Remarkable women are actively changing the world of apps, and Maria Larsson is one of them. The machine learning (ML) engineer’s work on sleep-tracking app Sleep Cycle has helped bring a greater understanding of sleep to millions of users.
Name: Maria Larsson (she/her)
Role: Machine learning engineer
App: Sleep Cycle from Gothenburg, Sweden
Favourite empowerment emoji: 🦾
For Sleep Cycle, Larsson gets deep into the algorithms. By analysing user data – taken from those who have consented to its use – Larsson aims to make the app smarter and more impactful on people’s sleep health.
Making sounder sleep a dream come true
One of Larsson’s most meaningful projects to date was building Sleep Cycle’s so-called Snoracle (a mix of the words snoring and oracle), which greatly improves sleep tracking, and ultimately sleep, for the app’s users.
The Snoracle algorithm is designed to attribute any snoring sounds to the correct person in the room. “Before, we had another model running and sometimes users would ask if the app would also pick up their partner snoring next to them during the night as well,” Larsson recalls.

Using her ML skills and the snoring data that some of the app’s users offered for testing, Larsson came up with a model that can distinguish between snores. “Basically you label some of the snoring as ‘this is me’ or ‘this is not me’ and do that for a couple of snores before it automatically categorises the snoring source to be yours or someone else’s,” she explains.
When Larsson realised how many cool things she could build with ML, she decided to help other women break into the field. In her own time, she occasionally offers workshops where women can code and try solutions in a safe space: “It turned out that a lot of women came to be like ‘This is fun’, and they would never have tried it if it wasn’t for the event.”

With a lot of technology, I think you can see that it’s sort of biased towards male usage.– Maria Larsson

Follow in her footsteps
These days, learning online and through apps is ubiquitous, and that holds true for ML and problem-solving. Larsson recommends trying courses on the most-used algorithms. She also suggests checking out platforms such as Kaggle where you can create your own projects: “It’s like ‘Here’s data, here’s a problem – please fix it’ and then there are prizes for the best solutions.”
Larsson believes that more women are needed in order to re-shape technology and make it appeal to more users. “I previously worked for a camera company, and the camera handle was not made for small hands,” Larsson recalls. “With a lot of technology, I think you can see that it’s sort of biased towards male usage.”
With more diverse opinions like Larsson’s spearheading the design of products and algorithms, we’re on the path to achieving lasting change.
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