Piotr Szymański
Assistant Professor at Kraina AI
Wrocław, Lower Silesian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolPiotr Szymański is an Assistant Professor and machine learning researcher with 13 years of experience building statistical models, teaching, and leading applied AI projects from Wrocław. He directs the Kraina.AI group and combines academic rigor—publications at ACL, NeurIPS, EMNLP, Interspeech and SIGSPATIAL—with product-focused work on speech and SLU at Avaya and Polish ASR/NLP tools in CLARIN-PL. A PhD in Machine Learning, he created the widely used scikit-multilearn library and implemented core multi-label algorithms (LP, RAkEL), reflecting deep expertise in label-space methods. Piotr’s work spans geospatial AI, speech–text joint representations, and optimization, and he brings a rare mix of open-source impact, cross-disciplinary collaboration, and local civic commitment to making Wrocław a better place to live.
13 years of coding experience
18 years of employment as a software developer
PhD Machine Learning, PhD Machine Learning at Wrocław University of Science and TechnologyStanford University
English, Polish, German, Russian, French
Stackoverflow
2,899reputation
472kreached
19answers
8questions
parallel-processing
top-5%
Github Skills (17)
multilabel-classification10
python10
scikit10
machine-learning10
scikit-learn10
data-structure9
algorithm9
algorithms9
parallel-processing9
data-structures9
pattern-recognition8
linear-algebra8
cluster-analysis6
neural-network6
tqdm6
Programming languages (16)
Github contributions (5)
scikit-multilearn/scikit-multilearn
Apr 2014 - Dec 2018
A scikit-learn based module for multi-label et. al. classification
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 releases, 439 commits, 48 PRs in 4 years 8 months
Contributions summary:Piotr's contributions focused on implementing and refining core machine learning algorithms within the scikit-multilearn library. Their work includes the implementation of Label Powerset (LP) and distinct Random k-Labelsets (RAkEL) algorithms, along with essential modifications to remove dependencies, such as NumPy, and clean up the codebase. They are also involved in fixing various minor issues and in general improving and modifying the existing structure. The changes suggest a strong understanding of both the theoretical underpinnings and practical implementation of multi-label classification techniques.
pythonk-meansdata-sciencepartitioningmulti-label-classification
scikit-multilearn/development-docker
Feb 2017 - May 2018
Contributions:12 commits, 6 pushes, 1 branch in 1 year 3 months
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