Ali Basirat
Associate Professor
Copenhagen, Capital Region of Denmark
Summary
👤
Senior🎓
Top SchoolAli Basirat is an Associate Professor in Natural Language Processing and Computational Linguistics based in Copenhagen with nine years of research and teaching experience spanning syntactic parsing, language modeling, and representation learning. He progressed from PhD work at Uppsala to postdoctoral and lecturer roles before securing a tenure-track assistant professorship and promotion to associate professor at the University of Copenhagen, blending deep theoretical expertise with hands-on model development. His work focuses on bridging formal grammar insights and modern representation learning, producing practical resources such as grammar-mapping frameworks and treebanks earlier in his career. Comfortable both in academia and classroom settings, he has taught core computing subjects from databases to AI, reflecting a broad technical foundation beyond pure NLP.
8 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at Islamic Azad University,Science And Research BranchDoctor of Philosophy - PhD, Computational Linguistics, Doctor of Philosophy - PhD, Computational Linguistics at Uppsala University
English, Persian, Arabic, Swedish, Danish
Github Skills (16)
principal-component-analysis8
phonetics7
glove7
acl7
word2vec6
computational-linguistics6
embedding6
natural-language-processing5
sentence-similarity5
nlp5
word-embeddings4
attention-mechanism4
relation-extraction4
dependency-parsing3
named-entity-recognition2
Programming languages (2)
Github contributions (5)
abasirat/principal_word_vectors
Mar 2019 - Sep 2019
We use principal component analysis for word embedding. The method is able to process both annotated and raw corpora.
Contributions:67 commits, 62 pushes, 1 branch in 6 months
nlpcorporaprincipal-component-analysisword-embeddingsmethod
abasirat/llm-adapter
Feb 2024 - Mar 2025
A plug-and-play adapter architecture that efficiently adapts large language models to downstream tasks. Essential for client-server architecture language model services. A practical approach to adapting LLMs' hidden activations without modifying the base model.
Contributions:1 PR, 38 pushes, 2 branches in 1 year
attention-mechanismdependency-parsingllmllm-trainingnamed-entity-recognition
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