Tal Linzen - Associate Professor

Tal Linzen

Associate Professor

New York, New York, United States

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Summary

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Tal Linzen is an associate professor at New York University who blends behavioral experiments with computational modeling to understand how people learn language. With 15 years in linguistics and cognitive science, he has held research roles at Google and academic positions at Johns Hopkins University and NYU, illustrating a strong track record across industry and academia. His work spans probabilistic models of language comprehension and acquisition, linking parsing, learning rules, and expression choices, and he applies these insights to neural and behavioral experiments. He also contributes to the broader research software ecosystem, notably improving the documentation and usability of the MNE-python library (MEG/EEG in Python), demonstrating a commitment to reproducible, accessible science. Based in New York, Tal completed his PhD in Linguistics at NYU and holds a BSc in Mathematics and Linguistics from Tel Aviv University. He brings a rare combination of theoretical rigor, experimental savvy, and practical software communication to interdisciplinary teams.

16 years of coding experience

14 years of employment as a software developer

Bachelor of Science (B.Sc.), Mathematics and Linguistics, Bachelor of Science (B.Sc.), Mathematics and Linguistics at Tel Aviv University

PhD, Linguistics, PhD, Linguistics at NYU

MLSS

English, Hebrew, Spanish

Stackoverflow

Stats

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0answers

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Github Skills (8)

documentations10

python10

neuroimaging10

documentation10

el9

ceph9

ec9

Programming languages (3)

HTMLJupyter NotebookPython

Github contributions (5)

mne-tools/mne-python

Nov 2013 - Feb 2014

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Role in this project:

Technical Writer

Contributions:44 commits, 3 comments, 1 issue in 3 months

Contributions summary:Tal's contributions primarily focused on improving the documentation within the MNE-python repository. Their commits involved refining docstrings, adding code examples with the correct formatting, and clarifying procedures, especially regarding downsampled data and the use of trial IDs. These changes aimed to provide more clarity and guidance for users, enhancing the usability of the MNE-python library. The user also expanded the docstring of drop_log and refined other documentation throughout the code.

visualizationneuroimagingneurosciencepythonstatistics

TalLinzen/tallinzen.github.io

Sep 2021 - Apr 2025

Contributions:122 pushes, 1 branch in 3 years 7 months

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