#32 Paris Women in Machine Learning & Data Science: joint meetup with Dresden

To continue our 2021 European tour, in March we had another joint meetup, this time with WiMLDS Dresden.

To kick off the event, Chloe-Agathe Azencott from Paris chapter introduced the agenda and the chapter, which is the 3rd largest in the world, with over 4000 members.

This was followed by a presentation by Shima Asaadi and Sarah Krebs from Dresden, who emphasized diverse and dedicated character of the Dresden chapter.

The first speaker was Sandra Lorenz, Researcher at the Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf. Her presentation was entitled “The role of data science and machine learning to achieve sustainable mineral exploration” .

As of today, we need plenty of raw materials from the ground to run the latest technology: you might have heard of cobalt, lithium or tungesten among others. Obviously, before starting to mine, you want to know what you might find and if it’s worth it. Otherwise you might dril holes and destroy a part of ecosystems… for nothing. Against it, the idea is to build a classifier that takes hyperspectral data imaging cubes and annotates it with the composition of the sample.

Sandra presented the challenges associated to such classifier. Here are two of them: you have to work with 3D points, plus the images might not be always the best quality as they are collected by (unstable) drones. She also presents some solution associated, one of them being the use of drone swarms.

If you want to know more, check out her slides below.

The second speaker was Anastasia Lieva, Director of Data Science and User Performance at Comwatt. Her presentation was entitled “Extreme Data Science”, and was accompanied by elegant watercolour slides.

To start with, what is extreme programming? It’s a software development methodology aiming at improving software quality and responsiveness to changing customer requirements. The image below details the key points of this methodology.

Can you lead a data science project with the extreme programming methology? Anastasia argues that it’s not only possible, but recommended, because the tech debt is much worse in data science projects than in traditional software development, and because the data science project are often left on their own and expected to reach the intended goal thanks to some “machine learning magic”.

You can find Anastasia’s tips in this thread:

If you want to know more, check out her slides below.

🎬 You can watch the entire meetup here

All our meetups in 2021 are joint European meetups! If you do not want to miss them, 🔗 follow our Twitter account, Meetup page, and LinkedIn page.

You can also:

📑 check our Google spreadsheet if you want to speak 📣, host 💙, help 🌠

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📩 send an email to the Paris WiMLDS team to keep in touch >paris@wimlds.org

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