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Prof. Johan Lilius is full professor of Embedded Systems at Åbo Akademi since 2001.  He is the leader of the Embedded Systems Laboratory, which has strong international and industrial links and he was a member of the research group "Formal Methods in Programming" that was awarded the status of Centre of Excellency by the Academy of Finland.  He has won the Ten-Year Most Influential Paper Award at the ACM/IEEE Conference on Model Driven Engineering Languages and Systems, and a number of Best Paper awrds. He has experience in leading large academic organisations, he has been Head of Turku Center for Computer Science, and Head of Department in two periods. During his leadership he particpated in major reorgansiations of the University. He is experienced in leading PhD reserach, he has been head of the TUCS graduate programme, and currently he is head of the DITM doctoral Network.  He is member of the Editorial board of "Transactions on Petri Nets and Other Models of Concurrency" and  a regular member of Program Committees of the relevant conferences in the Embedded systems area. He has co-organised and co-chaierd several workshops, summer schools and conferences. He has completed the supervision of 6 doctoral students, and is curretly supervising 5 students. He has published 140 research papers. His research interests include Energy-efficient Software, Programming Models for Multi-cores, System-Level Design, Performance Engineering and Models of Concurrency. He is in charge of the Embedded Systems Curriculum, the Embedded Computing Masters Programme and coordinates a number of double degree programmes. 

Research interests

Autonomous Navigation algorithms, Embedded Deep Neural Computing, Energy-efficient Software.

External positions

Visiting Professor, Institut National des Sciences Appliquées de Rennes

Oct 2016

Director, Turku Centre for Computer Science

Jan 2011Dec 2014

Northeast Normal University

2009 → …


  • QA76 Computer software
  • Energy Efficient Software
  • Autonomous Navigation Algorithms
  • Verification and Validation
  • Embedded Deep Neural Computing


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