Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +4 more

Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +5 more

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

Miloš RackovićView profile

Professor

• Artificial Intelligence
• Machine Learning
• Databases
• +4 more

Miloš Racković serves as a full Professor in the Department of Mathematics and Informatics at the University of Novi Sad, Serbia. He maintains active academic engagement through the Laboratory for the development of information systems, with his office located in the Information technologies and systems office (DMI&DF) on the second floor, room 49. Contact is available via telephone (485)-2868 or email rackovic@dmi.uns.ac.rs, and his personal website (http://www.is.pmf.uns.ac.rs/rackovicm/) provides additional resources. His research spans foundational and applied computer science, with seminal contributions in fuzzy database systems including PFSQL query language development and prioritized fuzzy logic for relational databases and XML. He has pioneered deep learning methodologies through innovative classification techniques using negative and missing features in convolutional neural networks. Additional expertise includes high-performance computing implementations of Lattice Boltzmann methods using OpenCL, robotics (symbolic modeling and trajectory planning), and blockchain applications for Industry 4.0 production processes. His sports analytics work applies neural networks to basketball player and referee movement analysis. Analysis of his 2012-2025 publications reveals a strategic evolution toward interdisciplinary applications, particularly in industrial transformation (blockchain-enabled traceability) and sports analytics. His work consistently bridges theoretical computer science with practical implementations, demonstrating increasing focus on real-world problem solving while maintaining strong foundations in database theory and computational methods. Professor Racković leads the Laboratory for the development of information systems, which focuses on advancing information system methodologies through formal modeling extensions (including Petri net innovations) and practical implementations for uncertainty management. The laboratory's work spans from foundational research in fuzzy logic systems to applied projects in high-performance computing and blockchain integration, fostering innovation in information technology development.

• Control Systems
• Machine Learning
• Robotics
• +6 more

Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.

Milan BrkljačView profile

Assistant Professor

• Marketing
• Consumer Behavior
• Digital Marketing
• +4 more

Milan Brkljač is an Assistant Professor at Alfa BK University, affiliated with the Faculty of Finance, Banking and Auditing. He holds a PhD from the University of Novi Sad (2021), with research focused on marketing, consumer behavior, and sharing economy dynamics. His professional journey includes roles at NIS GAZPROM NEFT, Digipuls d.o.o., and the University of Modern Business in Belgrade. Education: BSc/MSc from University of Novi Sad (Faculty of Economics in Subotica), PhD (2021) on 'Determinants of Intentions to Use Sharing Economy Services among the Millennial Generation'. Research Interests: Marketing strategies in digital age, consumer decision-making, sharing economy, blockchain applications in agriculture, and CSR in banking. He has conducted study visits to TU Munich and DAAAM International Vienna, and holds certifications in Digital Marketing (Google Ads) and ECDL. Awards: Ministry of Education Scholarship (2008-2010), FESTO Scholarship (2018), and first prize for promotional video (2010). Active in professional communities like Serbian Marketing Association (SeMa). Grants/Advising: Multiple academic publications, involvement in employer branding and talent recruitment studies. Fluent in English and German, with piano skills from Isidor Bajić Music High School.

Aleksandar JevremovićView profile

Professor

• Human-Computer Interaction (HCI)
• Cybersecurity
• Internet of Things (IoT)
• +6 more

Aleksandar Jevremović is a Full Professor at the Faculty of Informatics and Computing, Singidunum University (Belgrade, Serbia), and holds multiple academic and professional roles. He is the Serbian representative at the UNESCO IFIP Technical Committee on Human-Computer Interaction since 2018. He has served as Vice-Dean of his faculty (2015–2018) and held visiting professorships at institutions like Ss. Cyril and Methodius University (North Macedonia) and Tallinn University (Estonia). His research focuses on cybersecurity, IoT, AI, and e-learning innovation. Education and Affiliations: External Researcher at the Mathematical Institute of the Serbian Academy of Sciences and Arts Visiting Scholar at Cyprus Interaction Lab (Cyprus University of Technology) Alumni/Postdoc Researcher at Tallinn University's HCI Group Member of IEEE and the Informatics Association of Serbia Research Interests: Jevremović’s work spans cybersecurity (e.g., intrusion detection, secure IoT protocols), human-computer interaction (HCI), AI-driven education tools, and neurotechnological applications like EEG-based assessment systems. He emphasizes practical solutions for digital safety, such as children’s online protection and cryptographic key generation from biometric data. Grants and Projects: Member of the External Advisory Committee for the EU-funded ONTOCHAIN project (2022–2023) Mentor for training schools like AAPELE Training School and NET4Age-Friendly initiatives Trainer in IoT, cybersecurity, and health promotion programs across Europe Labs and Teams: He collaborates with interdisciplinary teams on projects like CASPER (Children Agents for Secure and Privacy Enhanced Reaction) and led the development of WIDE, a collaborative web development education platform.

• Environmental Science
• Sustainable Development
• Ecological Conservation
• +3 more

Vuk Gajić is an Assistant Professor at the Faculty of Applied Ecology, Singidunum University, where he has held academic roles since 2016. His career progression includes positions as a teaching associate (2016), assistant (2019), and current role (2023). He earned a Ph.D. in Environment and Sustainable Development from Singidunum University (2019–2022), following prior studies in environmental protection and risk management at the same institution. Research interests span environmental science, sustainable development, GIS applications, and radiation technology for waste and food treatment. He has contributed to interdisciplinary studies, including soil contamination analysis in Libya, microbial decontamination via ionizing radiation, and machine learning applications for software defect prediction and agricultural weed detection. His work bridges environmental engineering with technological innovation, emphasizing sustainability and ecological conservation. Publications reflect a focus on environmental monitoring, pollution assessment, and eco-technologies. Key themes include GIS-based environmental databases, forest fire prevention through sensor networks, and agricultural waste reuse. His research often integrates quantitative methods with geospatial tools, addressing both local and global environmental challenges. Teaching responsibilities include courses on geodiversity, sustainable development, and natural hazards. He actively participates in academic conferences, contributing to peer-reviewed journals and presenting at events like Sinteza and SETI. Current projects likely explore emerging technologies in environmental management and sustainable practices.

• Machine Learning
• Computer Vision
• Emotion Recognition
• +5 more

Marina Marjanovic is a faculty member at Singidunum University in Belgrade, Serbia, affiliated with the Faculty of Informatics and Computing. She maintains an active research profile with numerous publications spanning machine learning, computer vision, and signal processing applications. Her work demonstrates strong interdisciplinary connections between theoretical AI development and practical implementations in healthcare, security, and digital government services. Dr. Marjanovic earned her Doctoral degree in Signals and Systems from Universidad Politecnica de Madrid in 2007, following a Master's degree in the same field from the same institution in 2005. Her foundational education includes basic studies in Telecommunications at the Faculty of Electrical Engineering in Belgrade, and secondary education at the third Belgrade high school with a focus on natural sciences. This strong technical background has enabled her transition from traditional signal processing to contemporary AI research. Her research interests center around machine learning and computer vision, with significant contributions to emotion recognition, sign language processing, and explainable AI. She has demonstrated particular expertise in developing robust AI models that address real-world challenges such as demographic bias in age estimation systems, vulnerability to adversarial attacks in deep learning models, and accurate medical diagnostics through imaging analysis. Her recent work shows increasing focus on healthcare applications, security implications of AI systems, and making AI more transparent and interpretable. Analysis of her publication trajectory reveals a clear evolution from signal processing foundations toward contemporary AI applications. Her recent work (2022-2024) shows strong emphasis on healthcare AI, security applications, and explainability, often combining traditional machine learning approaches with novel optimization techniques. She frequently collaborates with international researchers across multiple institutions, demonstrating the global reach of her work. Dr. Marjanovic has established herself as a productive researcher with consistent publication output across reputable journals and conferences including IEEE transactions, Springer publications, and specialized AI conferences. Her collaborative approach is evident through her extensive co-authorship network spanning multiple Serbian and international institutions. She actively contributes to the academic community through conference organization (including Sinteza conferences) and participation in research projects at Singidunum University's research institutes. Her work bridges theoretical advancements with practical implementations across multiple domains, demonstrating versatility and impact across the AI research landscape.

Stefan ĆirkovićView profile

Lecturer

• Cybersecurity
• Artificial Intelligence
• Computer Vision
• +3 more

Stefan Ćirković is a Teaching Associate in the Department of Information Technologies at the Faculty of Technical Sciences in Čačak, University of Kragujevac. His office is located at Saint Sava 65, 32102 Čačak, Serbia. He is currently pursuing doctoral studies in Information Technology (2023–present) at the same institution. Education: Doctoral Academic Studies – Information Technology, University of Kragujevac (2023–present) Master of Academic Studies – Information Technology, University of Kragujevac (2022–2023) Basic Academic Studies – Information Technology, University of Kragujevac (2018–2022) Specialized Program – Cybersecurity, University of Kragujevac (2024) Research Focus: Ćirković specializes in applied artificial intelligence with significant work in cybersecurity, medical imaging, and network systems. His research demonstrates strong emphasis on practical AI implementations including real-time object detection (YOLO algorithm), anomaly detection in networks, federated learning for healthcare applications, and cybersecurity techniques using large language models. His work frequently intersects with medical informatics, particularly in diagnostic applications. Publication Trends: Recent publications (2024–2025) show predominant focus on AI/ML applications across multiple domains. Cybersecurity remains a core theme with novel approaches to web application security and network anomaly detection. Medical applications feature prominently, especially in diagnostic imaging (kidney stones, skin cancer) and hemodialysis optimization. Methodologically, there's consistent exploration of deep learning architectures and real-time systems. Laboratory Affiliations: Associated with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak. Utilizes institutional resources including the Moodle e-learning platform and Microsoft 365/Teams infrastructure for academic activities.

• Computer Engineering
• IoT Systems
• Machine Learning
• +4 more

Dijana Stojić is an Assistant at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac. She has held this position since January 2018 while pursuing her doctoral studies in Electrical and Computer Engineering with a specialization in Computer Engineering, which she began in 2021. Her academic journey began with excellent results at the 'Vuk Karadžić' elementary school in Čačak, followed by the Technical School in Čačak where she majored in computer electrical engineering. She earned her Bachelor's degree in Computer Engineering in 2011 (average grade 9.37) and Master's degree in 2012 (perfect average grade 10.00) from the Faculty of Technology in Čačak. Her Master's thesis, 'Software Support for Active RFID Localization,' was supervised by Prof. Dr. Siniša Ranđić. Dijana's research spans multiple domains within computer engineering, with recent publications focusing on practical applications of technology in diverse fields. Her work demonstrates strong expertise in IoT systems, machine learning applications, blockchain technology, and human-computer interaction. She has published extensively on topics including earthquake detection using mobile phones, predictive models for identifying students with difficulties in online learning, medical applications like ECG monitoring with Arduino, agricultural engineering applications, and infrastructure monitoring systems. Award for best first-year student at Faculty of Technology in Čačak Award for best second-year student at Faculty of Technology in Čačak Her professional activities include serving as Student Vice-Dean at the Technical Faculty in Čačak from December 2010 to September 2012, participating in the TEMPUS SIGMUS project, and completing an internship at D.O.O. DOCUS from June 2013 to June 2014. She was a scholarship holder of the Ministry of Education, Science and Technological Development from April 2013 to March 2017 and has been involved in significant research projects including 'Development and modeling of energy efficient, adaptable, multiprocessor and multisensory electronic low-power systems' (TR 32043) and the 'German-Serbian Collocation Dictionary for German Language and Teaching' (DeSKoll).

Miloš StojmenovićView profile

Professor

• Computer Vision
• Machine Learning
• Image Processing
• +6 more

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Artificial Intelligence
• Internet of Things
• Computer Vision
• +4 more

Miloš Dobrojević serves as a Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic foundation includes doctoral, postgraduate, and bachelor studies at the University of Belgrade's Faculty of Mechanical Engineering. His research spans Artificial Intelligence , Internet of Things , and Computer Vision with emphasis on practical applications. Key focus areas include metaheuristic optimization of neural networks for tasks like crop yield prediction, medical diagnostics, and cybersecurity. Recent work demonstrates strong industry relevance in precision agriculture, drone detection systems, and sustainable homestead automation. Analysis of his 15 most recent publications reveals a consistent pattern of applying modified metaheuristics to enhance deep learning models across diverse domains. His work bridges theoretical AI advancements with real-world implementations in agriculture, healthcare, and energy systems. Scientific Contributions: Author of 3 textbooks including 'Veb informacioni sistemi' (2024) and 'Veb programiranje' (2021) Published 40+ journal/conference papers between 2003-2025 Active contributor to Springer book chapters on AI applications Dobrojević's work demonstrates strong practical orientation with projects in Serbian renewable energy transition, flood prevention systems, and municipal e-government solutions. His recent collaborations with researchers like N. Bacanin and M. Zivkovic show consistent output in high-impact journals. Current projects focus on generative AI for medical data and computer vision for agricultural optimization. His laboratory work centers on IoT-based monitoring systems for environmental applications, particularly water management in mountainous regions. Ongoing research explores drone detection networks and waste classification systems using advanced computer vision techniques.

• Sensor Technologies
• Innovation Management
• Entrepreneurship Ecosystems
• +3 more

Tamara Papić is an Assistant Professor at Singidunum University in Belgrade, specializing in Innovation Management and Entrepreneurship. She holds a Ph.D. in Sensor Technologies from Jožef Stefan International Postgraduate School (Slovenia) and a Master's in Quality Management from Fakultet organizacionih nauka. Her research focuses on sensor technologies, dyslexia detection through eye-tracking, and the application of AI in education and marketing. Key academic contributions include developing sensor-based systems for analyzing reading patterns in children with dyslexia, leveraging machine learning models like VGG16 and autoencoders. She co-founded BabyFM, a startup creating smart thermoregulation devices for infants using sensors and mobile apps. Her work has been supported by grants from Innovation Forum Cambridge, Star Tech, and Serbia’s Innovation Fund. Professionally, she produced RTV Serbia’s entrepreneurial series 'My Hero' and 'Golden Idea,' and organized national startup competitions. She chairs strategic conferences like Smart City SEE and Belgrade Strategic Dialogue, promoting innovation ecosystems. Her cross-disciplinary projects bridge engineering, education, and business strategy.

• Artificial Intelligence
• Neural Networks
• Image Processing
• +3 more

Petar Biševac is a Lecturer at the Faculty of Informatics and Computing within Singidunum University , Belgrade. His academic work focuses on applied artificial intelligence and machine learning. Education : Not explicitly detailed in available texts. Research Interests : Spanning Artificial Intelligence , Neural Networks , Image Processing , and Metaheuristic Optimization , his research emphasizes hybrid AI systems for biomedical signal processing, cryptocurrency forecasting, and image analysis. Publications : Recent works include anomaly detection in ECG/EEG signals, vehicle damage recognition, and Ethereum price forecasting using LSTM and metaheuristic algorithms. Collaborations : Frequently works with researchers like Nebojsa Bacanin, Milan Zivkovic, and Predrag Spalević. Contact : Email pbisevac@singidunum.ac.rs

• Control Systems
• Machine Learning
• Electrical Engineering
• +4 more

Marko Tanasković (born December 6, 1986) is a researcher at Singidunum University with a PhD in Information Technology and Electrical Engineering from ETH Zurich (2015). His academic background includes master's (ETH Zurich, 2011) and bachelor's (University of Belgrade, 2009) studies in Electrical Engineering. Doctoral studies: Information Technology and Electrical Engineering, ETH Zurich (2011-2015) Master studies: Information Technology and Electrical Engineering, ETH Zurich (2009-2011) Basic studies: Electrical Engineering, University of Belgrade (2005-2009) Tanasković's research focuses on control systems , predictive modeling , and optimization algorithms for mechanical and electrical systems. His work addresses adaptive model predictive control (MPC), sensorless motor positioning, and data-driven approaches for nonlinear systems. Recent publications (2018-2024) demonstrate expertise in: Embedded control systems (rotor polarity detection) Drone forensics and autonomous navigation Industrial automation (LabVIEW applications) Biomedical sensor development ('Smart Anklet') Machine learning optimization (firefly algorithm)