About

CAMIL is a center of excellence in Artificial Intelligence and Machine Learning, gathering researchers and practitioners with comprehensive experience, ranging from knowledge discovery to semantic web to computer vision. CAMIL members have made internationally recognized contributions in fundamental research in AI and ML, and applied their expertise in numerous practical contexts, including medicine, biology, finance, and industry. CAMIL’s mission is to draw together the competences of its members and serve as a hub for channeling their combined activities. CAMIL is a member of Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) and the TAILOR network – Foundations of Trustworthy AI – Integrating Learning, Optimization and Reasoning.

Read full mission statement
CAMIL members participated in the following

Projects

Automatic Ligand Recognition
Applications / Structural Biology
Big Data Semantics
Big Data
Probabilistic Classifier Chains
Multilabel classification / Theory
Priority Attachment
Social Networks
Retinal Blood Vessel Segmentation
Applications / Medical Imaging
Measure Visualizations
Theory / Visualization
Our members' selected

Publications

  1. Brzezinski D, Stefanowski J (2014)
    Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
    IEEE Transactions on Neural Networks and Learning Systems 25(1): 81-94
  2. Dembczyński K, Waegeman W, Jachnik A, Cheng W, Hüllermeier E (2014)
    On the Bayes-optimality of F-measure maximizers
    Journal of Machine Learning Research 15: 3333-3388
  3. Dembczyński, Kotłowski W, Koyejo O, Natarajan N (2017)
    Consistency Analysis for Binary Classification Revisited
    ICML 2017: 961-969
  4. Keet CM, Ławrynowicz A, d’Amato C, Kalousis A, Nguyen P, Palma R, Stevens R, Hilario M (2015)
    The Data Mining OPtimization Ontology
    Journal of Web Semantics 32: 43-53
  5. Kotlowski W, Koolen WM, Malek A (2017)
    Random permutation online isotonic regression
    NIPS 2017: 4180-4189
  6. Kotłowski W, Dembczyński K (2017)
    Surrogate regret bounds for generalized classification performance metrics
    Machine Learning Journal 106: 549–572
  7. Kowiel M, Brzezinski D, Porebski PJ, Shabalin IG, Jaskolski M, Minor W (2018)
    Automatic Recognition of Ligands in Electron Density by Machine Learning
    Bioinformatics
  8. Krawczyk B, Minku L, Gama J, Stefanowski J, Woźniak M (2017)
    Ensemble learning for data stream analysis: A survey
    Information Fusion 37: 132-156
  9. Krawiec K, Błądek I, Swan J, Drake JH (2018)
    Counterexample-Driven Genetic Programming: Stochastic Synthesis of Provably Correct Programs
    IJCAI 2018: 5304-5308
  10. Liskowski P, Krawiec K (2016)
    Segmenting Retinal Blood Vessels with Deep Neural Networks
    IEEE Transactions on Medical Imaging 99: 2369-2380
  11. Ławrynowicz A, Potoniec J, Robaczyk M, Tudorache T (2018)
    Discovery of emerging design patterns in ontologies using tree mining
    Semantic Web 9(4): 517-544
  12. Wasik S, Antczak M, Badura J, Laskowski A, Sternal T (2018)
    A survey on online judge systems and their applications
    ACM Computing Surveys 51(1)
  13. Wrembel R et al. (2018)
    Big Data Semantics
    Journal on Data Semantics 7(2): 65-85

In Numbers

956
Publications
383
Publications with >5 Citations
14,803
Citations
15.48
Avg. Citations per Paper

People

Jerzy Stefanowski
Professor
Krzysztof Krawiec
Professor
Robert Wrembel
Professor
Mikołaj Morzy
Professor
Agnieszka Ławrynowicz
Assistant Proffesor
Krzysztof Dembczyński
Assistant Professor & SENIOR RESEARCH SCIENTIST, YAHOO RESEARCH, NYC
Wojciech Kotłowski
Assistant Professor
Szymon Wilk
Assistant Professor
Dariusz Brzeziński
Assistant Professor
Jędrzej Potoniec
Assistant Professor

Alumni

Szymon Wąsik

Software Engineer, Google, Warsaw