Bartosz Krawczyk is an Assistant Professor at the Chester F. Carlson Center for Imaging Science, where he heads the Machine Learning and Computer Vision (MLVision) Lab. His current research interests include continual learning, data streams, concept drift, class imbalance, ensemble learning, and XAI. 

He obtained his M.Sc. and Ph.D. degrees in Computer Science from Wroclaw University of Science and Technology, Poland, in 2012 and 2015 respectively. Dr. Krawczyk coauthored the book Learning from Imbalanced Data Sets (Springer 2018). He was a recipient of prestigious awards for his scientific achievements such as IEEE Richard Merwin Scholarship, IEEE Outstanding Leadership Award, and Amazon Machine Learning Award. He served as a Guest Editor for four journal special issues and as a Chair for twenty special session and workshops. Dr. Krawczyk is Program Committee member for high-ranked conferences, such as KDD (Senior PC member), AAAI, IJCAI, ECML-PKDD, ECAI, PAKDD, and IEEE BigData. He is the member of the editorial board for Applied Soft Computing (Elsevier).

Dr. Krawczyk’s team is working on novel ML algorithms designed for holistic continual learning from evolving data streams. These algorithms address the challenges of robustness to catastrophic forgetting and the accumulation of knowledge over time, while also ensuring adaptability to concept drift and data shift phenomena through proactive memory revisitation and relevant past information updating. Another vital part of Dr. Krawczyk’s research portfolio lies in the critical area of data imbalance and fairness, where he and his team are at the forefront of devising strategies to mitigate bias inherent in both data and algorithms. This research holds profound implications across numerous domains, particularly in contexts involving underrepresented groups and sensitive information, where biased decision-making processes can have significant ramifications. Dr. Krawczyk has co-authored “Learning from Imbalanced Datasets” (Springer, 2018), a seminal monograph in this field. Furthermore, the MLVision team explores methodologies for handling sparse access to data, a common challenge in real-world scenarios characterized by limited ground truth or training examples. Dr. Krawczyk focuses on the development of active and semi-supervised learning algorithms, as well as meta-models for few/one/zero-shot learning, to accommodate these constraints effectively. Beyond core ML/CV research, Dr. Krawczyk's team applies their algorithms to solve practical challenges, particularly in the domains of medical image analysis and remote sensing. Through their interdisciplinary approach, they seek to translate theoretical innovations into tangible solutions with real-world impact.