Marios Fokaefs is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering, where he directs the EASE (Economics and Administration of Software Engineering) lab. His expertise revolves around Software Engineering and more specifically on software evolution and DevOps. His research focuses on Software Engineering Economics, Software Performance Engineering, Cloud Computing and Self-Adaptive Systems among others. Professor Fokaefs holds a BSc in Computer Science from University of Macedonia, Greece, and a MSc and PhD on Software Engineering from the University of Alberta. Before joining York University, he was an Assistant Professor in the Department of Computer and Software Engineering at Polytechnique Montreal. He has long-standing partnerships with various companies, most notably IBM Canada, while his work is also funded by NSERC, Mitacs and the Wellcome Trust.
- Software design and architecture.
- Design patterns, Architectural styles, Reengineering, Software Modeling
- Software Evolution
- Versioning, Code/Model comparison, Migration
- Cloud Computing
- Scalability, Containers, Microservices
- Software Performance Engineering
- Performance Modeling, Self-adaptive systems
- DevOps, BizDevOps, DevSecOps, MLOps, AIOps
- Software Engineering for AI
Positions and Opportunities for Graduate Students and Postdoctoral Fellows
The following projects have open research positions. Please follow the link and the instructions to apply.
- An intelligent and dynamic planner for adaptive migration of big data systems
- Improving reliability efficiency through log mining and multi-objective optimizations
- A model-driven software development platform for Climate-Sensitive Infectious Disease Modelling
- BizDevOps: Integration of business and development operations for the economic sustainability of digital businesses
- Exploring the applicability of Generative AI and LLM on Software Architecture
- Exploring the applicability of Generative AI and LLM on Software Performance and Self-Adaptive Systems
- Adaptive and Optimal MLOps processes for restricted environments