Bartosz Krawczyk

Bartosz Krawczyk is an Assistant Professor at the Chester F. Carlson Center for Imaging Science, part of the College of Science at Rochester Institute of Technology (RIT). He leads the MLVision Lab, focusing on continual learning, data streams, concept drift, class imbalance, and explainable AI. He holds a PhD in Computer Science from Wrocław University of Science and Technology (2015) and an M.Sc. (2012).

His research bridges theoretical advancements and real-world applications in medical imaging and remote sensing. Key contributions include the book Learning from Imbalanced Data Sets (Springer 2018), development of algorithms for robust continual learning, and frameworks for addressing data imbalance and bias in AI systems. He has pioneered techniques like DeepSMOTE and ROSE, addressing critical challenges in imbalanced data streams.

• Education: PhD/MSc in Computer Science from Wrocław University of Science and Technology
• Editorial Roles: Member of Applied Soft Computing editorial board, Guest Editor for 4 journal special issues
• Conference Roles: Senior PC member (KDD), Program Committee (AAAI, IJCAI, ECML-PKDD)

His work emphasizes algorithmic fairness, bias mitigation in underrepresented groups, and efficient learning in edge devices. Recent articles focus on adversarial drift detection, memory-efficient neural networks, and interpretable ML frameworks.

• Awards: IEEE Richard Merwin Scholarship, Amazon Machine Learning Award
• Teaching: Courses in computer vision, imaging science programming, and graduate thesis supervision

Current research explores active learning, semi-supervised methods for sparse data, and XAI explainability for imbalanced datasets. The MLVision lab actively collaborates on interdisciplinary projects spanning healthcare and environmental monitoring.