Douglas Eck
Senior Research Director at Google DeepMind
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDouglas Eck is a Senior Research Director with 17 years of experience leading large AI teams at Google and DeepMind, co-creating foundational generative media projects across image, video, 3D, music and audio. He founded Magenta to explore AI’s role in music and art, and has driven both core research (e.g., Music Transformer, Imagen, Phenaki) and product-facing systems such as music recommendation and large-scale LLM tasks. Trained as a cognitive scientist with a PhD and postdoc in machine learning, he blends HCI, neuroscience-informed music research, and scalable ML engineering. An active open-source contributor, he has directly improved MIDI tooling and Magenta’s music-generation code, surfacing practical fixes that enabled better event ordering, tempo handling, and Jupyter-friendly workflows. Based in San Francisco, he is known for translating deep research into tools that give artists and users creative agency with AI.
17 years of coding experience
20 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Indiana University BloomingtonPostdoctoral Fellow Machine Learning, Postdoctoral Fellow Machine Learning at IDSIA
English, French, Italian
Github Skills (10)
midi10
music-information-retrieval10
midi-instrument10
machine-learning10
tensorflow10
python10
sorting-algorithms9
sorting9
jupyter-notebook9
bokeh8
Programming languages (5)
Github contributions (5)
magenta/magenta
Jun 2016 - Dec 2018
Magenta: Music and Art Generation with Machine Intelligence
Role in this project:
ML Engineer
ML EngineerContributions:23 commits, 43 PRs, 29 pushes in 2 years 6 months
Contributions summary:Douglas primarily contributed to the Magenta project by upgrading dependencies, fixing bugs related to MIDI file handling, and improving the functionality of the music generation components. They addressed issues with drum track recognition and event ordering within MIDI files. Furthermore, the user fixed a call within the MelodyRNN model and added functionality for plotting and downloading Magenta bundles within a Jupyter Notebook environment.
artmusic-generationgenerative-artmagentamachine-learning
craffel/pretty-midi
Nov 2015 - Apr 2017
Utility functions for handling MIDI data in a nice/intuitive way.
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 commits, 9 PRs, 26 comments in 1 year 4 months
Contributions summary:Douglas primarily contributed to the core logic of the pretty-midi library, focusing on improving MIDI data handling. Their work involved fixing bugs related to time conversion and event sorting within the `pretty_midi.py` file. The user also added features such as setting default values for time signature events and indexing instrument note events by channel, improving the library's functionality and correctness. Additionally, they resolved a tempo-related indexing issue and enhanced the sorting of events for improved performance.
utility-functionsmidiintuitivehandlingmusic
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