Pawel Ksieniewicz

assistant professor

University of Wrocław

Poland

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Dr. Pawel Ksieniewicz is an assistant professor at Wrocław University of Technology in Poland. His research areas encompass data stream evaluation, open set recognition, and explainable AI, with a focus on applications such as fake news detection and content credibility assessment. He has contributed to the development of frameworks and methodologies for machine learning, including work on neural network architectures and ensemble classification techniques.

Pawel Ksieniewicz

Research Interests

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Articles (20)

Structuring the Processing Frameworks for Data Stream Evaluation and Application

Taking class imbalance into account in open set recognition evaluation

Towards explainable fake news detection and automated content credibility assessment: Polish internet and digital media use-case

How to break big tech’s stranglehold on AI in academia

Distance profile layer for binary classification and density estimation

torchosr — A PyTorch extension package for Open Set Recognition models evaluation in Python

Certainty-Based Neural Network Architecture Selection Framework for TinyML Systems

Neural network architecture with intermediate distribution-driven layer for classification of multidimensional data with low class separability

Active Weighted Aging Ensemble for drifted data stream classification

Alphabet Flatting as a variant of n-gram feature extraction method in ensemble classification of fake news

Hybrid Regression Model for Link Dimensioning in Spectrally-Spatially Flexible Optical Networks

Efficient dynamic routing in Spectrally-Spatially Flexible Optical Networks based on traffic categorization and supervised learning methods

Processing data stream with chunk-similarity model selection

Statistical Drift Detection Ensemble for batch processing of data streams

Stream-learn — open-source Python library for difficult data stream batch analysis

Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

Fusion of linear base classifiers in geometric space

Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method

How to design the fair experimental classifier evaluation

Application of imbalanced data classification quality metrics as weighting methods of the ensemble data stream classification algorithms

Collaborators (7)

Róża Goścień

Szymon Wojciechowski

Paweł Zyblewski

Marta Gackowska-Kątek

Krzysztof Walkowiak

Gracjan Kątek

Robert Burduk

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