Join Data Science Festival London, in partnership with Trainline this March. The evening will consist of talks on how Trainline use anomaly detection techniques to automatically detect fluctuations in demand and will be discussing the role of the Data Science team in a commercial e-commerce business.
Those randomly selected and approved will then be e-mailed tickets for the event. If you do not receive an approval e-mail from us by the 11th of March you have been unsuccessful in getting a ticket for this event.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
Please click here to apply for a ticket: https://www.datasciencefestival.com/event/dsf-meetup-with-trainline/
6:00pm: Guests arrive
6:30pm: Louisa Johns & Tim Williams
7:15pm - Break & Refreshments
7:45pm - Miriam Redi
8:30pm - Networking
9:00pm - Close
Address: 120 Holborn, London EC1N 2TD
Louisa Johns - Data Scientist at Trainline
Bio: Louisa is a data scientist at Trainline. She has developed an in-depth knowledge of the UK rail network on here current project and is particularly interested in the on-time performance of trains on the network.
Tim Williams - Data Scientist at Trainline
Bio: Tim is a data scientist at Trainline and developed our anomaly detection system alongside other members of the team. In his spare time he loves a good game of “guess the anomaly”.
Summary: Have you ever been stuck on a train with a crowd of football supporters when you weren’t one of them? Missed out on those cheap train tickets because they sold out so fast? In this talk we will review how we used anomaly detection techniques to automatically detect these fluctuations in demand. We will also discuss the role of the Data Science team in a commercial e-commerce business and how we determine how we work and what we work on.
Miriam Redi - Research Scientist at the Wikimedia Foundation and Visiting Research Fellow at King's College London
Title: The Science of (Visual) Knowledge Equity
Abstract: In this talk, we will see how computer vision and machine learning can support knowledge equity and help to break down the social, political, and technical barriers preventing people from accessing free knowledge. We will look at technologies designed to bridge content and verifiability gaps in Wikipedia. We will see how multimedia retrieval techniques can be used to break language barriers by visually enriching Wikimedia projects, and we will learn how science can promote knowledge equity in online and offline communities beyond Wikimedia spaces.
Bio:Miriam Redi is a Research Scientist at the Wikimedia Foundation and Visiting Research Fellow at King's College London. Formerly, she worked as a Research Scientist at Yahoo Labs in Barcelona and Nokia Bell Labs in Cambridge. She received her PhD from EURECOM, Sophia Antipolis. She conducts research in social multimedia computing, working on fair, interpretable, multimodal machine learning solutions to improve knowledge equity.