Specialized field

• Machine learning、statistics

Charge subject in this year

Charge subject in the past

Message

• My area of research is machine learning. I am particularly interested in the theory of model evaluation and selection and the reproducibility of computational experiments. Potential applications of my work can be found in various fields that use machine learning for data analysis and knowledge extraction from high-dimensional data. More information about my current research can be found on my personal website at www.berrar.com.
  
  I teach courses in machine learning, data science, and statistics. The ability to analyze data and extract meaningful insights is an immensely valuable skill that you can develop during your studies. In my courses, we will not only explore the theory but also emphasize practical applications -- working with real-world datasets and solving fascinating problems.

Researcher Information

• www.berrar.com

Degree

• University of Ulster, United Kingdom/PhD

Academic Background

• University of Ulster, UK
• 2004/11/University of Ulster, UK

Career

• 2017/04/2022/03/Tokyo Institute of Technology/School of Engineering/Specially Appointed Associate Professor
• 2022/04/2025/03/The Open University/School of Mathematics and Statistics/Lecturer in Statistics/Data Science
• 2025/04/Musashi University/School of Liberal Arts and Sciences/Professor

Academic Society and Social Activity

Social Activity

Committee Career

Reward

• 2012/06/Japanese Society for Artificial Intelligence/Best paper award
• 2023/11/Advance Higher Education UK/Senior Fellowship/SFHEA

Bibliography

• A Practical Approach to Microarray Data Analysis/Dubitzky W., Granzow M., and Berrar D./Coeditor/Springer/2002/1402072600
• Fundamentals of Data Mining in Genomics and Proteomics/Berrar D., Dubitzky W., and Granzow M./Coeditor/Springer/2007/978-0-387-47508-0

Academic Thesis

• Incorporating domain knowledge in machine learning for soccer outcome prediction/Berrar D., Lopes P., and Dubitzky, W./Joint Author/Machine Learning/2018/08
• A self-organizing incremental neural network for continual supervised learning/Wiwatcharakoses C. and Berrar D./Joint Author/Expert Systems with Applications/2021/12
• Using p-values for the comparison of classifiers over multiple data sets: pitfalls and alternatives/Berrar D./Sole Author/Data Mining and Knowledge Discovery/2022/04
• Estimating the replication probability of significant classification benchmark experiments/Berrar D./Sole Author/Journal of Machine Learning Research/2024/09
• A data- and knowledge-driven framework for developing machine learning models to predict soccer match outcomes/Berrar D., Lopes P., and Dubitzky, W./Joint Author/Machine Learning Journal/2024/09

Other Achievement

Conference

• Self-organizing incremental neural networks for continual supervised and unsupervised learning/Berrar D./8th International Conference on Innovation in Artificial Intelligence (ICIAI 2024)/2024/03/17/ICIAI

Works

Competitive research funds

Other

Example of Education Method

Created Textbooks

Special Instructions About Experienced Worker

Other