Thomas Van Dongen
Head Of AI Engineering at Minish
Groningen, Groningen, Netherlands
Summary
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Rockstar🎓
Top SchoolThomas Van Dongen is Head of AI Engineering at Springer Nature with 11 years of experience building ML-driven products for the scientific domain, blending production-focused engineering with NLP research. He co-founded The Minish Lab to advance efficient, eco-friendly NLP tooling and maintains active open-source work (github.com/Pringled) on lightweight embeddings, nearest-neighbor search, and semantic deduplication. Previously he led recommender systems and ML teams at Slimmer AI and Springer Nature Group, and earlier co-founded Gronalytica to automate exam grading in pharma. With a master’s in Artificial Intelligence from the University of Groningen, he combines academic rigor with startup pragmatism and a clear focus on scalable, sustainable ML. An understated strength is shipping compact, high-performance models that lower inference cost while preserving SOTA quality.
11 years of coding experience
7 years of employment as a software developer
Praedinius GymnasiumPre-master, Artificial Intelligence, Pre-master, Artificial Intelligence at Rijksuniversiteit GroningenMaster's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at University of Groningen
Stackoverflow
29reputation
2kreached
0answers
1question
Github Skills (125)
similarity-search10
multilabel-classification10
benchmark10
annoy10
neural-search10
sentence10
python10
nearest-neighbors10
semantic-search10
datasets10
bug-reporting10
embedding10
clustering10
multilingual10
code-search10
Programming languages (9)
Github contributions (5)
MinishLab/vicinity
Nov 2024 - May 2026
Lightweight Nearest Neighbors with Flexible Backends
Contributions:9 releases, 18 reviews, 69 PRs in 1 year 6 months
nearest-neighbor-searchnearest-neighborsembeddingsvector-databaseai
MinishLab/model2vec-rs
May 2025 - May 2026
Official Rust Implementation of Model2Vec
Contributions:1 release, 2 reviews, 38 PRs in 1 year
rustembeddingsmodel2vecnlpword-embeddings
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