PyData Paris 2024 — the speakers, verified

Paris, France · September 2024 · organized by PyData Paris / NumFOCUS

35 speakers below were extracted from the event's public agenda, and PyData Paris 2024 publishes its own site.

Who spoke, and about what

• Marco Gorelli — Senior Software Engineer at Quansight Labs
  • Polars Plugins: how you (yes, you!) can extend Polars source
• Olivier Grisel — Olivier is an Open Source Fellow at probabl and a core contributor to the scikit-learn machine learning library.
  • Keynote: Handling predictive uncertainty in Machine Learning source
• Nour El Mawass — Nour leads the Generative AI technical group at Modus Create. She has a PhD in Machine Learning. and has worked on Machi
  • Evaluating the evaluator: RAG eval libraries under the loop source
• Guillaume Lemaitre — I have a PhD in computer science and have been a scikit-learn and imbalanced-learn core developer since 2017. I am curre
  • An update on the latest scikit-learn features source
• Luca Baggi — ML Engineer @xtream
  • Foundational Models for Time Series Forecasting: are we there yet? source
• Gabriele Orlandi — Data Scientist @ xtream
  • Foundational Models for Time Series Forecasting: are we there yet? source
• Noé Achache — I am a Lead Data Scientist at Sicara, where I worked on a wide range of projects mostly related to vector databases, com
  • Towards a deeper understanding of retrieval and vector databases source
• Nicolas Guenon des Mesnards — Nicolas is a Sr. ML Engineer at GitGuardian where he develops NLP-based technologies to detect vulnerabilities in code a
  • Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems source
• Patrick Hoefler — Patrick Hoefler is a member of the pandas core team and a Dask maintainer. He is currently working at Coiled where he fo
  • Building Large Scale ETL Pipelines with Dask source
• Joris Van den Bossche — I am a core contributor to pandas and Apache Arrow, and a maintainer of GeoPandas. I did a PhD at Ghent University and V
  • The expanding Apache Arrow universe - standardizing and accelerating tabular data access and interchange source
• David Brochart — David Brochart is a Technical Director at QuantStack. David is a contributor to the Jupyter ecosystem. He focuses on the
  • Collaborative editing in Jupyter source
• Maria Knorps — Maria's professional goal is to improve the environment by understanding it first in
  • Evaluating the evaluator: RAG eval libraries under the loop source
• Alonso Silva — Alonso Silva is currently a Researcher on Verifiable AI at Nokia Bell Labs in the Machine Learning and Systems Research
  • Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-weights LLMs source
• Justine BEL-LETOILE — Justine leads the data science team at HelloWork, a digital provider of employment, recruitment, and training solutions.
  • Leveraging LLMs to build supervised datasets suitable for smaller models source
• Cérès Carton — Senior data scientist at HelloWork
  • Leveraging LLMs to build supervised datasets suitable for smaller models source
• Serge « sans » Paille — Sometimes a compiler engineer, sometimes a wood chopper, but also a retired wizard of the coast. Enjoy telling stories,
  • xsimd: from xtensor to firefox source
• Sophia Yang — Sophia Yang is the Head of Developer Relations at Mistral AI, where she leads developer education, developer ecosystem p
  • Keynote: Building with Mistral source
• Max Halford — https://maxhalford.github.io/bio/
  • Unpack business metrics to explain their evolution source
• Kevin Klein — Kevin is a Data Scientist at QuantCo, where he's been working with insurances on fraud detection, risk modelling and pol
  • Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python source
• Francesc Martí Escofet — Francesc is a MSc Data Science student at ETH Zürich. Before that, he graduated in Maths & CS at the Polytechnic Univers
  • Catering Causal Inference: An Introduction to 'metalearners', a Flexible MetaLearner Library in Python source
• Louis Lacombe — Louis Lacombe, Data Scientist at Capgemini Invent, France.
  • Boosting AI Reliability: Uncertainty Quantification with MAPIE source
• Thibault Cordier — Thibault Cordier is a Data and Research Scientist at Capgemini Invent, where he is a member of the Lab Invent team in Fr
  • Boosting AI Reliability: Uncertainty Quantification with MAPIE source
• Valentin Laurent — Senior Data Scientist @ Capgemini Invent
  • Boosting AI Reliability: Uncertainty Quantification with MAPIE source
• Andro Sabashvili — Andro Sabashvili transitioned to a data science career after obtaining a PhD in theoretical physics. Andro's data scienc
  • Adaptive Prediction Intervals source
• Stefanie Senger — Stefanie is an open source developer at :probabl. and a contributor to scikit-learn.
  • An update on the latest scikit-learn features source
• Franz Kiraly
  • sktime - python toolbox for time series: next-generation AI – deep learning and foundation models source
• Benedikt Heidrich — I completed my PhD in deep learning based time series forecasting in 2023 with the Karlsruhe Institute of Technology. In
  • sktime - python toolbox for time series: next-generation AI – deep learning and foundation models source
• Alyssa Wright — Alyssa helps lead Bloomberg’s Open Source Program Office (OSPO) in the Office of the CTO. Bloomberg's OSPO serves as the
  • Open Source Sustainability & Philanthropy: Building Contributor Communities source
• Devpriya Dave — Devpriya Dave is a software engineer on the FX Options team at Bloomberg. While at Georgia Tech obtaining her master's d
  • Open Source Sustainability & Philanthropy: Building Contributor Communities source
• Luis Blanche — I am a freelance Machine Learning Engineer
  • Track your code's C02 emissions with Code Carbon source
• Benoît Courty — Benoît Courty is a data scientist with over 20 years of experience in the tech industry. He began his career as an insid
  • Track your code's C02 emissions with Code Carbon source
• Pierre Raybaut — [Pierre Raybaut](https://pierreraybaut.github.io/) is a long-term advocate of Python in a scientific context, renowned a
  • DataLab: Bridging Scientific and Industrial Worlds for Advanced Signal and Image Processing source
• Guillaume Desforges — Senior Software Engineering at Modus Create with an academic background in mathematics, statistics and AI and profession
  • Processing medical images at scale on the cloud source
• Alix Tiran-Cappello — I have been working as a Data Scientist at Renault Digital for 3 years, focusing on applications for the industry and ma
  • MLOps at Renault Group: A Generic Pipeline for Scalable Deployment source
• Alexandre Carton — I'm a data scientist and ML engineer at Renault, working on putting our ML models into production.
  • MLOps at Renault Group: A Generic Pipeline for Scalable Deployment source

Questions about PyData Paris 2024

Who spoke at PyData Paris 2024?

35 speakers we hold a record for, listed below with the session each one gave. The list comes from the conference's own published agenda, so it is who actually appeared rather than who applied or who paid.

When and where was PyData Paris 2024?

September 2024, in Paris, run by PyData Paris / NumFOCUS. The agenda it was taken from is linked at the top of this page.

Are the talk titles real?

35 of the 35 records carry the session title exactly as the agenda printed it, so every one below is quoted.

Can I contact these speakers?

Not through us. We hold no contact details and no fees. Each entry links to the agenda page it came from, and that is the route.