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Pedro Bizarro

@pedrobizarro.bsky.social

223 followers 673 following 16 posts

Co-founder & Chief Science Officer @ Feedzai Data Processing, Data Visualization, ML, Responsible AI, Fraud Detection, AML, Science. Ironman, running Research: research.feedzai.com Youtube: bit.ly/3nnnb9T arXiv: bit.ly/3tCxDv2

Posts Replies Media

Pedro Bizarro @pedrobizarro.bsky.social · 28/11/2024

This is great! A reading list of science books published in 2024!

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Reposted by Pedro Bizarro

Carlos Baquero @xmal.bsky.social · 27/11/2024

A short analysis of what might have contributed to the recent rise of BlueSky. Now, on the Blogs section at Communications of the ACM cacm.acm.org/blogcacm/the...

cacm.acm.org

The Rise of Bluesky – Communications of the ACM

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Pedro Bizarro @pedrobizarro.bsky.social · 27/11/2024

🥳 Yay: new blog post out, a summary of a recent Feedzai Research paper: “Show Me What’s Wrong!”: Enhancing Fraud Detection Analysis by Combining Charts and Text. medium.com/feedzaitech/...

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Pedro Bizarro @pedrobizarro.bsky.social · 21/12/2023

🎉 If you just finished or are about to finish your MSc and are passionate about AI, we have a position for you at Feedzai Research! Actually, we have *two* positions, bring a friend! 😎 More of AI work here: research.feedzai.com/research_are... Job descriptions: careers.feedzai.com/job_descript...

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Pedro Bizarro @pedrobizarro.bsky.social · 20/11/2023

We are hiring 2 people for our Systems Research - ML Infrastructure team. 🎉😎 Software Engineer, Data and ML Infrastructure careers.feedzai.com/job_descript... Senior Software Engineer, Data and ML Infrastructure careers.feedzai.com/job_descript...

Systems Research: The Systems Research group focuses mostly on the performance and reliability of engineering systems. It focuses on projects that enable new software capabilities important for a RiskOps Platform, and improve systems characteristics such as memory, storage, computation, latency, throughput, availability, cloud cost, reliability and stability, etc.

Research Focus: Streaming Engines, Graph-based storages, ML infrastructure, Distributed processing

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