Grzegorz Chrupała
Associate Professor
North Brabant, Netherlands
Summary
👤
Senior🎓
Top SchoolGrzegorz Chrupała is an Associate Professor with 16 years of experience researching computational language learning, specializing in how multimodal signals (speech and vision) and representations in multilayer recurrent neural networks support language acquisition. Based in North Brabant, he combines deep academic training (PhD in Computer Science) with practical ML development experience, including contributions to an RNN-based text analysis library where he improved training and model snapshotting. His career spans research and teaching roles across Europe, from postdoctoral work at Saarland University to lecturing in programming and formal languages, reflecting a rare blend of cognitive science, translation studies, and hands-on engineering. Known for analyzing emergent network representations, he brings both theoretical insight and reproducible tooling to questions about how machines learn language.
16 years of coding experience
12 years of employment as a software developer
MA, English, Spanish, MA, English, Spanish at University of Silesia in KatowiceMaster's, Translation and Localization, Master's, Translation and Localization at Universitat Rovira i VirgiliPhD, Computer Science, PhD, Computer Science at Dublin City UniversityDEA, Cognitive Science, DEA, Cognitive Science at Universitat de Barcelona
English, Polish, Spanish
Stackoverflow
3,073reputation
32kreached
12answers
16questions
Github Skills (17)
python10
data-science10
machine-learning10
rnn-model10
n10
text-analysis10
deep-learning10
theano9
compiler-optimization6
nlp6
optimization6
haskell-platform6
haskell6
clojure6
incanter6
Programming languages (6)
Github contributions (5)
IndicoDataSolutions/Passage
Feb 2015 - Feb 2015
A little library for text analysis with RNNs.
Role in this project:
Data Scientist
Data ScientistContributions:6 commits, 1 PR, 5 comments in 4 days
Contributions summary:Grzegorz primarily contributed to the development and maintenance of a text analysis library utilizing Recurrent Neural Networks (RNNs). Their work involved implementing and refining the `RNN.fit` method, including the addition of model snapshot saving functionality. Additionally, they modified the example usage file and introduced new features to the library. The contributions demonstrate a focus on training and evaluation of machine learning models for text processing.
nlpnatural-language-processingrnns
gchrupala/imaginet
Apr 2015 - Jun 2015
Contributions:33 commits, 23 pushes, 2 branches in 1 month
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