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About
I am a machine learning researcher. My research focuses on Continual Learning (also known as Continuous or Lifelong Learning). I am especially curious about advanced and unconventional deep learning models and derivative-free optimizers. In particular, I extended Hinton's Forward-Forward algorithm to large CNNs and am currently exploring how to adapt it to transformers. I am fascinated by how autonomous agents make decisions in the face of new or imperfect information. I also enjoy encountering intelligent artificial friends and foes in video games. I collaborated with ESA, LSA and CERN.
I recently completed my PhD in machine learning at the Polish Academy of Sciences, supervised by Prof. Paweł Morawiecki, and I am currently seeking postdoctoral and research positions.
The longer version — my research path and collaborations (click to expand)
During the summer of 2025, I was a researcher at the University of California, Santa Cruz, as part of the Summer of Reproducibility program, where I explored Kolmogorov–Arnold Network layers as structured replacements for feedforward blocks in small language models. Also in 2025, I joined a collaboration applying continual learning to data-quality monitoring for the CMS experiment at CERN. In 2024, I worked as a researcher at FDL Europe, where I adapted large-scale foundation models to work with SAR data in collaboration with the Luxembourg Space Agency and the European Space Agency. I was honored to receive two EU Horizon grants, TAILOR and ENFIELD, which allowed me to lead independent research projects on experience replay and parameter-efficient continual learning. The TAILOR grant brought me to the University of Pisa's Pervasive AI Lab, where I worked with Prof. Vincenzo Lomonaco.
I hold an MSc degree from Jagiellonian University, where I was co-supervised by Prof. Jacek Tabor and Dr. Stanisław Jastrzębski.
If you have any questions or are interested in collaborating, feel free to email me!
When it comes to coding, I prefer Python for its simplicity and the beauty of Zen. I strive for referential transparency and adhere to the single-responsibility principle with minimal side-effects, but only when it makes coding and reusing the code for other projects faster and cleaner. The spiritual side of my work is based on Unix philosophy.
The Distilled Manifesto: Academic freedom comes first.
I support the Slow Science Movement.
Visualizations
Optimization methods:
- Original Hinton's Forward-Forward Algorithm
Research interests
I have published research on the following topics:
- Continual learning: the topic of my doctoral dissertation
- Transfer, few- and zero-shot learning
- Representation learning
- Neural network optimization algorithms (Hebbian, Forward-Forward, SFF)
Work in progress:
- Reinforcement learning
- Meta-Learning
- Anomaly detection for continual learning
- AI in video games, especially agents that interact with the player (NPCs and companions). I am also interested in designing "wild" wildlife for games like No Man's Sky.
I would be excited to collaborate on projects involving:
- ML for magic tricks 🪄🎩
- Video and simple text games development (I like this PhD Simulator)
- Virtual (research) assistants, artificial scientists
- Space exploration
Of course, I have deep faith in the supreme power of evolutionary algorithms. Just check out this naturally occurring bacterial flagellar motor that spins up to 30,000 rpm with 80-100% efficiency and can reverse its rotational direction in as short as 2–5 milliseconds!
Credits: Singh, Prashant K et al. “CryoEM structures reveal how the bacterial flagellum rotates and switches direction.”
Різкими зламами, лихими закрутами
Продирається доля крізь хащі.
Хай груди заквітнуть червоними рутами –
Не втратити честі своєї нізащо!
Там, де з рідними-милими,
Там, де зі своїми хорошими,
Власним розумом, власними силами
Щастя своє збудувати зможемо!
Credits: @neivanmade, PhD