Find Academic Supervisors

• Process planning
• Production optimization and logistics
• Techno-economic optimization
• +5 more

Dejan Lukić serves as Full Professor and Vice-Dean for Investments and Cooperation with Industry at the Faculty of Technical Sciences, University of Novi Sad. He has held progressively senior academic positions since joining the institution in 2000, including Director of the Department of Production Engineering (2021-2024) and Assistant Director (2018-2021). His academic journey began with undergraduate studies in Mechanical Engineering at the same faculty, followed by master's (2007) and doctoral degrees (2012) in specialized production engineering fields. His educational background includes: Bachelor's degree in Mechanical Engineering, Faculty of Technical Sciences (1999) Master's thesis: 'Development of a system for automated design of technological processes for manufacturing plastic injection molds' (2007) Doctoral dissertation: 'Development of a general model of technological preparation of production' (2012) Professor Lukić's research focuses on process planning, production optimization, and techno-economic analysis in manufacturing systems. His work bridges theoretical frameworks with practical industrial applications, particularly in virtual design, CAPP systems integration, and optimization of manufacturing processes. He has made significant contributions to the application of digital technologies in production engineering, including STEP-NC implementation, fuzzy logic for quality control, and collaborative process planning systems. His publication record demonstrates consistent advancement in manufacturing process optimization, with recent work emphasizing multi-criteria decision making, thin-walled structure machining, and digital integration of manufacturing systems. The research trajectory shows evolution from foundational work in traditional process planning toward contemporary applications involving artificial intelligence, collaborative systems, and Industry 4.0 technologies. Professor Lukić has been actively involved in academic service, participating in numerous evaluation committees, professional competitions, and serving as representative of Serbia in the Assembly of the Institute for Materials Testing. His industrial engagement includes consulting on CE/3A product marking, technical system maintenance, and implementation of application software solutions. As an educator and mentor, he has supervised more than 45 undergraduate and master's theses, completed one doctoral dissertation supervision, and currently guides three doctoral candidates. He teaches a comprehensive curriculum spanning process planning, production optimization, logistics, and technological preparation of production across all academic levels. His leadership extends to heading the Master of Professional Studies in Production Engineering program and serving on program committees for multiple scientific conferences and journals in mechanical engineering. Professor Lukić also maintains active industry collaboration through solving professional problems related to product development, manufacturing systems maintenance, and specialized training programs.

Lidija FodorView profile

Assistant Professor

• Optimization
• Big Data Analytics
• Clustering Algorithms
• +3 more

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
• +4 more

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.

• Environmental Chemistry
• Air Pollution
• Toxicology
• +4 more

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.

• Atmospheric Pollution Modeling
• Air Quality Assessment
• Machine Learning Applications in Environmental Science
• +5 more

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.

• Information Systems
• Computational Intelligence
• Machine Learning
• +4 more

Miljan Vučetić is a Serbian academic specializing in Information Technologies , currently employed as a scientific advisor at the Faculty of Electrical Engineering, University of Belgrade. He has also made significant contributions at the Vlatacom Institute since 2014, leading their Artificial Intelligence Team from 2019 onward. Bachelor's in Telecommunications (Faculty of Transportation, 2008) Doctorate in Information Systems (Faculty of Organizational Sciences, 2013) His research focuses on Computational Intelligence , with key contributions to Fuzzy Logic , Big Data , and Machine Learning , particularly in applications for Smart Cities and Network Optimization . His work spans from theoretical database modeling to practical implementations in IoT and Security Systems . In 2024, he achieved IEEE Senior Member status, reflecting his significant professional impact. He also holds a Microsoft Certification and has contributed to 14 technical solutions. Vučetić has been instrumental in developing Vlatacom Institute's AI Team and participates in FP7 , Horizon 2020 , and COST international research programs. His collaborative work extends across Smart City systems, Healthcare technology, and Transportation optimization projects.

Milena RadenkovicView profile

Professor

• Intelligent mobile networks
• Deep artificial intelligence
• Dynamic geographic-spatial-time complex graphs
• +6 more

Professor Milena Radenkovic is affiliated with Alfa BK University as a Full Professor in the Faculty of Information Technologies. She graduated from the Faculty of Electronics in Niš, Serbia, and earned her doctorate from the Faculty of Computer Sciences at the University of Nottingham, Great Britain. Research focuses on intelligent mobile networks, deep AI, and distributed predictive analytics for autonomous vehicles, drones, and IoT systems Work includes delay-tolerant protocols for emergency communications and interplanetary internet applications Mentor to over 100 master’s students and multiple doctoral candidates globally Active in international academic governance as a member of doctoral defense committees and quality control panels for scientific ministries.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +3 more

Timea Bezdan is a researcher at Singidunum University, specializing in artificial intelligence, machine learning, and optimization algorithms. She holds a doctoral degree in Advanced Protection Systems (2021-2024) from Singidunum University, following master's degrees in Contemporary Information Technologies (2020-2021) and bachelor's degrees in Software and Data Engineering (2017-2020) and Marketing and Management (2013-2017). Her research focuses on applying AI to environmental science, healthcare, and computational efficiency, with notable work on air pollution modeling, disease detection, and algorithm optimization. Key research interests include neural network architecture design, hybrid metaheuristic approaches, and bias mitigation in AI systems. Recent projects address demographic bias in age estimation models and energy-efficient cloud-edge computing. Her work spans interdisciplinary collaborations, integrating machine learning with environmental monitoring and medical diagnostics. Education: Doctoral studies: Advanced Protection Systems, Singidunum University (2021–2024) Master's: Contemporary Information Technologies, Singidunum University (2020–2021) Bachelor's: Software and Data Engineering, Singidunum University (2017–2020) Bachelor's: Marketing and Management, Singidunum University (2013–2017) Publications highlight contributions to AI-driven environmental analysis, healthcare applications (e.g., Parkinson’s disease detection), and optimization techniques for neural networks. Her work often combines metaheuristic algorithms like firefly, bat, and particle swarm optimization with machine learning frameworks. Research groups and collaborations include Singidunum University’s Institute for Applied AI, with active participation in international conferences such as IEEE Symposia and Sinteza. Ongoing projects focus on explainable AI for emotion recognition and AI-based crisis analysis of environmental pollutants.

Миона В. Андрејевић СтошовићView profile

Professor

• Electronics
• Signal Processing
• Power Systems
• +3 more

Миона В. Андрејевић Стошовић is a full Professor at the Department of Electronics, Faculty of Electronic Engineering, University of Niš, Serbia. Appointed to her current rank in 2023, she has maintained continuous academic affiliation with the same institution since completing her education. Education: PhD in Electronics (2006, University of Niš) MSc in Electronics (2003, University of Niš) BSc in Electronics (2000, University of Niš) Her research spans electronics with specialization in signal processing , power systems , and renewable energy applications . Recent work integrates neural networks for geotechnical and hydrological modeling, demonstrating interdisciplinary innovation. She actively contributes to photovoltaic system optimization and harmonic pollution detection in power grids. Analysis of her publications reveals a strong focus on filter design (40% of publications), renewable energy systems (30%), and neural network applications (20%), with consistent contributions to high-impact electronics journals since 2014. Administrative Roles: Head of the Department of Electronics. Currently participates in 2 national research projects with no international projects listed.

Branko MarkovićView profile

Assistant Professor

• Speech Processing
• Whispered Speech Recognition
• Computer Engineering
• +3 more

Dr. Branko Marković serves as an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His academic career spans over two decades, transitioning from industry experience in telecommunications to dedicated academic work following the merger of the College of Technical Vocational Studies with the Faculty in 2020. Dr. Marković earned his Bachelor's degree from the Faculty of Electrical Engineering in Belgrade in 1992, followed by a Master's degree in 2004 and a Doctorate in 2018, all focusing on telecommunications and speech processing. His doctoral research centered on speech processing and recognition, establishing the foundation for his subsequent research trajectory. His primary research focuses on speech processing , with groundbreaking work in whispered speech recognition - a challenging domain due to reduced acoustic energy in whispered speech. His research explores sophisticated methodologies including data augmentation techniques, wavelet transformations, inverse filtering, and various feature extraction approaches. He has made significant contributions to Serbian speech database development, notably the "Phonemes_1.0" database. His work bridges theoretical research with practical applications in assistive technologies, security systems, and human-computer interaction, demonstrating consistent innovation through 2024 publications. Analysis of his publication trends reveals an evolution from fundamental techniques like Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) toward more advanced approaches involving deep learning and multimodal systems. His recent work emphasizes data augmentation for model robustness and wavelet-based feature extraction, reflecting the field's progression toward more sophisticated machine learning methodologies while maintaining focus on the unique challenges of whispered speech. Dr. Marković teaches across multiple academic levels, covering essential computer engineering subjects including Computer Assembly and Service, Internet Technologies, Computer Networks, Distributed Software Systems, Web Programming, and Computer Systems Design. His educational contributions extend to several textbooks for vocational studies in computer engineering fields. His professional journey includes valuable industry experience at the Mihailo Pupin Institute in Belgrade and Canadian telecommunications companies Bell Sygma, Architel, and Nortel Networks, followed by founding his own company Big engineering before transitioning to academia. This industry-academia trajectory enriches his practical teaching approach and research relevance.

Dimitrije RozgićView profile

Assistant Professor

• Electrical Power Engineering
• Signal Processing
• Power System Analysis
• +3 more

Dimitrije Rozgić is an Assistant Professor at the Department of Electrical Power Engineering within the Faculty of Technical Sciences in Čačak , University of Kragujevac . He has been affiliated with the university since 2008, holding roles from teaching assistant to his current position. His educational background includes a B.Sc. and Ph.D. in Electrical Engineering (Electrical Power Engineering) from the University of Kragujevac. His research focuses on signal processing applications in power engineering, specifically in harmonics and phasor parameter estimation removal of decaying DC components from fault currents electromagnetic transient analysis optimization of power line emissions His publications highlight expertise in Newton-Raphson algorithms , DFT-Prony methods , and symmetrical component analysis . He has contributed to international journals like IET Signal Processing and Electrical Engineering , and conferences such as IEEE EUROCON . He participated in the national project III 42009: Intelligent Energy Networks (2018-2019, 2021-present). His teaching portfolio includes High Voltage Techniques Power System Analysis Electric Power Transmission Power System Substations He is actively involved in Laboratory for Electrical Machines, Electromotor Drives and Automation , and collaborates with institutions like the University of Belgrade’s School of Electrical Engineering.

Andrijana GaborovićView profile

Assistant Professor

• Information Technologies and Systems
• Information Systems
• Information Technology
• +3 more

Andrijana Gaborović is an Assistant Professor at the Department of Information Technologies within the Faculty of Technical Sciences in Čačak, University of Kragujevac. She teaches courses in Information Systems, Information Technology, Computer Applications, E-commerce, and Web Technologies. B.Sc. in Organizational Sciences (Faculty of Organizational Sciences, Belgrade, 2018) M.Sc. in Information Technology Engineering (Faculty of Technical Sciences, Čačak, 2021) Ph.D. candidate in Information Technology (since 2021) Her research focuses on Information Technologies and Systems, with specializations in: Cloud computing for project management Business intelligence and data warehousing Regression modeling for predictive analytics Web technologies and sentiment mining IoT standardization CRM analytics Recent publications analyze cloud-based project portfolio management, comparative regression techniques, and standardization frameworks for IoT. She holds two student awards: Vuk diploma and Borko Nikitović fund award . Professional roles include: Secretary of the Department of Information Technologies (2023–2024) Member of accreditation teams for Information Technologies programs (2020, 2023, 2024) Organizing committee member for international conferences (TIE 2022, TIE 2024, ITOP 19)

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +3 more

Аleksa Iričanin is a Teaching Assistant and doctoral student at the Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. Employed since 2024, he actively contributes to academic instruction while pursuing his PhD in Information Technology. His educational path includes: Graduation from Electrical and Traffic Technical School "Nikola Tesla" (specializing in Information Technology Electrical Engineering) in 2019 Bachelor's and Master's degrees in Information Technology from Faculty of Technical Sciences Čačak Ongoing doctoral studies in the same program Research focuses on Artificial Intelligence and Machine Learning applications in software engineering, privacy preservation, and web systems. His work addresses code churn prediction, differential privacy implementations, and scalable architecture solutions for real-world technical challenges. Publication trends reveal strong emphasis on AI-driven software maintenance and privacy-aware machine learning, with recent work exploring horizontal scaling techniques for PHP-based MVC applications. These contributions bridge theoretical AI concepts with practical software development constraints. As a teaching assistant trainee, he participates in academic instruction though specific advising roles remain undocumented. His work connects with the faculty's Computer Science Laboratory and broader technical infrastructure supporting Information Technologies research.

Saša MilojevićView profile

Assistant Professor

• Automotive Engineering
• Tribology
• Alternative Fuels
• +5 more

Saša Milojević is an Assistant Professor at the Faculty of Engineering, University of Kragujevac , specializing in the Department for Motor Vehicles and Motors. His research focuses on automotive engineering, tribology, alternative fuels, and mechanical systems optimization. He holds a scientific expertise in motor vehicles and engines, with a strong emphasis on material science applications in automotive components and emission reduction strategies. His work spans composite materials for vehicle components, energy efficiency in hybrid powertrains, and tribological optimization of mechanical systems. Recent studies include biomethane-powered vehicles, particulate matter emission control, and machine learning applications in electric vehicle maintenance. He also explores sustainable logistics for recycling rare metals in hybrid vehicles and digital technologies for waste collection services. Publications highlight advancements in stress analysis of structural components, numerical hybridization optimization, and safety protocols for LNG-fueled trucks. His research reflects a commitment to eco-friendly transportation solutions and industrial maintenance innovation. Affiliated with the University of Kragujevac, he contributes to academic activities through the SciDAR repository, publishing works on vehicle dynamics, tribology, and alternative fuel systems. His interdisciplinary approach bridges mechanical engineering with environmental science and digital technologies.

• Artificial Intelligence
• Computer Science
• Fuzzy Logic
• +5 more

Đorđe Obradović is affiliated with Singidunum University's Faculty of Informatics and Computing. He holds a PhD from the University of Novi Sad's Faculty of Technical Sciences in Electrical Engineering and Computing (2003–2011), following a Master's (1998–2003) and Bachelor's (1992–1997) in the same field. His research focuses on artificial intelligence, fuzzy logic, image processing, and geospatial systems. Recent work includes air quality assessment frameworks using fuzzy sets, medical image analysis, and GIS applications. He has authored/co-authored numerous journal articles and conference papers, contributing to domains like neural networks, environmental modeling, and computer vision. His books include Artificial Intelligence: Theory and Applications (Springer, 2021) and Intelligent Systems: Models and Applications (Springer, 2013). Collaborations span academic and industrial projects, emphasizing practical applications of computational methods in real-world scenarios. Publications highlight contributions to fuzzy geometry, road lane detection, and SLAM algorithms, with a strong emphasis on interdisciplinary applications. Obradović's work often integrates machine learning with spatial data analysis, reflecting his expertise in both theoretical and applied computer science. His current research trends align with computational solutions for environmental monitoring, medical diagnostics, and geospatial challenges. No formal awards or grants are explicitly mentioned, though his extensive publication record and book authorship underscore his academic impact. His involvement in Singidunum's informatics faculty suggests teaching and mentoring roles, though specific advisees are not listed here. Projects include software systems for GIS-based simulations, sensor networks, and educational platforms.