Chrysostomos
Symvoulidis
AI Researcher and Technical Manager with experience in designing and developing AI-driven solutions in healthcare, smart cities, and distributed systems. Worked on research and innovation projects involving Cloud and Edge Computing, Health Information Exchange, AI medical imaging, Federated Learning, and intelligent digital platforms. Experienced in technical leadership, system architecture, product management, and AI services, including LLM applications and chatbots. Actively involved in European and national R&D projects, proposal writing, and technical coordination, focused on translating research into high-impact solutions.
Education
University of Piraeus
- Dissertation: Causal and contextual information extraction and data enrichment methods with emphasis on artificial intelligence-driven optimization algorithms for cloud and edge computing.
- Supervisor: Prof. Dimosthenis Kyriazis
- Grade: Excellent (10/10)
University of Piraeus · MSc Programme: Digital Systems and Services
- Thesis: Complex Event Processing for real-time events identification.
- Supervisor: Prof. Dimosthenis Kyriazis
- Grade: Excellent (9.17/10)
University of Piraeus · Major in Electronic Services
- Final Year Project: Forecasting the Athens Stock Exchange General Index using Machine Learning algorithms.
- Grade: Very Good (6.53/10)
Causal and contextual information extraction and data enrichment methods with emphasis on artificial intelligence-driven optimization algorithms for cloud and edge computing.
Causal Discovery and Context Awareness are central subjects in research for many years. With its origins deeply rooted in scientific research, statistical inference, and philosophical questions, causality has thrilled researchers due to its key role in revealing the relationships between variables. Its significance lies in the pursuit of understanding of not just what happens, but why it happens, and thus making it vital in many scientific fields. Context awareness on the other hand, refers to the ability of a system to understand the context and what are the factors that affect the environment in which it is being used. That being said, Causal Discovery and Context Awareness are of crucial importance when it comes to the development of Artificial Intelligence systems that can be more accurate, robust and generalizable. This thesis focuses on the design and implementation of two Contextual and Causal Extraction methods which can be used for the enrichment of datasets towards the improvement of Machine Learning models. The first method identifies the most influential instances in a dataset and utilizes them in order to generate an Influence-based dataset. The second method discovers the causal relationships that may exist in a dataset and afterwards utilizes this information to generate causal features in order to incorporate this information to the initial dataset. In order to evaluate the methods' performance they have been applied in several, diverse frameworks and strategies used for the optimization of Cloud and Edge Computing environments. The results show that the methods can effectively extract contextual and causal information from datasets. Furthermore, the evaluation proves that the utilization of this information can significantly improve the performance of Machine Learning models. This thesis also provides potential directions for future research that could build upon the findings of the current thesis.
View full dissertationWork Experience
- Technical lead of the SEARCH federated storage and learning platform.
- Coordinate development activities, technical roadmaps, and documentation.
- Contribute to technical deliverables and project coordination.
- Lead the architecture and product management of smart city and healthcare platforms.
- Design and develop AI-driven services, including LLM applications, chatbots, and fleet profiling solutions.
- Manage feature prioritization, technical documentation, and development activities.
- Contribute to European and national R&D proposals and technical deliverables.
- Coordinated scientific and technical activities across project work packages.
- Monitored project progress and ensured alignment with timelines and objectives.
- Facilitated collaboration among partners and ensured deliverable quality.
- Developed orchestration and Complex Event Processing components in the MATILDA project.
- Designed 5G application modelling and service recommendation frameworks.
- Contributed to technical deliverables and research activities.
- Teaching / Lab Assistant: C Programming and Information Systems.
- Coordinated integration activities within the InteropEHRate project.
- Managed BYTE’s technical team and contributed to technical planning.
- Designed and implemented the Health Storage Cloud and data prefetching mechanisms.
- Contributed to technical deliverables and project reporting.
- Supported project management activities for the BigDataStack project.
- Contributed to technical deliverables and coordination activities.