David Bourgin
Senior Machine Learning Scientist at Adobe
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolDavid Bourgin is a Senior Machine Learning Scientist in New York with a decade of experience building personalized generative models and human-in-the-loop tools for creative workflows. His background blends rigorous cognitive science research (PhD, UC Berkeley) with product-focused ML at Adobe and Spotify, where he translated models of human decision-making into practical recommendation and predictive systems. He has a track record of improving user-facing metrics through cognitively informed algorithms—e.g., a recommendation system and state-of-the-art choice prediction models from his doctoral work. Beyond research, he contributes full-stack improvements to open-source tools such as the Gentle forced aligner, adding UI controls and backend handling for disfluencies to better support transcription workflows. David’s interdisciplinary mix of cognitive modeling and production ML makes him adept at turning insights about human behavior into scalable, creative AI features.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Cognitive Science, Doctor of Philosophy - PhD, Cognitive Science at University of California, BerkeleyBachelor of Arts (B.A.), Biology, Psychology, Bachelor of Arts (B.A.), Biology, Psychology at Saint Mary's College of Maryland
Github Skills (7)
python10
language-modeling10
back-end-development9
web-development9
front-end-development8
html6
javascript6
Programming languages (2)
Github contributions (5)
strob/gentle
Jul 2016 - Jul 2016
gentle forced aligner
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:5 commits, 1 PR in 1 day
Contributions summary:David contributed to the development of the "gentle forced aligner," focusing on both front-end and back-end aspects. They added UI elements such as checkboxes and modified the display of disfluencies. They also modified back-end files relating to language models, including features for handling disfluencies during transcription and alignment.
alignergentle
ddbourgin/numpy-ml
Mar 2019 - Jan 2022
Machine learning, in numpy
Contributions:5 reviews, 271 commits, 25 PRs in 2 years 10 months
machine-learningnumpyneural-networkstopic-modelinggaussian-mixture-models
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