جست‌وجوی استاد و پژوهشگر

Lidija Fodorمشاهده پروفایل

استادیار

• Optimization
• Big Data Analytics
• Clustering Algorithms
• +۳ مورد دیگر

Lidija Fodor is an Assistant Professor at the Department of Mathematics and Informatics, University of Novi Sad, Serbia, based in the Information Technologies and Systems office on the second floor of the DMI&DF building. Her core research areas include: Optimization (specializing in distributed/parallel algorithms) Big Data Analytics (banking, cross-sectoral applications) Clustering Algorithms (convex clustering, hierarchical methods) Time-series Analysis (psychological domains) Distributed Computing (HPC clusters, multi-agent systems) Data Mining (real-world implementation) Recent publications (2012-2025) reveal a strong focus on algorithmic innovation for distributed optimization, with significant contributions to parallel Levenberg-Marquardt methods, ADMM-based clustering, and performance evaluation in HPC environments. Her work bridges theoretical optimization with practical applications in banking (CaixaBank case study) and psychological time-series analysis. Scientific Awards: No awards mentioned in provided text Advising and Grants: No student advisement information available No grant funding details provided

• Computer Engineering
• Software Engineering
• Artificial Neural Networks
• +۴ مورد دیگر

Dr. Željko Jovanović is an Assistant Professor at the Department of Computer and Software Engineering , Faculty of Technical Sciences in Čačak , University of Kragujevac , Serbia. He holds a PhD in Electrical Engineering and Computing (2020) from the University of Niš, with a focus on Application of IT in improving the quality of patient transport , and a Diploma Engineer in Computer Science (2008) from the University of Kragujevac. Current roles: Head of Department (2023–present), Coordinator of Undergraduate Studies (2021–present) Teaching areas: Programming basics, Object-oriented programming, Software engineering, Medical informatics (PhD studies) His research focuses on Computer Engineering and Software Engineering , with applications in Medical Informatics , Intelligent Transportation Systems , IoT , and Smart Cities . He has contributed to 15+ publications in journals and conferences, including IEEE Transactions on Intelligent Transportation Systems , Electronics , and Transactions in GIS , emphasizing artificial neural networks , data mining , and comfort assessment in healthcare and transportation contexts. Projects include the national TR32043 initiative on low-power computing systems and the international NeReLa project for remote labs. He has developed technical solutions for thermal comfort monitoring , smart suspension control , and IoT-based systems , and serves on academic committees for software engineering and computer science education.

• Engineering Management
• Quality Management
• Decision Making
• +۵ مورد دیگر

Aleksandar Aleksić is an Associate Professor at the Faculty of Engineering Sciences, University of Kragujevac, specializing in Engineering Management within the Department for Production Engineering. His academic position was officially confirmed on June 9, 2021, and he maintains an active research profile through the SciDAR repository. Dr. Aleksić's research interests span Engineering Management, Quality Management, Decision Making, Risk Assessment, Fuzzy Logic, and Machine Learning Applications. His scholarly work demonstrates a strong focus on integrating advanced computational methods with traditional engineering management practices to solve complex industrial problems. He has made significant contributions to fields such as PFMEA 4.0 maturity assessment, organizational resilience, and multi-criteria decision-making under uncertainty. His recent publications (2024-2025) reveal a clear research trajectory emphasizing the application of fuzzy logic, machine learning, and exact solution methods to engineering management challenges. Notably, his work bridges theoretical advancements with practical industrial applications, particularly in manufacturing systems, asset maintenance, and quality performance enhancement. The consistent publication output across multiple high-impact topics demonstrates his active engagement in cutting-edge research. As a faculty member at one of Serbia's leading engineering institutions, Dr. Aleksić contributes to the academic community through his teaching in production engineering and his research that addresses contemporary challenges in engineering management. His work has implications for both academic theory and industrial practice, particularly in the areas of risk management, process optimization, and decision support systems.

Marko Đapanمشاهده پروفایل

دانشیار

• Industrial Engineering
• Manufacturing Systems
• Occupational Safety
• +۷ مورد دیگر

Dr. Marko Đapan is an Associate Professor at the Department of Production Engineering, Faculty of Engineering, University of Kragujevac. His work focuses on advancing manufacturing systems through Industry 4.0/5.0 integration, occupational safety, and human-robot collaboration. He leads research in neuroergonomics, lean manufacturing, and risk assessment, emphasizing practical applications in SMEs and automotive industries. Key research interests include optimizing assembly line performance, mental workload assessment via EEG, and resilient supply chain design. His projects frequently address challenges in workplace safety compliance, process improvement through robotics, and mitigating human factors risks. Notable contributions include frameworks for neuroergonomic evaluations, risk assessment tools for SMEs, and studies on Industry 4.0 adoption. His work bridges technological innovation with human-centric design principles, particularly in high-risk industrial environments. Publications highlight interdisciplinary approaches combining machine learning with engineering practices, such as deep learning for waste sorting and IoT integration for ergonomic safety monitoring. Current trends in his work emphasize sustainable manufacturing practices aligned with occupational health goals. No scientific awards are listed. His advising and grant activities are not detailed in available texts, though his extensive publication record suggests active research collaboration. He is affiliated with the Faculty of Engineering's laboratories focused on neuroergonomics and human-robot interaction experiments.

Marija Savkovićمشاهده پروفایل

پژوهشگر

• Industrial Engineering
• Manufacturing Systems
• Human-Robot Collaboration
• +۵ مورد دیگر

Marija Savković is a Researcher at the Department for Production Engineering, Faculty of Engineering, University of Kragujevac in Serbia. Her scientific field focuses on Industrial engineering and engineering management, with a strong emphasis on neuroergonomics, human-robot collaboration, and Industry 4.0 implementation. She holds a position in the Department for Production Engineering, contributing to advancements in manufacturing systems and workplace safety. Her research interests include optimizing industrial processes through robotics and automation, assessing mental workload in assembly tasks using EEG, and enhancing workplace safety via IoT and AI technologies. She has conducted extensive work on ergonomic assessments in human-robot collaboration environments, emphasizing both physical and cognitive aspects of operator performance. Her recent studies explore frameworks for neuroergonomic evaluation, comparative analyses of agile project management tools, and the integration of Industry 4.0 technologies in SMEs. Key projects involve developing robotic stations for fruit and vegetable processing, predictive demand analysis in automotive industries, and evaluating safety compliance using deep learning object detection. Marija collaborates on modular industrial workstations for neuroergonomics research and has contributed to national radon surveys in Serbia. Her work bridges advanced technologies with practical industrial challenges, aiming to improve operational efficiency and worker well-being in manufacturing sectors.

Jovanka Lukićمشاهده پروفایل

استاد

• Automotive Engineering
• Vehicle Dynamics
• Ergonomics
• +۳ مورد دیگر

Jovanka Lukić is a Professor and Chair of the Department for Motor Vehicles and Engines at the Faculty of Engineering, University of Kragujevac. Her research focuses on automotive engineering, vehicle dynamics, and ergonomics with a strong emphasis on environmental impact and sustainable transportation solutions. She leads studies on brake wear pollution, electric vehicle optimization, and driver comfort under vibration exposure. Her academic contributions span over three decades, addressing technical challenges in vehicle design, energy systems, and human-machine interaction. Key areas include: (1) Development of predictive models for brake wear particle emissions, (2) Optimization of vehicle suspension and transmission systems, (3) Application of AI/ML for comfort assessment, and (4) Sustainable materials in automotive manufacturing. She holds a prominent position within the Department’s Motor Vehicles and Motors unit, overseeing cutting-edge research facilities including specialized dynamometers and vibration testing laboratories. Her work integrates mechanical engineering principles with environmental science to address global transportation challenges.

Aleksandar Milenkovicمشاهده پروفایل

استادیار

• Medical Informatics
• Health Information Systems
• Software Engineering
• +۳ مورد دیگر

Aleksandar Milenkovic is an Assistant Professor at the Faculty of Electronics in Niš, University of Niš, specializing in Computing and Informatics. He holds a PhD in Electrical and Computer Engineering (2022) and a BSc in Computer Science (2009), both from the same institution. His research focuses on medical information systems integration, chronic disease tracking, pandemic response strategies, and software engineering practices in healthcare. Education: PhD in Electrical and Computer Engineering, Faculty of Electronics, University of Niš (2022) Bachelor's in Computer Science, Faculty of Electronics, University of Niš (2009) Research interests span medical information systems, chronic disease management, pandemic-adaptive technologies, and cross-platform software development. Notable projects include the MEDIS.NET system and collaborative work on distributed medical data repositories. His recent publications emphasize data synchronization in healthcare systems, resource-aware software updates, and telemedicine expansion during crises. Advising and Grants: Collaborates with senior researchers like Dragan Janković and Petar Rajković on national/international projects. Active in developing software solutions for Serbian public healthcare challenges. Labs/Teams: Involved in the MEDIS.NET project and interdisciplinary teams addressing healthcare system interoperability and pandemic response technologies.

• Computational Electromagnetics
• Antenna Design
• TLM Modeling
• +۱ مورد دیگر

Nebojša S. Dončov is Full Professor at the Department of Telecommunications, Faculty of Electronic Engineering, University of Niš. As Vice-Dean for Science and IEEE Senior Member, he leads research in computational electromagnetics with applications in antenna design and electromagnetic compatibility. His TLM modeling techniques have been validated internationally. Education & Affiliations: PhD in Telecommunications (2002) Corresponding Member of Academy of Engineering Sciences of Serbia IEEE MTT/AP/EMC Society Senior Member Research Focus: Pioneers cylindrical TLM methods for: Conformal antenna analysis EM propagation modeling On-body communication systems Machine learning-assisted EM simulation Publication Trends: Recent IEEE Transactions papers demonstrate advances in neural network applications for antenna optimization and experimental validation of novel simulation techniques. Research consistently bridges theoretical electromagnetics with practical wireless system design. Awards & Projects: Aleksandar Marinčić Award (2017) URSI Young Scientist Award (2002) NATO SPS Project Director: UAV-Based Explosive Detection Royal Society projects on sensor networks Teaching & Service: Authors textbooks on antenna theory and simulation techniques. Chairs IEEE TELSIKS conferences and coordinates Erasmus+ collaborations with European technical universities.

• Telecommunications
• RF Amplifier Design
• Antenna Modeling
• +۳ مورد دیگر

Aleksandar S. Atanasković is a Lecturer at the Faculty of Electronic Engineering within the University of Niš , affiliated with the Department of Telecommunications . His academic career has been dedicated to advancing telecommunications engineering through both theoretical and applied research. PhD in Telecommunications (2015) Master's Degree in Telecommunications (2004) Bachelor's Degree in Electronics and Telecommunications (1997) His research focuses on telecommunications and RF engineering, with a specialization in amplifier linearization techniques, computational modeling of antennas, and deep learning applications for sensors. His work addresses challenges in wireless communication systems, including: Digital injection methods for Doherty amplifiers Even-order nonlinear signal processing Cylindrical TLM method for antenna analysis Soft sensor development using neural networks Near-field measurement calibration Broadband amplifier optimization The trends in his publications reflect expertise in microwave engineering, signal processing, and machine learning integration. His work spans theoretical modeling and experimental validation. No scientific awards or fellowships are explicitly mentioned in the provided text.

• Environmental Chemistry
• Air Pollution
• Toxicology
• +۴ مورد دیگر

Gordana Jovanović is a researcher at Singidunum University with expertise in environmental chemistry and pollution analysis. She holds a PhD in Environmental Chemistry from the University of Belgrade (2012–2015), followed by postdoctoral research at the Institute for Medical Research and Occupational Health in Zagreb (2017–2018). Her research focuses on persistent organic pollutants (POPs), polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs), with applications in air quality, indoor/outdoor environmental fate, and human health risk assessment. She employs advanced methods like machine learning (XGBoost, SHAP) and metaheuristics for predictive modeling of pollutant behavior. Key contributions include studies on PCBs and OCPs in breast milk, fish, and soil-plant-air systems, as well as biomonitoring using mosses and tree leaves. Her work bridges environmental chemistry, toxicology, and AI-driven analytics, addressing global challenges in pollution management and public health.

• Operations Research
• Optimization
• Electric Vehicle Scheduling
• +۳ مورد دیگر

Raka Jovanović is a Researcher at Singidunum University , affiliated with the Faculty of Informatics and Computing . His work bridges theoretical algorithm design with practical applications in transportation, energy systems, and optimization. Bachelor's, Matematički fakultet - Beograd (2002) Postgraduate, Matematički fakultet - Beograd (2007) Doctorate, Matematički fakultet - Beograd (2016) His research focuses on metaheuristics and matheuristics for complex optimization problems, including: Knapsack problems with penalty constraints Electric vehicle charging infrastructure planning Graph-based combinatorial optimization Energy management in extreme climates Integration of machine learning with traditional algorithms Recent publications demonstrate a strong emphasis on Fixed Set Search as a unifying methodology across diverse domains. Journal articles in 2024-2025 explore its application to: Maximum Diversity Problem Multidimensional knapsack Charging station placement Multi-objective optimization Conference papers from 2023-2024 highlight collaboration with international teams on: EV fleet scheduling Desert climate agricultural energy systems Max-Cut and disjoint dominating sets problems Waterway transport prediction models

• Atmospheric Pollution Modeling
• Air Quality Assessment
• Machine Learning Applications in Environmental Science
• +۵ مورد دیگر

Andreja Stojić is an active academic researcher at Singidunum University in Belgrade, Serbia, specializing in atmospheric pollution modeling and environmental health. Her work integrates advanced machine learning techniques with traditional environmental science methodologies to address urban air quality challenges. Born January 3, 1976 in Jagodina, Serbia Doctoral studies (2007-2015) in Theoretical Physics/Atomic and Molecular Physics at University of Belgrade Bachelor studies (1998-2007) in Applied Physics and Informatics at University of Belgrade Her primary research focuses on atmospheric pollution dynamics, particularly PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and their health implications. She pioneers explainable artificial intelligence approaches for predicting pollutant behavior, including innovative applications of XGBoost, SHAP, and hybrid metaheuristics. Her work bridges environmental chemistry, computational modeling, and public health risk assessment. Analysis of her 15 most recent publications reveals a strong trend toward explainable AI frameworks for environmental systems, with increasing emphasis on PM2.5-bound pollutants, indoor-outdoor pollution transfer mechanisms, and VOC fate prediction. Her research consistently targets urban environments, particularly Belgrade, with growing international collaboration evident in Mediterranean marine pollution studies. While no specific scientific awards are documented in the provided materials, her extensive publication record in high-impact journals demonstrates significant scholarly contribution to environmental science. Dr. Stojić leads collaborative research projects involving multi-institutional teams across Serbia and international partners, focusing on atmospheric modeling and pollution source apportionment. Her work frequently employs receptor modeling techniques and advanced multivariate statistical methods for environmental data analysis. Her research infrastructure includes access to PTR-MS measurement systems and computational resources for developing AI-driven environmental models, with applications spanning urban air quality management to marine ecosystem contamination studies.

• Digital Marketing
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Aleksandar Đorđević is affiliated with Singidunum University, where he contributes to research in digital marketing, machine learning, and cybersecurity. His work spans academic publications in journals like Journal of Industrial Intelligence and Sustainable Cities and Society , as well as conference proceedings from events such as Sinteza and ICIST. He co-authored the textbook Digitalni marketing (2021) and has been actively involved in pandemic-related research, including predictive modeling for COVID-19 cases using hybrid algorithms. His research also explores Bitcoin price prediction via LSTM networks and fraud detection in digital advertising. Key areas of focus include cybersecurity applications of neural networks, optimization algorithms in healthcare, and entrepreneurship opportunities in emerging markets like the GCC region. Đorđević has collaborated on interdisciplinary projects involving data analysis, artificial intelligence, and sociological studies of pandemic impacts.

• Cybersecurity
• Educational Technology
• Biometric Systems
• +۵ مورد دیگر

Jelena Gavrilović is an Assistant Professor at Singidunum University's Faculty of Informatics and Computing, specializing in Computer Science with a focus on cybersecurity, educational technology, and biometric systems. She holds a Doctoral degree in Advanced Protection Systems from Singidunum University (2022), preceded by a Master's in Contemporary Information Technologies (2011) and a Bachelor's in Programming and Design (2007). Education Background: High School: Srednja Tehnička PTT škola, Beograd (2002) Bachelor: Fakultet za informatiku i računarstvo, Programming and Design (2007) Master: Fakultet za informatiku i računarstvo, Contemporary Information Technologies (2011) PhD: Singidunum University, Advanced Protection Systems (2022) Research interests include cybersecurity frameworks, adaptive learning systems, and robust biometric authentication mechanisms. Her work combines technical innovation with pedagogical applications, such as integrating dynamic mathematics software into distance education platforms. Recent projects focus on drone forensics and multi-modal biometric systems for enhanced security. Notable contributions include studies on noise uncertainty in predictive systems (2015), comparative analysis of activity-based costing models in automotive industries (2011), and development of free mathematics education tools (2012). Her publications span academic journals like Journal of Mechatronics and conference proceedings such as Sinteza and ICETRAN. She actively contributes to Singidunum University's distance learning initiatives, emphasizing interoperability between educational software and modern e-learning architectures. Current projects explore hybrid optimization algorithms for healthcare applications and AI-driven path planning for mobile robots.

Ivan Milićevićمشاهده پروفایل

استاد

• Mechanical Engineering
• Mechatronics
• CNC Machining
• +۴ مورد دیگر

Ivan Milićević is a Professor at the Department of Mechanical Engineering, Faculty of Technical Sciences Čačak, University of Kragujevac. His work focuses on mechanical engineering, mechatronics, and industrial automation with a strong emphasis on CNC machining, robotics, and materials processing. He has contributed to over 80 scientific and professional papers, two university textbooks, and multiple technical solutions. Employment history includes roles at AD “Proleter” Arilje (1999-2000) and Faculty of Technical Sciences Čačak since 2000. Research spans tool design for excavators, intelligent control systems, and thermal analysis of materials. Recent publications show expertise in 3D printing optimization, robotic simulation, and industry 4.0 educational reforms. Active in projects related to mold filling control, bearing diagnostics, and collaborative robotics. His work integrates MATLAB/Simulink, CAD/CAE/CAM tools, and genetic algorithms for manufacturing optimization.