Name: 52. Paris Women in Machine Learning & Data Science @Probabl
Start: 2025-02-11T19:00:00+01:00
End: 2025-02-11T21:00:00+01:00
Location: Montparnasse Tower

The Women in Machine Learning & Data Science (WiMLDS) Meetup aims to inspire, educate, regardless of gender, and support women and gender minorities in the field.

**We are back for our 52th edition to kick-off 2025!**

All genders may attend our meetups.

Agenda
18:50 arrival to go up to floor 27 in montparnasse tower and ID check
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19:00 - Launch of the evening by Paris WiMLDS meetup team & Probabl
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19:15 - **Decision by AI in medical context, by Christel Gérardin,** MD, PhD, Specialised in Internal Medicine and Automatic Language Processing, Assistant Chief of Clinic.

Abstract: There are now a large number of solutions based on artificial intelligence algorithms in the healthcare sector. However, their implementation in clinical practice is not always guaranteed. The aim of the presentation will be to give several examples of AI algorithms that have been co-constructed from the design phase by multi-disciplinary teams (specialist clinicians, data engineers, developers), and whose performance has been evaluated throughout the development phases.

19:45 - **Learning common structures in a collection of networks. An application to food webs, by Sophie Donnet,** Directrice de Recherche (Senior researcher) INRAE, Unité MIA Paris Saclay

Abstract : Studying networks in ecology helps uncover the structural patterns underlying ecosystem interactions, shedding light on species roles, resilience, and the organization of biodiversity. In this work with Pierre Barbillon and Saint Clair Chabert Liddell, we analyze collections of networks to identify shared structural patterns and cluster them into homogeneous groups. Using a probabilistic approach (Stochastic Block Model (SBM)) and a variational EM algorithm, we capture common connectivity structures and classify networks effectively. This approach, validated on ecological data, reveals structural homogeneity and key mesoscale patterns across diverse ecosystems.
Based on the work [https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.short](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.short)

20:15 - **Data in the shadows: How Open-Source Intelligence and Malicious AI can fuel Cybercrime, by Noor Bhatnagar,** Senior Cybersecurity Analyst, EMEA @cybelangel

Abstract : Data is everywhere and we interact with it on a regular basis. It is collected, analyzed, and shared at an unprecedented scale. But as much as this data powers insights, it also create new vulnerabilities. For data scientists and anyone handling data, the challenge is not only about building smarter models but also navigating the fine line between secure data use and inadvertent risk. As open-source intelligence (OSINT) becomes more accessible, cybercriminals are finding new ways to exploit publicly available data for malicious purposes. Additionally, the rise of AI-driven tools like WormGPT has given bad actors the power to automate attacks at scale. We will explore two critical ways in which data practices — both innocent and intentional — can contribute to the dark web’s ever-growing ecosystem.
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**20:45 - Cocktail & Networking**
The cocktail is sponsored by probabl.
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After the meet-up, a summary be available on our Medium page : [https://wimlds-paris.medium.com/](https://wimlds-paris.medium.com/)
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Code of Conduct
WiMLDS & MLOps are dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form.
All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate.
Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate.
Thank you for helping make this a welcoming, friendly community for all.
All attendees should read the full Code of Conduct before participating: [https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct](https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct)

Marie S

Caroline Chavier

Jihane

Chloe

Juliette Bassnagel

Paris Women in Machine Learning & Data Science

Technology

Data Science

Data Mining

Machine Learning

Big Data

Open Source

Women in Technology

Data Management

Professional Women

Data Analytics

Data Visualization

Predictive Analytics

Artificial Intelligence

WiMLDS

52. Paris Women in Machine Learning & Data Science @Probabl

Montparnasse Tower

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Paris Women in Machine Learning & Data Science

52. Paris Women in Machine Learning & Data Science @Probabl

Paris Women in Machine Learning & Data Science

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