Valeriya Nikolaeva Simeonova is an Associate Professor at the Faculty of Mathematics and Informatics, University of Sofia, specializing in Information Technologies . Her research focuses on interdisciplinary applications at the intersection of Bioinformatics , Machine Learning , and Parallel Computing , particularly for error discovery in metagenomics data and QSAR modeling in plant biology. Her work emphasizes Next-Generation Sequencing (NGS) data analysis, where she develops algorithms for error detection and correction. She has contributed to optimizing genome assembly techniques through soft computing approaches and explored distributed computing for financial time series forecasting. Notable publications include studies on Golden Root in vitro culture growth (2013) and metagenomics NGS error detection (2015) in journals like BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT . Her collaborations span institutions in Bulgaria, France, and Belgium.
Edgar Vadimovich Vatamanitsa serves as an Assistant Professor in the Department of Computer Science at Chernivtsi National University, specializing in cross-platform software development and cloud computing applications. His work bridges academic research with industry practice through active engagement in both university teaching and commercial software development. Education: Yuriy Fedkovych Chernivtsi National University (2013): Systems Software, Software Engineer Dr. Vatamanitsa's research centers on Java/Android pattern development, client-server architectures, and distributed systems. He pioneers practical implementations of design patterns in cloud environments, with significant contributions to optical field simulation, semiconductor modeling, and intelligent transportation systems. His methodology emphasizes cross-platform efficiency and real-world applicability of theoretical computing concepts. Analysis of his 2022-2024 publications reveals dominant themes in cloud-based optical computation (AWS infrastructure), Java pattern implementation for Android, and cross-platform solutions for traffic management and semiconductor physics. His work consistently integrates distributed systems with domain-specific challenges in optics, printing technology, and educational analytics, demonstrating exceptional versatility across engineering disciplines. Professional Activities: Member of Bukovina Information Technology Cluster (since 2022) Senior Software Developer at EPAM SYSTEMS Certificate in Cross-platform Programming and Information Security (Ternopil National Technical University, 2023) Tech Summer Bootcamp for Teachers participant (2023)
Radoslava Danailova Hristova serves as an Associate Professor at the Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, specializing in advanced computational systems. Her work focuses on integrating grid and cloud technologies with business processes and scientific applications. Her research spans Grid Computing , Cloud Infrastructure , and Service-Oriented Architectures , with significant contributions to distributed systems management. Key areas include: Business Process Management in heterogeneous grid environments Performance monitoring frameworks for HPC applications Middleware development for virtual organizations Resource allocation strategies for scientific modeling Analysis of her 15 most recent publications reveals a consistent focus on practical grid/cloud implementations, particularly in evolving middleware infrastructure (2012-2014) and business process integration. Her work bridges theoretical physics applications with enterprise-level distributed systems. No formal scientific awards are documented in available sources. While no current advisees or major grants are explicitly listed, her publications indicate active collaboration with international research teams including EGI (European Grid Infrastructure) and g-Lite middleware developers. Hristova contributes to Sofia University's Grid Farm operations, supporting physics experiments and environmental modeling. Her technical leadership includes developing ROOT services, accounting systems, and security frameworks for distributed scientific computing environments.
Rosen Pasarelski is a Professor in the Telecommunications Department at New Bulgarian University (NBU), where he has taught since 2003. His academic career includes roles as assistant professor (2003–2015) and current professor status since 2015. He has directed NBU's Telecommunications bachelor's program (2005–2015) and actively participates in departmental councils and seminars. Education : PhD in Radio Transmitter and Receiver Equipment (2009), Master's in Telecommunications Systems (2003), Bachelor's in Telecommunications Systems (2001), Specialist degree in Telecommunications (1999) His research focuses on radio communications , wireless networks , and WEB technologies , with recent publications exploring 5G network optimization, microwave antennas for space-based radar, IoT vehicle tracking, and femtocell technologies. He has contributed to projects including satellite studio development, WLAN network analysis, and electromagnetic field safety studies. Recent publications highlight trends in 5G network algorithms , radio wave attenuation modeling , and AI-driven cybersecurity . His work often intersects telecommunications engineering with practical applications in smart city infrastructure and educational technology . He teaches courses such as: Network Administration and Virtualization Applied Software for Telecommunications Mobile Communications WEB Services Installation Computer Hardware/Software Installation Technical expertise includes LINUX systems , cloud computing , VoIP security , and WiMAX protocol analysis . He holds advanced English and Russian language proficiency alongside native Bulgarian.
Valentina Volodymyrivna Dvorak serves as an Assistant Professor in the Department of Computer Science at Chernivtsi National University. Her professional activities span across computer vision, deep learning, IT project management, and business analysis, with significant contributions to both academic research and practical applications in these fields. Dr. Dvorak holds a Candidate of Technical Sciences degree with specialization in Pressure Processing Processes and Machines. Her educational background includes a Master's in Metallurgical Equipment (2004) and a degree in Organizational Management (2010) from Donbass State Machine-Building Academy. This unique combination of metallurgical engineering and computer science expertise informs her interdisciplinary research approach. Her research interests prominently feature Computer Vision and Deep Learning applications across diverse domains including medical diagnostics, satellite imagery analysis, and industrial process optimization. Dr. Dvorak has made significant contributions to convolutional neural network implementations, automated recognition systems, and polarization-correlation microscopy techniques for biomedical applications. Her work bridges theoretical computer science with practical implementations in healthcare, manufacturing, and environmental monitoring. Analyzing her publication record reveals a strong focus on practical applications of deep learning, with particular emphasis on computer vision systems for medical diagnostics, industrial quality control, and environmental monitoring. Her research demonstrates consistent progression from fundamental metallurgical process optimization to advanced AI applications. Certificate of acknowledgment as scientific supervisor for BLACK SEA SCIENCE 2022 Educational Grant from SoftServe for developing training course (2021) Dr. Dvorak actively contributes to IT education through multiple textbooks including 'Deep Learning for Computer Vision,' 'IT Project Management,' and 'Intelligent Data Analysis.' Her professional development includes numerous certifications from Sigma Software University and SoftServe, reflecting her commitment to staying current with industry practices. She serves as a member of the Bukovina Information Technology Cluster and provides scientific consulting for SKB 'Electronmash KM.'
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.
Odayska Khrystyna Saveliivna is an Assistant Professor at the Department of Computer Systems and Networks, Chernivtsi National University named after Yu. Fedkovych. She holds a Candidate of Technical Sciences degree with a thesis on "Methods and means of improving the visual quality of images in computerized optoelectronic systems" (2020). Her academic work focuses on digital image processing and artificial intelligence systems. Education: Chernivtsi National University (2013) - Computer Systems and Networks. Her research spans digital image processing techniques, particularly Gaussian noise estimation and removal, with applications in medical imaging and optoelectronic systems. She has contributed to international journals and conferences, including studies on neural networks for X-ray image analysis and bilateral filtration methods for medical imaging. Recent publications include "Software and hardware for determining Gaussian noise level in images" (2022) and "Adaptive oriented filtration of digital images in the spatial domain" (2019). Her work combines theoretical advancements with practical tools like hardware-software complexes for noise estimation and removal.
Mykhailo Petrovych Gorsky serves as Assistant Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University, specializing in computational modeling of physical systems and high-performance computing. He holds a Candidate of Physical and Mathematical Sciences degree (2012) and Associate Professor certification (2017). His educational credentials include: Bioengineer from Yuriy Fedkovych Chernivtsi National University (2002) Candidate of Physical and Mathematical Sciences in Optics and Laser Physics (2012) Professional development certificates in cloud services and distributed systems from Ternopil National Technical University (2021) Gorsky's research centers on computer modeling of physical phenomena with emphasis on parallel, distributed, and cloud computing applications. His work bridges optical physics, digital image processing, and material science, developing algorithms for 3D polarization mapping, polycrystalline structure reconstruction, and statistical analysis of microscopic images. Key application areas include printing industry quality control and biomedical diagnostics. Analysis of his 2022-2024 publications reveals a strong trend toward cloud-based optical computation (particularly AWS infrastructure), interdisciplinary biomedical tomography, and industrial applications of polarization mapping. His work consistently integrates computer science with optics and material science, demonstrating technical depth in both algorithm development and practical implementation. Professionally, Gorsky serves as Team Lead at OSF GLOBAL UKRAINE and participates in the Chernivtsi IT Cluster 'Cluster Bit' since 2020. He has authored multiple textbooks on distributed systems and cloud computing, including 'Technologies of Distributed Systems and Parallel Computing' (2022) and 'Applied Programming: From Theory to Practice' (2021).
Associate Professor Yakovleva Inna Dmitrievna serves in the Department of Computer Systems and Networks at Chernivtsi National University's Faculty of Physics. With over two decades of academic experience, she has established herself as a prominent researcher in computer architecture and parallel computing. Her career progression from Assistant Professor (2003-2011) to her current Associate Professor position demonstrates her commitment to the institution and field. Her educational background includes a degree from Chernivtsi State University named after Yu. Fedkovych (Faculty of Physics, Department of Computer Science, 1993) followed by candidate studies at Lviv Polytechnic National University (2004-2007). She earned her Candidate of Technical Sciences degree in 2010 with a thesis on "Methods and tools for designing algorithmic operating devices with graphical representation of executed algorithms". Professor Yakovleva's research spans specialized computer systems, theoretical foundations of computer construction, data stream processing, algorithm development, parallelization techniques, and specialized processor design. Her work demonstrates a consistent focus on bridging theoretical computer science with practical hardware implementation, particularly through graphical representations of algorithms. She has made significant contributions to structural matrix representations of algorithm flow graphs and parallel computing architectures. Her publication record shows a strong trajectory in algorithm representation methods, evolving from foundational work on structural matrices to applications in smart home technology, voice control systems, and image processing. Recent publications indicate expanding interests in AI applications while maintaining core expertise in parallel computing and hardware design. Certificate of Appreciation from Ministry of Education (2023) 5 Years of Service Instructor Certificate (2021) Honorary Certificate from Regional Authorities (2019) Multiple EarthRover competition awards (2011-2018) Professional certifications in Linux, Cisco, and ChatGPT technologies Professor Yakovleva teaches System Software, VHDL Design Technology, Linux System Administration, and Cisco NDG Linux Essentials. Her professional development includes numerous certifications reflecting current industry trends. While the text doesn't detail specific mentoring activities, her extensive publication record with multiple co-authors suggests active collaboration with students and colleagues. Her work with research teams is evident through numerous joint publications, particularly with colleagues like Melnyk A.O. and Lisovenko I.D.
Olar Oleksandr Valeriyuvych serves as an Assistant Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University, holding a Candidate of Physical and Mathematical Sciences degree with expertise spanning computer science and optics. His interdisciplinary work bridges web development, machine learning, and biomedical imaging applications. Education: Graduated from Yuriy Fedkovych Chernivtsi National University (2015) with a degree in Laser and Optoelectronic Engineering (Research Engineer) Awarded Candidate of Physical and Mathematical Sciences (2019) for dissertation on 'Differential Muller-matrix diagnostics of polycrystalline structure of biological layers' (Specialty 01.04.05 – Optics, laser physics) His research integrates computational techniques across two primary domains: computer science (web development frameworks, machine learning algorithms, distributed systems) and optics (Mueller-matrix polarimetry, biomedical tissue analysis, forensic diagnostics). Recent publications demonstrate strong interdisciplinary collaboration, particularly in developing web applications for health monitoring systems and computational methods for optical field analysis. Analysis of his 15 most recent publications reveals evolving focus from pure optics research (2018-2020) toward computer science applications (2021-2024), with increasing emphasis on machine learning implementations and full-stack development. Key trends include convolutional neural networks for image recognition, distributed computing solutions for optical simulations, and practical web frameworks for real-world applications like pandemic monitoring and e-commerce systems. Professional Activities: Member of Bukovina Information Technology Cluster (Chernivtsi IT Cluster) since 2021 (Certificate CB No. 102021) Practitioner for 'New Side Solutions' company Author of multiple textbooks including 'Applied Programming: From Theory to Practice' (2021) and 'Modern Web Development Technologies: Frontend Development' (2022) He maintains active professional development through numerous certifications including Google Project Management specialization and advanced React/Node.js training, while teaching JavaScript technologies courses. Information regarding specific student advising and grant funding details is not provided in available sources.
Lisovenko Iryna Dmytrivna serves as Assistant Professor in the Department of Computer Systems and Networks at Yuriy Fedkovych Chernivtsi National University since 2002, following her 2000 diploma in Computer Systems and Networks from the same institution. Her professional trajectory includes software engineering at a municipal medical institution (2000–2002) and ongoing contractual collaboration with Elogic Commerce since 2018. Her academic credentials include: Yuriy Fedkovych Chernivtsi State University, Faculty of Physics, Department of Computer Science, specialty "Computer Systems and Networks" (2000) Dr. Lisovenko's research centers on parallel and distributed computing, emphasizing automatic parallelization techniques, GPU-accelerated non-graphical computing, and structural analysis of program intermediate representations. She concurrently investigates specialized computer systems for data stream processing and develops methodologies for enterprise accounting automation, bridging theoretical computer science with business process optimization. Her scholarly output demonstrates sustained innovation in high-performance computing architectures. Analysis of her 15 most recent publications (2012–2021) reveals three dominant research thrusts: GPU-based parallelization (40% of works, including Kalyna encryption and bitonic sorting implementations), algorithmic optimization for diverse hardware (33%, covering sorting networks and structural matrices), and applied systems for real-world problems (27%, spanning smart home control, social media analysis, and enterprise automation). This evolution reflects increasing focus on practical GPU applications while maintaining core expertise in parallel algorithm design. She actively contributes to academic development through teaching Parallel and Distributed Computing, Economic and Legal Aspects of Enterprise Automation, and DevOps/DevNet courses, while authoring 12+ methodological manuals including "Parallel and Distributed Computing: Laboratory Workshop" (2022) and "System Administration of Linux OS" (2021). Her professional development includes certifications in Constitutional Law, Finance and Investment, DevNet Associate, and All Digital Week initiatives (2020–2021).
