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Research areas: machine learning, explainable ML, anomaly detection
Position: Research Consultant
Jakub focuses on information security, software engineering, machine learning, stream data processing, anomaly detection and explainable machine learning models. He holds a PhD in Intelligent Information Systems from the Slovak University of Technology.
Jakub has taught several machine learning-related and software engineering-related courses. He has participated in national research projects. Experimentation is Jakub’s favorite way to move forward: in building models, in prototyping new approaches and in life in general.
All publications: see google scholar profile
Selected Student Supervising
- Karolína Giertlová – Interpretation of machine learning models using vector data representations – ongoing
- Tomáš Mizera – Interpretation of Text Processing Machine Learning Models. Defended 2021
- Patrik Židuliak – Parallel processing of multidimensional data. Defended 2020.
- Branislav Pecher – Interpretability of neural network models used in data analysis. Defended 2019.
- Jakub Janeček – Interpretability of machine learning models created by clustering algorithms. Defended 2019.
- Imrich Nagy – Processing sequences of transactions using deep learning models. Defended 2019.
- Martin Mocko – Anomaly detection in transaction data. Defended 2018.
- Peter Zajac – Incremental detection of anomalies. Defended 2018.
- Lukáš Skala – Histogram based incremental anomaly detection. Defended 2018.
- Matúš Cimerman – Stream analysis of incoming events using different data analysis methods. Defended 2017.
- Martin Cibula – Query evaluation over incoming data stream. Defended 2016.
- Denis Laca – Privacy preservation in machine learning using simulation. Defended 2019.
- Jakub Janeček – Executable data analysis tutorials. Defended 2017.
- Martin Mocko – Web portal usage trend discovery. Defended 2016.
- Matúš Cimerman – Data stream analysis. Defended 2015.