Douglas Eck
Senior Research Director at Google DeepMind
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDouglas Eck is a Senior Research Director at Google DeepMind in San Francisco with 16 years of experience leading machine learning research and large teams focused on generative media across image, video, 3D, music and audio. He founded Magenta in 2015, an influential open-source effort exploring AI for art and music, and has made practical contributions to projects like pretty_midi (improving MIDI event handling and robustness) and Magenta’s MelodyRNN and notebook tooling. His work sits at the intersection of machine learning and HCI, marrying research on music perception and large audio datasets with tools that give artists creative agency. Doug has steered major efforts that produced T5, Imagen, Phenaki, Music Transformer and contributed strategically to PaLM, while also shipping product features for Google Play Music’s recommendation and discovery systems. A former faculty member at Université de Montréal and co-founder of the BRAMS music-neuroscience center, he holds a PhD in Computer Science and Cognitive Science and did a postdoc at IDSIA with Juergen Schmidhuber.
16 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 (12)
midi10
music-information-retrieval10
midi-instrument10
tensorflow210
machine-learning10
tensorflow10
python10
sorting-algorithm9
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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