Daniel Abolafia
Research Scientist in AI
Summary
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RockstarDaniel Abolafia is a Research Scientist in AI with a decade of hands-on experience building machine learning systems and a strong autodidact streak. He has contributed to high-profile open-source projects like Magenta, where he implemented and refactored RNN/LSTM pipelines for music generation, including melody extraction from MIDI and modularized training and generation scripts. Comfortable bridging research and engineering, he focuses on reproducible model pipelines and scalable data processing. Known for turning experimental ideas into maintainable code, he blends curiosity-driven exploration with pragmatic software architecture. Based in the United States, he brings both creative and technical fluency to AI research and applied ML projects.
10 years of coding experience
Github Skills (11)
machine-learning10
rnn-model10
tensorflow10
n10
python10
data-processing9
midi-instrument9
midi9
music-information-retrieval8
architecture8
architectures8
Programming languages (3)
Github contributions (5)
magenta/magenta
Jun 2016 - Nov 2016
Magenta: Music and Art Generation with Machine Intelligence
Role in this project:
ML Engineer
ML EngineerContributions:22 commits, 35 PRs, 20 pushes in 5 months
Contributions summary:Daniel's contributions primarily revolve around implementing and refining a recurrent neural network (RNN) for music generation, as evidenced by the "A Recurrent Neural Network Music Generation Tutorial" commit. This includes developing code for melody extraction from MIDI files and training an LSTM model in TensorFlow. The user also refactored the code to align with a modular pipeline structure, demonstrating an understanding of software architecture and data processing techniques. Moreover, the user modified the generation scripts.
artmusic-generationgenerative-artmagentamachine-learning
danabo/magenta
Jul 2016 - Nov 2016
Contributions:55 commits, 118 pushes, 20 branches in 4 months
artmagentamachine-learningmusicmachine-intelligence
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