Pierre Laffitte is an ML and data science leader with a decade of academic and industrial R&D experience, specializing in audio signal processing, music information retrieval, and multimodal machine learning. He has led and scaled research teams, managed product-focused ML development, and shipped prototypes into production across startups and research institutes. His PhD-level background in computational and applied mathematics and stints at IRCAM and Carnegie Mellon underpin a rigorous approach to problems like automatic melody transcription and psychoacoustic optimization. Based in Copenhagen, he blends hands-on engineering with Agile research management and a tangible track record of turning complex audio research into deployable products. Notably, he has applied nonlinear optimization and gradient-based methods to perceptual audio masking—an uncommon bridge between psychoacoustics and practical ML systems.
8 years of coding experience
12 years of employment as a software developer
Computer Systems Networking and Telecommunications, Computer Systems Networking and Telecommunications at Universidad Politécnica de Madrid
Doctor of Philosophy (PhD), Computational and Applied Mathematics, Doctor of Philosophy (PhD), Computational and Applied Mathematics at University of Lille 1 Sciences and Technology
Master of Engineering (M.Eng.), Telecommunications Engineering, Master of Engineering (M.Eng.), Telecommunications Engineering at Télécom SudParis
Visiting Scholar (PhD candidate), Langage Technologies Institute, Visiting Scholar (PhD candidate), Langage Technologies Institute at Carnegie Mellon University
A chrome extension that keeps track on where am I on my favourite shows
Contributions:4 PRs, 31 pushes, 11 branches in 4 months
chromefavouritechrome-extension
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