Emmanuel Mogenet is no longer with Google as of spring 2018. The below reflects his efforts during his tenure at Google.
I joined Google in February 2006, after a 15 years career in the trenches as a software engineer. During that time, my work was mainly focused on 3D Computer Graphics, Image Processing and Special Effects, and I worked at various companies including Apple Computers, Nothing Real, Sony Pictures ImageWorks, Alias/Wavefront and Thomson Digital Image.
My areas of interest include analog electrical engineering, 3D computer graphics, signal/image processing, computer vision, machine learning, information retrieval, cryptography, crypto-currencies, compression and low-level optimization.
I have held multiple roles at Google:
- I initially (2006) worked with the SRE organization where I helped design and build some of Google's planet-scale operational functions, a set of problems spanning large distributed systems, monitoring, automation, networking and hardware.
- I subsequently (2010) took leadership of the Zürich section of the Search engineering organization, where I oversaw a broad portfolio of strategic projects, including:
- Now On Tap
- Google Alerts
- Personal search results
- Freshness in search ranking
- Google's Contextual search engine
- Search Infrastructure, a portfolio of projects that help power Google's search engine
- Engine that powers Knowledge Graph
- Image Search infrastructure, the large image processing pipeline that powers Google Image Search
- In 2015, I started to organize the group that has now become Google Research Europe, a brand new research team based in Zürich Switzerland, where we focus on four areas:
- Machine Learning
- Natural Language Understanding
- Computer Perception and on-device machine intelligence
- I grew up, studied and started my career in France (Marseille, St-Etienne, Paris) and subsequently lived and worked in Singapore, Tokyo, Los Angeles, and currently Zürich, Switzerland.
- I started experimenting with deep(-ish) neural networks around '93 with a goal to apply them to image processing, at a time when they were far from fashionable and very far from powerful enough, especially when compared to how powerful they are today. I am truly amazed at how far deep neural nets have come since then and I am truly impatient to discover what we will be able to do with them next.