Thanks to Equinor for sponsoring this meetup.
Talk 1: Non-trivial at scale: the mountains we need to climb to transform the energy sector with data driven applications
We will discuss some of the most pressing and complex data science problems we have, and how we intend to solve them at scale.
- Protecting our assets, colleagues and the environment: Making machines in offshore installations operate autonomously
- True competitive advantage: Maximising production from hydrocarbon source you don’t see and you can’t measure
- Protecting our colleagues: Building a machine that understands the context of safety incidents
- Protecting the environment: Detecting micro-seismic activity from CO2 injection
- Protecting our assets: Baselining complex machines and detecting deviations
How to do it at scale?
Bio: Ahmed Khamassi is VP Data Science at Equinor. Formerly at JP Morgan, Wipro Digital, SAS and Google.
Talk 2: Transfer Learning for Fun and Profit
With very little setup time, we needed to track lots of dishes at the Oktoberfest 2017. This talk explains how we mined those dishes from raw videos without prior data. We’ll recap how to choose the right pretrained model for human-in-the-loop labelling. Next, we’ll cover how occlusion, realtime constraints and motion blur affect the entire object tracking pipeline. The talk concludes with thoughts on the future of sharing visual datasets.
Bio: Alexander Hirner is the founder and CTO of moonvision.io
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