Research
Research projects, collaborations, and grants connected to applied artificial intelligence, data analysis, intelligent systems, and energy technologies.
My research focuses on the intersection of machine learning, intelligent systems, and data-driven analysis, with particular emphasis on renewable energy systems, computer vision, and educational technologies.
Current work explores generative and predictive deep learning approaches for photovoltaic systems, electricity forecasting, synthetic time-series data generation, smart home automation, and intelligent software systems.
Deep learning, Generative adversarial networks (GANs), Synthetic data generation, Time-series forecasting, Predictive analytics, Neural networks
Photovoltaic power forecasting, Energy consumption prediction, Renewable energy analytics, PV performance analysis, Smart monitoring systems
Object detection, Image processing, Feature extraction, Visual analytics
Intelligent IoT, Edge Computing, Data monitoring, Smart Buildings, Home Automation, AI-assisted Automation, Embedded Intelligence
Automated programming assessment, Information systems, Intelligent feedback systems
I am a part of a research group, whose main goal is to develop methods for optimizing hybrid renewable energy systems in the residential sector.
Research projects, collaborations, and grants connected to applied artificial intelligence, data analysis, intelligent systems, and energy technologies.
Participation as a researcher in the Ruse Research University initiative, funded by the European Union under the NextGenerationEU programme. The project supports the development of research capacity, innovation, and scientific excellence.
This research project explores the application of artificial intelligence across various professional domains, examining opportunities for innovation, implementation challenges, and ethical concerns related to responsible AI adoption.
AIGUIDE is an Erasmus+ KA2 project focused on the use of artificial intelligence for green understanding, insight generation, and decision empowerment. The project supports international collaboration in higher education and promotes AI-driven approaches for sustainability, environmental awareness, and informed decision-making.
The programme addresses societal challenges in livestock farming by supporting the development of new knowledge, innovative technologies, and modern practices to improve sustainability and competitiveness in the Bulgarian livestock sector.
This project investigates methods and technologies for the automated analysis, processing, storage, and management of multimedia data and digital documents, with emphasis on intelligent information extraction and organization.
BENEFIT is an Erasmus+ project aimed at strengthening education and research capacity in precision agriculture through innovation, collaboration, and knowledge exchange.
The project focuses on the development of computational models and simulation methods across different domains of application, supporting research and analysis of complex systems.
The project investigates approaches for energy-efficient control and optimization of street lighting systems, aiming to improve sustainability and reduce energy consumption.
The project investigates and optimizes a hybrid renewable energy system for supplying a livestock farm. The research focuses on modeling, analysis, and optimization of renewable energy sources in agricultural conditions, aiming to improve energy efficiency, reliability, and sustainability in livestock production.
The project explores methods and algorithms for big data analysis across different application domains, supporting data-driven decision-making and intelligent analysis.
This project investigates intelligent computer systems, focusing on their development, practical applications, and effective management strategies.
The project focuses on technologies for multimedia content retrieval, document analysis, and automated processing of large structured and unstructured data collections.
The project investigates integrated environments for efficient collection, organization, and processing of large-scale datasets to support research and analytical activities.
This research project investigates methods for collecting, organizing, accessing, processing, and analyzing large-scale structured and unstructured datasets.
The DIGD project aims to establish a Competence Centre for the Digitalization of the Economy in a Big Data Environment. The centre provides distributed computational resources and cloud infrastructure to support scientific research, collaboration, and advanced data-driven studies across Bulgaria and internationally.