Bartosz Krawczyk
Bartosz Krawczyk is an Assistant Professor in the Chester F. Carlson Center for Imaging Science at Rochester Institute of Technology, where he heads Machine Learning and Computer Vision (MLVision) Lab. He received the M.Sc. and Ph.D. degrees from the Wroclaw University of Science and Technology, Wroclaw, Poland, in 2012 and 2015, respectively.
Dr. Krawczyk has authored more than 60 journal articles and more than 100 contributions to conferences. He has coauthored the book Learning from Imbalanced Datasets (Springer, 2018). He is a Program Committee member for high-ranked conferences, such as KDD (Senior PC member), AAAI, IJCAI, ECML-PKDD, IEEE BigData, and IJCNN. He was a recipient of prestigious awards for his scientific achievements such as the IEEE Richard Merwin Scholarship, the IEEE Outstanding Leadership Award, and the Amazon Machine Learning Award, among others. He served as a Guest Editor for four journal special issues and as the Chair for 20 special session and workshops. He is the member of the editorial board for Applied Soft Computing (Elsevier).
Research interests:
- Machine Learning: class imbalance, ensemble learning, robust algorithms, big data
- Data Streams: concept drift, adaptive learning, active learning
- Deep Learning: continual & lifelong learning, adversarial learning, generative models, XAI
- Computer Vision: representation learning, video & tensor classification
- Imaging Science: remote sensing, medical image analysis
For more details on my research visit my Google Scholar, ResearchGate, and DBLP pages.
news
Mar 1, 2024 | Two new papers published in Machine Learning and IEEE Internet of Things |
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Oct 30, 2023 | I am included in the Stanford University list of TOP 2% of most cited researchers in AI field |
Jun 21, 2023 | Multiple fully-funded PhD positions available |
Jun 1, 2023 | I am included in the World’s Best Computer Science Scientists Ranking by Research.com! |
selected publications
- DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced DataIEEE Transactions on Neural Networks and Learning Systems, 2022
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- Adversarial concept drift detection under poisoning attacks for robust data stream miningMachine Learning, 2022
- ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streamsMachine Learning, 2022