Welcome to my personal website, below you will find some of the things I've made and done:

Most recent news & events

Our paper titled "Generalized test utilities for long-tail performance in extreme multi-label classification" was accepted at NeurIPS 2023.
MLSS^S 2023 (Machine Learning Summer School on Applications in Science) that I co-organized took place at Jagiellonian University in Kraków.
New release of  ViZDoom (1.2.0) library with a new official  Gymnasium wrapper, support for ARM/Arch64 architectures including M1/M2 Macs and other improvments.
New version of  Vowpal Wabbit (9.7.0) was released with my improved implementation of PLT reduction.
Me and  ViZDoom project joined  The Farama Foundation.
ML in PL Conference 2022 that I co-organized took place in Warsaw.
I started an internship at  Yahoo! in Paris, France.
Our paper titled "On missing labels, long-tails and propensities in extreme multi-label classification" was accepted at KDD'22.
New release of  ViZDoom (1.1.12) library with a new official  OpenAI Gym wrapper.
I joined the management board of  ML in PL Association.
New release of  ViZDoom (1.1.11) library with new features, including access to the game's audio buffer.
ML in PL Conference 2021 that I co-organized took place virtualy.
Our paper "ViZDoom Competitions: Playing Doom From Pixels" received  2022 IEEE ToG Outstanding Paper Award.
Our short paper titled "Propensity-scored Probabilistic Label Trees" was accepted at SIGIR'21.
Our paper titled "Efficient Set-Valued Prediction in Multi-Class Classification" was accepted for publication in Data Mining and Knowledge Discovery.
Our paper titled "Online probabilistic label trees" was accepted at AISTATS 2021.
I gave a talk titled "Probabilistic Label Trees in Vowpal Wabbit" at Vowpal Wabbit Workshop at NeurIPS'20. [ video]
I gave a talk titled "Extreme classification: applications and algorithms" on GHOST Day: AMLC'20. [ video]




Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information.
[ www] [ code]


Extremely simple and fast library for extreme multi-class and multi-label classification.
[ code]


Extension of fastText library for multi-label classification including extreme cases with hundreds of thousands and millions of labels.
[ code]

ML in PL

I'm proud to be a member of the awesome ML in PL Association, a non-profit organization devoted to fostering the machine learning community in Poland and around the world.

From 2021 I'm a member of the management board of ML in PL Association and try to support other members of the association in the organization of great machine learning-related events, including taking care of the recruitment of new members of the association and leading the development of new websites for ML in PL Conference 2022, ML in PL Conference 2023 and MLSS^S 2023

Before, I co-organized ML in PL Conference 2019, ML in PL Virtual Event 2020, and ML in PL Conference 2021 serving as Call for Contributions (Talks and Posters) coordinator.

Reviewing scientific papers

I try to review at least a dozen of papers each year. So far I have served as a reviewer for:


The one that learns the most from the course is the teacher.
Wojciech Jaśkowski (my Eng. thesis supervisor)
As part of my PhD/being an assistant, I teached/teach a few courses at Poznan University of Technology (PUT) related to the topics of Machine Learning, Artificial Intelligence, Data Science and Big Data:
  • Advanced Methods of Computational Intelligence 2021, 2022 and 2023 (lectures + exercises)
    based on Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction and selected chapters from Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach.
  • Methods of Artificial and Computational Intelligence 2021 (exercises)
    based on selected chapters from Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach.
  • Big Data Processing 2019/2020 and Processing of Massive Datasets 2018/2019 (exercises)
    based on selected chapters from Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets.
  • Information Theory and Lossless Compression Methods 2018 (exercises)
    based on the first chapters from David J.C. MacKay, Information Theory, Inference, and Learning Algorithms.
    Note:  The course was awarded as the best new course in 2018 at the Institute of Computer Science.
If you are my student feel free to contact me via e-mail.