Benjamin Antin
PhD Candidate
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolBenjamin Antin is a PhD candidate and neuroscience-focused machine learning researcher with a decade of engineering experience and a strong foundation from Stanford in electrical and electronic engineering. Based in San Francisco and affiliated with Columbia’s Center for Theoretical Neuroscience, he bridges rigorous probabilistic modeling and practical tooling to make complex state-space and HMM methods more accessible. His open-source contributions include improving documentation, pedagogy, and visualizations for the lindermanlab/ssm repository, turning advanced Bayesian inference demos into clearer learning experiences. Known for combining theoretical depth with attention to usability, he works at the intersection of ML algorithms and neuroscience experiments to extract interpretable insights from neural data.
10 years of coding experience
Bachelor of Science (B.S.), Electrical and Electronics Engineering, Bachelor of Science (B.S.), Electrical and Electronics Engineering at Stanford University
English
Github Skills (8)
machine-learning10
jupyter-notebook10
state-space10
hmmlearn10
python9
data-visualisation9
data-visualization9
data-visualizations9
Programming languages (7)
Github contributions (5)
lindermanlab/ssm
Oct 2019 - Oct 2020
Bayesian learning and inference for state space models
Role in this project:
Data Scientist
Data ScientistContributions:176 commits, 33 PRs, 125 pushes in 1 year
Contributions summary:Benjamin primarily focused on enhancing the documentation and usability of the "Simple HMM Demo" notebook within the `ssm` repository. They added explanations and clarity to the notebook, refined the wording, and refactored code for better readability. Furthermore, the user incorporated exercises into the demo, enhancing the learning experience, and also introduced visualizations to illustrate state transition matrices and state duration histograms, increasing the user's understanding of the HMM's behavior.
state-space-modelsbayesian-inferenceinferencestate-spacejulia
bantin/jPCA
May 2020 - Oct 2020
jPCA for Neural Data Analysis in Python
Contributions:25 commits, 23 pushes, 2 branches in 4 months
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