https://medium.com/@dessa_/space-2-vec-fd900f5566
Jan 18
Pop into Dessa’s offices and you’ll soon find traces of the company’s fascination with outer space. A Lego replica of Saturn V, the rocket that made it to the moon, sits on our reception area coffee table. Venture a bit further, and you’ll discover that each of the meeting rooms are named after spaceports from around the world. For a few employees on Dessa’s software and machine learning engineering teams, this company-wide enthusiasm for space has evolved into a full-blown passion project.
The space2vec team has transformed our company’s enthusiasm for space into a serious side-hustle. Pictured left to right: Jinnah Ali-Clarke, Pippin Lee & Cole Clifford.
Combining their interest in space with an exploration of deep learning’s potential applications for astronomy, 3 of Dessa’s engineers have collaborated on a project called space2vec since summer 2017. Recently, the team built a deep learning system that identifies supernovas from telescope images with record-breaking speed: cutting the time it would take astronomers to identify supernovas almost in half! Here’s the team’s personal account of how they did it.
It was summer 2017 when Cole, one of our Machine Learning Engineers, first started flying these words around the Dessa office. The team was discussing dream projects to work on within the field of deep learning, and a quick survey of the room confirmed our initial suspicions… to work on something space-related would be pretty freaking awesome!
Luckily, we found ourselves in the right place at the right time. The company had recently started a program for employees to dedicate a portion of working hours to personal projects that would help us advance our machine learning knowledge.
We knew this program would help advance our product, too. At Dessa, two-thirds of the space2vec team (Jinnah and Pippin) spend the majority of work-time building features for Foundations, a platform for engineering enterprise-grade AI solutions. In order to build tools for ML engineers that they actually want to use, it’s essential for us to understand how they work and what problems they face from end to end. While the frequent exchange of feedback between our machine learning and software engineering teams is extremely useful, one of the best ways to build empathy into the product is to tackle a machine learning project hands on. Working on space2vec offered us an amazing way to get there, while also giving us the opportunity to grow our knowledge of space.
Before we started writing code, we wanted to figure out if we could find an answer to the following question:
Can we find a real, annoying problem in astronomy where we can help by applying advanced machine learning techniques like deep learning?
To find out, we interviewed six different astronomers. As we began our research, we were very aware of our lack of astronomy knowledge, but to our delight the astronomers we spoke with were all wonderfully welcoming.