Factual state discovery benchmark: evaluating fact elicitation in Polish tax law
M Bystroński, K Tagowski, DP Janiak, J Farganus, ŁM Augustyniak, T Kajdanowicz, …
ACL 2026
Research Assistant Professor
Machine Learning enthusiast, a Ph.D. student with five years of experience in the field. Co-author of machine learning publications concerning graph representation learning field and natural language processing.
😊 Eager to deploy machine-learning techniques for real-world problems regarding textual, graph, or multimodal data.
👨💻 Libraries & Benchmarks Co-author of CLARIN-PL Embeddings library (https://github.com/CLARIN-PL/embeddings/) that provides various ready-to-use NLP pipelines. Co-author of LEPISZCZE benchmark (lepiszcze.ml) - A new, comprehensive open-source benchmark for Polish NLP with a continuous-submission leaderboard. NeurIPS 2022 Accepted Paper on Dataset and Benchmark Track
👨🏫 Teaching Instructor, creator of assignments and course materials, host of Kaggle InClass competitions at WUST (Wroclaw University of Science and Technology) laboratory and project courses.
➡️ Current projects Currently engaged in projects concerning the development of dialogue and Question-Answering (QA) systems for Polish and representation learning for dynamic graphs.
B.Eng. in Computer Science
Czestochowa University of Technology
M. Sc. in Computer Science
Wroclaw University of Science and Technology
PhD in Computer Science
Wroclaw University of Science and Technology
M Bystroński, K Tagowski, DP Janiak, J Farganus, ŁM Augustyniak, T Kajdanowicz, …
ACL 2026
K Tagowski, P Bielak, J Binkowski, T Kajdanowicz
ICCS 2023 352–364
P Bielak, K Tagowski, M Falkiewicz, T Kajdanowicz, N Chawla
Knowledge-Based Systems 236, 1–19 · Cited by 1
ŁM Augustyniak, K Tagowski, AK Sawczyn, DP Janiak, RD Bartusiak, A Szymczak, …, A Janz, P Szymański, T Kajdanowicz, M Piasecki, …
NeurIPS 2022 1–14
K Tagowski, P Bielak, T Kajdanowicz
Conference paper · Cited by 6
T Kajdanowicz, K Tagowski, M Falkiewicz, P Kazienko
Network intelligence meets user centered social media networks 133–142