Tom Pelsmaeker
Quantitative Analyst
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolTom Pelsmaeker is a quantitative analyst based in Amsterdam with 10 years of experience at the intersection of machine learning, NLP, and applied analytics. He holds an MSc in Artificial Intelligence and completed PhD-level research in low-resource machine translation and deep generative models, bringing strong research rigor to production problems. At Mathrix Group he applies quantitative methods to real-world datasets, building on prior roles that include accelerating neural training pipelines at Unbabel and developing GAN- and CV-based solutions during academic and startup internships. His background in psychology and co-founding a neurotech venture gives him a user-centered perspective uncommon among quantitative practitioners. Colleagues know him for translating complex ML research into pragmatic, high-impact engineering and for spotting efficiency gains—once achieving up to a 10x training speed-up. He combines academic depth with hands-on implementation experience across research labs, startups, and municipal projects.
10 years of coding experience
3 years of employment as a software developer
VWO Diploma, Natuur & Techniek, Natuur & Gezondheid, VWO Diploma, Natuur & Techniek, Natuur & Gezondheid at Veenlanden College MijdrechtMaster of Science (MSc), Artificial Intelligence, 8.9, Master of Science (MSc), Artificial Intelligence, 8.9 at Universiteit van Amsterdam
English, Dutch
Github Skills (7)
generative9
nlp6
pytorch5
deep-learning4
density-estimation4
variational-autoencoder4
variational-inference1
Programming languages (2)
Github contributions (5)
tom-pelsmaeker/deep-generative-lm
Apr 2019 - May 2020
Code accompanying the paper "Effective Estimation of Deep Generative Language Models".
Contributions:8 commits, 4 PRs, 12 pushes in 1 year
pytorchnlpdeep-learningeffectiveestimation
tom-pelsmaeker/tom-pelsmaeker.github.io
Apr 2019 - May 2020
Contributions:4 PRs, 30 pushes, 2 branches in 1 year 1 month
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