Dr Leandro Soares Indrusiak
- Reader in Real-Time Systems, Department of Computer Science, The University of York
Real-Time Systems and Networks, On-Chip Multi and Many-Cores, Distributed Embedded Systems, High-Performance Computing and several types of resource allocation problems (in computing, manufacturing and transportation)
Leandro Soares Indrusiak is a faculty member of University of York's Computer Science department, and a member of the Real-Time Systems (RTS) research group. His current research topics include real-time systems and networks, on-chip multi and many-cores,
distributed embedded systems, high-performance computing, and several types of resource allocation problems in computing, manufacturing and transportation. He has published more than 150 peer-reviewed papers in the main international conferences and journals
covering those topics (nine of them received best paper awards). He is or has been a principal investigator in projects funded by EU, EPSRC, DFG, British Council and industry. He serves as his department's Internationalisation Advisor, and has held visiting
faculty positions in five different countries. He is a member of the EPSRC College, a member of the HiPEAC European Network of Excellence, a senior member of the IEEE, a member of the editorial board of ACM Transactions on Cyber-Physical Systems, and a member
of York's Sciences Faculty Board. He graduated in Electrical Engineering from the Federal University of Santa Maria (UFSM) in 1995, obtained a MSc in Computer Science from the Federal University of Rio Grande do Sul (UFRGS) in 1998, and was issued a binational
doctoral degree by UFRGS and Technische Universität Darmstadt in 2003. Prior to his appointment at York, he held a tenured assistant professorship at the Informatics department of the Catholic University of Rio Grande do Sul (PUCRS) (1998-2000) and worked
as a researcher at the Microelectronics Institute of TU Darmstadt (2001-2008).
Title: Cloud-based Evolutionary Optimisation of Cyber-Physical Systems
Abstract: Evolutionary optimisation is the application
of an evolutionary algorithm to iteratively uncover improved solutions to an optimisation problem. Such an algorithm is heuristic in nature, meaning that there is no guarantee that it will ever find an optimal solution, or that it will identify a solution
as optimal if it is found. Nonetheless, such approach is widely used in a variety of optimisation problems in science and engineering, where a sufficiently fit solution is acceptable despite of being suboptimal. In this talk, we will show how evolutionary
optimisation has been used to improve performance and energy-efficiency of different types of cyber-physical systems such as autonomous vehicles and smart factories. We will provide details on the research achievements that enabled optimisation, including
the creation of fast and accurate fitness functions and the distribution and orchestration of the evolutionary pipeline over cloud environments.