Scholar
Mika Mäntylä
Google Scholar ID: iD0POWUAAAAJ
Professor, University of Helsinki / University of Oulu, Finland
Citations & Impact
Citations
6,739
H-index
35
i10-index
87
Publications
20
Co-authors
62
list available
Publications
Resume (English only)
Academic Achievements
- - Publications in journals such as IEEE Transactions on Software Engineering, IEEE Software, Empirical Software Engineering, and Information and Software Technology.
- - Serves as an associate editor for IEEE Software and Empirical Software Engineering journals.
Research Experience
- - 2022/09-2026/09: Principal Investigator (PI) (with Davide Taibi) in Multimodal Fusion based Anomaly Detection for Improving Microservice-based System project (funded by Academy of Finland).
- - 2020/10-2023/09: Principal Investigator (PI) for WP4 (with Simo Hosio) in Critical project (funded by Strategic Research Council at the Academy of Finland).
- - 2020/01-2022/12: Principal Investigator (PI) for NLP-TD research project (funded by Academy of Finland).
- - 2017/10-2020/09: Responsible Leader of Testomat project (ITEA3/Tekes) at the University of Oulu.
- - 2016/09-2020/08: Principal Investigator (PI) for Auto-Time research project (funded by Academy of Finland).
- - Since 2015/01: Professor of Software Engineering at the University of Oulu, Finland.
- - 2014/08-2014/12: Assistant Professor in the CSE department at Aalto University, Finland.
- - 2012-2014: Senior Research Scientist in Software Engineering in the Software Process Research Group at Aalto University, Finland.
- - 2011-2012: Post-doc in the Software Engineering Research Group at Lund University, Sweden.
Education
- - Received D. Sc. degree in 2009 in Software Engineering from Aalto University, Finland.
Background
- - Research interests include end-of-lifecycle software engineering activities such as software testing, software maintenance, and software operations.
- - Interested in systematic multivocal literature reviews and the behavioral and psychological aspects of software engineering.
- - Mainly applies quantitative research methods from empirical software engineering and mining software repositories.
- - Uses natural language processing, classical machine learning, and deep learning to tackle software engineering problems.
Miscellany
- - Personal interests not mentioned.
