, Expect

Is deep learning Alchemy? No! But it heavily relies on tips and tricks, a set of common wisdom that probably works for similar problems. In this talk, I’ll introduce what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression -- how to prepare the audio data, pre- and post-process them, how to design the networks (or which one to steal from), and what we can expect as a result.

Keunwoo Choi, Research Scientist @Spotify

2019 Tracks