Javier Nistal is a researcher on artificial intelligence for music and audio applications, specializing in deep generative models and sound synthesis. His work focuses on creating intuitive, controllable tools that enhance human-machine collaboration, making music technology more accessible and transformative for artists and producers alike.

He holds a degree in Telecommunications Engineering from the Technical School of Madrid (UPM), a master’s in Sound and Music Computing from the Music Technology Group (MTG) in Barcelona, and a PhD from Télécom IP Paris. Javier’s professional journey includes internships at SoundCloud, Jukedeck, and a year as a Junior Machine Learning Engineer at Music Tribe, where he worked on AI features for mixing consoles.

At Sony Computer Science Laboratories, he has developed groundbreaking tools such as DrumGAN, the first AI-driven drum synthesizer commercialized as a VST plugin, and Diff-A-Riff, a state-of-the-art system for musical accompaniment co-creation using latent diffusion models. 

As an active contributor to the field, Javier has published at leading music conferences such as ISMIR, NeurIPS, and ICML, advancing the state of AI for music.