#31 Paris Women in Machine Learning & Data Science: joint meetup with Limassol
To begin 2021 with a bang, we had a new joint European meetup. After Berlin in April 2020, Milan in June 2020, and Brussels in September 2020, we partnered with the Limassol team (Cyprus) — Georgina Tryfou and Christina Orphanidou to organise a joint event in February 2021.
It was a roaring success, with over 80 people attending. (If you were not one of them, do not miss our next events!)
To kick off the event, Natalie Cernecka from Paris chapter introduced the agenda and the Paris chapter. She shared some lessons we’ve learnt building this chapter:
Then the Limassol chapter introduced themselves. The chapter was founded in November 2019, and their members come from the whole island. The ML field is growing rapidly in Cyprus these days. Remarkably, Cyprus participated in EU code week with 49% female participants! 👏
You can consult the presentation here:
🎬 You can watch the entire meetup here :
The first speaker was Dr Xenia Miscouridou, who is a Research Associate at Imperial College London in the Statistics Section of the Department of Mathematics. She presented her work on Bayesian Probabilistic Modelling for Social Interaction Data.
The model is a graph, where each node is a person, and the data is a triplet representing the node sending the interaction, the node receiving it, and the time of this interaction. The hypothesis is that an interaction will trigger another interaction. You can then model the interaction between nodes in a network with
What’s it all useful for? For example, once you’ve inferred the model parameters from your observed network, you can then predict the number of future links, or measure effects of interventions. Cherry on top, the model is both explainable and scalable!
If you want to know more, check out her slides below.
The second speaker was Sotiria Bampatzani. She is NLP Data Engineer at QWAM Content Intelligence and presented her work on Named Entity Recognition (NER) from a business point of view: how a rule-based approach with Machine Learning algorithms.
The objective of NER is to recognize and classify entities in a text, such as a person, a organization or also a date or an amount. It’s a supervised task which requires a enormous amount of labelled data. To acquire this data, you can actually use old methods which are rule-based! They fall short if used alone, but are great to do a first round of labeling.
If you want to know more, check out her slides below.
Our next meetups will be all joint European meetups! Can you guess with which chapter we are partnering? Check out the European chapter on our website if you want some help 😉
If you want to keep posted about our activities, you are welcome to:
📑 check our Google spreadsheet if you want to speak 📣, host 💙, help 🌠
🔗 follow our Twitter account, Meetup page, and LinkedIn page
📍join our Slack channel for more discussions about machine learning, data science, and diversity in tech!
📩 send an email to the Paris WiMLDS team to keep in touch >paris@wimlds.org
🎬 follow our WiMLDS Paris Youtube channel
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🔥 Feel free to share your company or lab’s job positions for free on WiMLDS’ website.
