Nevyana Krasteva is a Professor of Marketing and International Business at Sofia University St. Kliment Ohridski and the International Business School, Botevgrad. She is a member of the American Marketing Association and European Management Council, with research spanning diverse marketing contexts including political environments, conflict zones, and cultural practices. Research Interests : Digital marketing, artificial intelligence applications, consumer behavior, augmented reality, tourism economics, and sustainable agricultural marketing. Publications : Focus on interdisciplinary topics like marketing philosophy, gamified education, and global consumer trends. Specializations : Has specialized in marketing practices across Russia, Austria, Greece, Denmark, Japan, Israel, and Hungary. Professional Memberships : American Marketing Association European Management Council
Assoc. Prof. Boryana Pelova is an Associate Professor at the Department of Statistics and Econometrics, Faculty of Economics and Business Administration (FEBA), Sofia University. She holds a PhD and has over 15 years of expertise in data analysis, large dataset processing, and machine learning applications. Her work addresses societal challenges such as air quality improvement, mental health prevention, and digitalization of public sectors and SMEs. She currently leads research projects including 'Digitalization of SMEs' (2023–present), 'SHARE-ERIC Bulgaria' (2018–present), and a National Science Fund project (2023–present). Prof. Pelova serves as the Academic Director of the MSc Program 'AI for Business and Finance' and pioneered an industry-funded PhD program. She previously led the Career Center at FEBA (2016–2023), organizing career development initiatives for students. As former Vice-Dean of FEBA (2019–2023), she fostered academic-business collaboration to enhance tech-driven education. She also coordinates initiatives linking vocational schools with universities, such as the affiliation of the Burgas Vocational School of Computer Programming and Innovation with Sofia University. Her research focuses on applying data science to real-world problems, including financial stability and environmental sustainability. While no specific scientific awards are listed, her contributions to academic-industry synergy and educational innovation highlight her leadership in advancing tech-driven education and societal impact. Prof. Pelova collaborates with institutions such as the ESG Lab and Center for Education Services at FEBA, driving interdisciplinary projects like the Twin4Eco and ConsuWAIS initiatives. Her work emphasizes bridging academic research with practical solutions for businesses and public sectors.
Stanimir Yordanov Yordanov is an Associate Professor at the Department of Automation, Information and Control Technology within the Faculty of Electrical Engineering and Electronics at Technical University - Gabrovo. With a Doctorate in Technical Sciences and over 30 years of professional experience since 1993, he has established himself as a leading researcher and educator in control systems and automation. His educational background includes a Master of Engineering from VMEI - Gabrovo (1992) with specializations in Computer Engineering, Management Technologies, and Pedagogy, followed by a Doctorate (2006) and Associate Professor qualification (2010) in specialized technical fields. His teaching portfolio encompasses System Programming, Operating Systems, Digital Control Systems, and Industrial Robotics, among others. Professor Yordanov's research focuses on automated control systems, intelligent management of technological processes, industrial system monitoring, and object/system modeling. His work demonstrates a consistent trajectory toward increasingly sophisticated control algorithms and applications across diverse domains from electrohydraulic systems to environmental monitoring. The recent publications reveal a strong emphasis on advanced control techniques including neuro-PID regulators, model predictive control, and applications of artificial intelligence in industrial contexts. His extensive project portfolio includes 22 significant research initiatives, ranging from national projects like the 'Competence Center for Intelligent Mechatronic Systems' to international collaborations such as the 'MechMate' project focused on European SME growth. These projects demonstrate his ability to secure funding and lead research teams across various technical domains. Professor Yordanov has mentored six PhD students to completion, with several successfully defending dissertations on topics including intelligent energy systems, embedded real-time operating systems, and robotic systems. His academic leadership extends to serving as an academic mentor for over 600 student internships. His laboratory work centers on electrohydraulic control systems, robotic manipulation, and intelligent monitoring applications, with recent projects developing smart dispensers, beehive monitoring systems, and low-cost health monitoring devices for pregnant women, demonstrating practical applications of his theoretical research.
Yavor Ivanov Dankov is an Assistant Professor in the Department of Software Technologies at the Faculty of Mathematics and Informatics, Sofia University 'St. Kliment Ohridski'. His academic career focuses on the intersection of educational technology, software engineering, and cultural heritage preservation. Dr. Dankov holds a PhD and has established himself as a specialist in educational video games, particularly those designed for cultural heritage education and climate resilience. His research interests center around user-centered design of educational video games, data visualization for learning analytics, and the development of instruments supporting game designers in creating effective educational experiences. Dr. Dankov's work bridges the gap between theoretical educational frameworks and practical software implementations, with strong emphasis on adaptive learning systems and personalized educational experiences. His research demonstrates how educational video games can be effectively leveraged for cultural heritage preservation and climate resilience education, particularly through the innovative DIZU-EVG (Data visualIZation instrUment for Educational Video Games) platform he has developed. Analysis of Dr. Dankov's recent publications reveals a consistent focus on the design, development, and evaluation of educational video games, with particular attention to cultural heritage applications. His work shows a progression from theoretical frameworks to practical implementations, with increasing sophistication in data visualization and learning analytics components. The research demonstrates strong interdisciplinary connections between computer science, education, and cultural heritage studies, with growing emphasis on practical tools that support both educators and game designers. Dr. Dankov has been actively involved in numerous research projects including the DIZU-EVG tool development (2024), MOTIVATE project on intelligent tools for adaptive educational games (2024), and various European Commission-funded initiatives such as UNITE and E-CREHA. His grant portfolio demonstrates significant expertise in securing competitive research funding at national and international levels. His work is organized around several key research groups focusing on educational video game design, data visualization for learning analytics, and cultural heritage applications. These teams collaborate across disciplines to develop innovative approaches to educational technology that combine software engineering excellence with pedagogical effectiveness.
Milen Chipchanov is an Assistant Professor at New Bulgarian University (NBU) in the Department of Foreign Languages and Cultures. He has over 35 years of teaching experience in French language education, focusing on courses such as written and oral translation, phonetics, and the integration of multimedia and new technologies in foreign language instruction.
Prof. Plamen Tsvetkov is a faculty member at New Bulgarian University (NBU) in the Department of Telecommunications since 2013. He previously held roles at Technical University of Sofia (1986–2019), including Head of the Department of Electrical Metrology (2004–2019). His academic career spans over 35 years in teaching and research. Education : PhD in Electrical Metrology from Technical University of Sofia (1998), specializing in robust structures for measurement systems. Research Interests : Metrology and calibration of measurement devices Power quality and electromagnetic field analysis Design of information-measurement systems Robust structures for measurement accuracy Applications in telecommunications and computer technology Recent Work Trends : His 15 most recent publications focus on power quality calibration methods, 4G/5G network protocols, and advanced signal processing techniques. Collaborations with international researchers emphasize standards like BDS EN 50160 and tools such as LabVIEW and MATLAB. Scientific Awards : Silver Medal, Technical University of Sofia Grants and Projects : Led 8 international projects and participated in over 30, including EU-funded initiatives on measurement standards and power quality. He also contributed to educational reforms in electrical engineering curricula. Publications and Leadership : Authored over 20 books and 130+ publications, with 15+ indexed in Scopus/Web of Science. Active in symposiums like International Symposium Metrology and Metrology Assurance as committee member.
Sergey Vasilyevich Golub is a Professor at the Department of Software for Automated Systems at Cherkasy State Technological University. He holds a Doctor of Technical Sciences degree and has been part-time faculty since 2018. His career spans multiple roles, including dean at CHNU and head of departments in intelligent systems and software engineering. His research focuses on agent programming, intelligent monitoring, medical informatics, and machine learning. He has contributed to interdisciplinary projects linking software engineering with environmental monitoring and healthcare diagnostics. Recent publications emphasize multi-agent systems, social network analysis, and data-driven disease prediction. Key article trends include integrating satellite data for climate monitoring, authorship classification in social media, and agent-based modeling for pandemic forecasting. He collaborates with researchers in Ukraine and abroad, with works in Springer LNNS and CEUR-WS proceedings. Thanks and honorary diplomas from Cherkasy National University (2000, 2003, 2015) Honorary diplomas from Cherkasy Regional State Administration (2001, 2014) Honorary diploma from Ukraine's Ministry of Education and Science (2011) He has authored monographs on data analysis technologies and cross-platform decision support systems. His work bridges academic research and practical applications in intellectual monitoring across healthcare, ecology, and cybersecurity domains.
Myroslav Lyubomyrovych Kovalchuk serves as an Associate Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University. His academic foundation includes degrees in Electronics and Microelectronics, culminating in a Candidate of Physical and Mathematical Sciences qualification. His educational background comprises: Bachelor of Electronics, Yuriy Fedkovych Chernivtsi National University (2001) Engineer-physicist in Microelectronics and Semiconductor Devices, Yuriy Fedkovych Chernivtsi National University (2002) Candidate of Physical and Mathematical Sciences, Specialty 01.04.01 (2010) Kovalchuk's research centers on neural networks, database systems, information system development, and electronics engineering. His work bridges theoretical computer science with practical engineering applications, particularly in AI-driven solutions for real-world problems. He has developed specialized systems for image analysis, financial forecasting, and tourism infrastructure, demonstrating consistent innovation in computational methodologies. Over the past five years, his publication record reveals a strategic focus on neural network optimization, distributed database systems, and intelligent information architectures. His textbooks and research articles show increasing integration of cloud computing with traditional AI techniques, reflecting industry trends toward scalable, accessible computational solutions across finance, healthcare, and tourism sectors. Kovalchuk actively contributes to the Bukovina Information Technology Cluster (since 2019) and provides scientific consulting to KM TRADE LLC on security systems. He maintains professional currency through Sigma Software University's Teachers' Smart Up program and international training initiatives in Warsaw and Suceava, emphasizing modern pedagogical approaches for technical education.
Maria Vitalievna Talakh serves as an Assistant Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University. Holding a Candidate of Biological Sciences degree, she bridges the gap between biological sciences and computer science with her unique interdisciplinary expertise. Education: Bohdan Khmelnytskyi National University of Cherkasy (2018): Systems Analysis, Qualification: Systems Analyst (Diploma with honors) Yuriy Fedkovych Chernivtsi National University (2004): Ecology and Environmental Protection, Qualification: Master of Ecology (Diploma with honors) Candidate of Biological Sciences (2009): Specialty 03.00.16 – ecology, dissertation on 'The role of the forest beech as an edificator species in the forest ecosystem' Research Interests: Dr. Talakh's research spans machine learning algorithms (particularly ensemble models), big data analytics, computer vision, and their applications in life sciences and ecological modeling. Her work demonstrates how computational methods can solve complex problems in environmental monitoring, medical diagnostics, and software engineering. She has developed intelligent monitoring systems for climate, air temperature, plant health, and software test automation, showing the versatility of her methodological approaches. Publication Trends: Her recent scholarly output reveals a strong focus on practical applications of machine learning across diverse domains. The majority of her publications from 2020-2024 explore convolutional neural networks, ensemble methods, and big data processing techniques applied to environmental monitoring, medical diagnostics, and software quality assurance. She frequently collaborates with researchers across disciplines, particularly with colleagues at her institution on projects involving satellite data analysis and ecological monitoring systems. Professional Activities: Member of the Bukovina Information Technology Cluster named after Joseph Schumpeter (Chernivtsi IT Cluster, Cluster Bit Association) since 2019 Scientific consultant for Limited Liability Company 'KM TRADE: Security Systems' (2019) Teaching: Dr. Talakh teaches courses related to data mining, intelligent data analysis, ensemble architectures, geoinformation systems, and big data technologies. She has authored numerous textbooks and teaching materials in these areas, demonstrating her commitment to both research and education in computational methods.
Yuriy Yaroslavovych Tomka is an Associate Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University. He holds a Candidate of Physical and Mathematical Sciences degree and has been serving as an Associate Professor since April 15, 2021. His academic work spans software engineering, neural networks, and biomedical image processing, with numerous publications in these fields. Dr. Tomka's educational background includes: Bachelor's degree in Laser and Optoelectronic Engineering from Yuriy Fedkovych Chernivtsi National University (2005), with honors Candidate of Physical and Mathematical Sciences degree (2009) in specialty 01.04.05 – Optics, Laser Physics Dr. Tomka's research interests focus on software architectures, enterprise application development on the .NET platform, pattern recognition, digital image processing, and neural networks. His work bridges theoretical computer science with practical applications in biomedical diagnostics, financial technology, and e-commerce systems. He has made significant contributions to the development of information systems, machine learning applications, and educational methodologies in computer science. Analysis of Dr. Tomka's recent publications reveals a strong focus on practical applications of software engineering principles, particularly in the .NET ecosystem. His work spans from fundamental software architecture to specialized applications in biomedical imaging and financial forecasting. There's a clear trend toward interdisciplinary research, combining computer science with fields like medicine and finance, demonstrating the versatility and applicability of his technical expertise. Dr. Tomka has received recognition for his mentorship, guiding student research that won second-degree diplomas in national competitions. His professional development includes numerous certifications in modern software technologies from institutions like Sigma Software University. As an active member of the Bukovina Information Technology Cluster named after Joseph Schumpeter since 2019, Dr. Tomka contributes to regional IT development initiatives. He also provides scientific consulting to the Limited Liability Company "Buknanotech" and has managed research projects including "Development of new methods and biomedical systems for polarization-holographic fractalmetry of crystallites of tissues and fluids of human organs" (2019-2021).
Yuriy Ushenko is Professor and Head of the Department of Computer Science at Chernivtsi National University named after Yuriy Fedkovych. An Academician of the Academy of Engineering Sciences of Ukraine (2021), he holds a Doctor of Physical and Mathematical Sciences degree with specialization in optics and laser physics. His interdisciplinary work bridges computer science, biomedical engineering, and optical diagnostics. His educational background includes: Master of Telecommunications from Chernivtsi National University (2003, Diploma with honors) Doctor of Physical and Mathematical Sciences (2015) in specialty 01.04.05 – Optics, Laser Physics Professor title in Computer Science (2017) Professor Ushenko's research focuses on information systems design , data mining , pattern recognition , and digital image processing with specialization in laser polarimetry and interferometry . His work pioneers Mueller-matrix tomography and polarization-correlation microscopy for biomedical applications, developing computational methods to analyze biological tissues and fluids for diagnostic purposes. Key innovations include 3D digital polarization-holographic histology for myocardial damage assessment and intelligent systems for disease identification from medical images. His publication trends reveal a strong emphasis on applying machine learning to biomedical imaging and geospatial analysis, with recent work spanning transformer-based lyrics generation, polarization-singular tissue processing, and migration forecasting systems. The research demonstrates consistent translation of theoretical optics into practical diagnostic tools. Scientific recognition includes: Academician of the Academy of Engineering Sciences of Ukraine (2021) As Director of the Bukovinian Innovation Technologies Cluster (since 2019), he leads regional IT initiatives while serving as Associate Editor for the SCOPUS Q3 journal International Journal of Image, Graphics and Signal Processing . His mentorship has guided students to international awards including ICYS silver medals and EUCYS special prizes. Current projects involve developing automated intelligent systems for LLC 'KM Trade' and advancing Mueller-matrix diagnostic techniques through international collaborations with Springer and SPIE conferences. His laboratory work centers on creating computational frameworks for polarization-correlation microscopy, with active teams developing laser autofluorescence polarimetry techniques and digital image processing pipelines for biomedical diagnostics. The research group maintains strong ties with international partners in optical engineering and medical diagnostics.
Світлана Львівна Воропаєва serves as an Assistant Professor at the Department of Computer Systems and Networks within the Faculty of Physics at Yuriy Fedkovych Chernivtsi National University. Holding a Candidate of Technical Sciences degree (2012) with her dissertation on 'Computerized system of UV sources monitoring', she teaches Engineering Graphics, Computer Graphics, and Technology of Designing Computer Systems while maintaining active research in applied computer engineering. Education: Chernivtsi National University named after Yuriy Fedkovych, Faculty of Physics, Department of Electronic Computing Machines (2000) Her research centers on data compression and protection in telemetry reconfigurable information systems, with significant contributions across computer vision (handwritten text recognition, video parameter optimization), image processing (scanned document enhancement), UV radiation monitoring (Tensor-31M radiometer-dosimeter), and semiconductor photodetector systems. She specializes in hardware-software integration for real-time control applications, particularly in UV source management and smart home technologies, while developing advanced signal processing algorithms for spectral coordination and parameter stabilization. Analysis of her 15 most recent publications (2005-2018) reveals consistent focus on applied solutions bridging computer science and physical instrumentation. Her work demonstrates strong interdisciplinary collaboration, particularly with Vorobets and Dobrovolskyi teams, evolving from semiconductor interface modeling (2005-2007) toward practical implementations in computer vision (2018) and smart systems. Key thematic progression shows increasing emphasis on neural networks, parallel computing, and IoT-integrated hardware solutions while maintaining core expertise in radiation physics and optoelectronics. Professional development is evidenced by 15+ certificates (2019-2023) in digital competencies including Adobe Photoshop, project planning, active learning methods, and international conference participation (Austria, Czech Republic, Romania). Her technical leadership spans hardware-software co-design for UV monitoring systems, photodetector optimization, and embedded control modules, with consistent publication output in Ukrainian and international engineering venues.
Assoc. Prof. Ivan Ivanov is affiliated with the Faculty of Theology at the University of Sofia , where he holds the academic rank of Associate Professor . His research spans interdisciplinary domains, including Medical Research , Artificial Intelligence , Public Health , and Theology . Email: ivan.ivanov@theo.uni-sofia.bg Research Interests: His work addresses diverse topics such as: Medical Research: Cancer prognosis, Ki-67 assessment, dental treatments, and infectious diseases. Artificial Intelligence: Energy consumption prediction and AI in programmer training. Public Health: Co-infections in pandemic contexts, substance abuse interventions, and epidemiological studies. Biomechanics: Analysis of human movement and therapeutic applications. Education: AI-driven educational tools and language acquisition. Theology: Historical and liturgical interpretations. Publications: His recent works focus on interdisciplinary applications in healthcare, AI, and theological studies, reflecting a broad methodological approach. Additional Contributions: Ivanov actively engages in software-defined radio research, cybersecurity, and educational technology development.
Стефан Кирilов Къртунов is a Professor in the Management Department at Technical University of Gabrovo, Bulgaria. With a career spanning over three decades, he has established himself as a prominent figure in mechatronics, micro-nano systems technology, and manufacturing engineering. His work bridges theoretical knowledge with practical applications, focusing on innovative educational approaches and technological advancements in engineering disciplines. Professor Kъртунов's research interests encompass a wide spectrum of engineering fields, with particular emphasis on mechatronics systems, micro and nano technology, production technologies, and educational methodologies for engineering students. His work demonstrates a consistent focus on practical applications of theoretical concepts, especially in the areas of manufacturing processes, quality control, and system integration. He has developed specialized training modules for polymer and hybrid nanocomposites, silicon structures, and optical components that have advanced both educational practices and industrial applications. Analysis of his recent publications reveals a strong trend toward interdisciplinary research that combines mechanical engineering with electronics, materials science, and environmental applications. His work shows increasing focus on sustainable technologies, educational innovation in engineering training, and the integration of modern manufacturing techniques with traditional engineering principles. The recurring themes across his publications include technological optimization, precision manufacturing, and the development of educational frameworks that prepare students for contemporary engineering challenges. Professor Kъртунов has successfully supervised six PhD students through completion, demonstrating his commitment to academic mentorship and the development of the next generation of engineers. His extensive project portfolio includes 15 significant research initiatives, ranging from internal university projects to international collaborations with institutions in Germany, Austria, Romania, and Ukraine. These projects have focused on areas such as mechatronic systems, micro-nano technology, production optimization, and waste treatment technologies. He has contributed significantly to the academic community through his leadership in developing automated design systems like PROTECH and MICROSIS, which have advanced the field of micro-mechanical component design and manufacturing. His work continues to influence both academic research and practical industrial applications in Bulgaria and beyond.
Atanas Atanasov is an Associate Professor and Head of the Department of Agricultural Machinery at the University of Ruse, Bulgaria. His academic career spans agricultural engineering with a dual focus on precision farming technologies and apicultural systems. Contact details include email aatanasov@uni-ruse.bg and phone +359 82 888 442. His research integrates advanced engineering principles with agricultural applications, specializing in agricultural machinery optimization, precision agriculture through UAV and satellite remote sensing, and honeybee population dynamics modeling. Key areas include vibration analysis in harvesters, NDVI-based crop monitoring, soil-water conservation practices, and mathematical modeling of bee colony health. His work bridges theoretical optimization techniques with practical field applications in sustainable farming systems. Analysis of his 2023-2025 publications reveals a strong emphasis on integrating deep learning with remote sensing for crop disease detection (particularly wheat rust), precision management of agricultural machinery fleets, and sophisticated modeling of honeybee colony collapse factors. His research consistently targets resource efficiency improvements in sloped land agriculture and innovative solutions for Varroa mite control in apiculture.
