Find Academic Supervisors

• Algorithms
• Computational Geometry
• Data Structures
• +5 more

Erik Demaine is a Professor at the Massachusetts Institute of Technology (MIT), holding positions in the Department of Electrical Engineering and Computer Science (EECS) within the School of Engineering. He is also affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Theory of Computation (TOC) group. His research focuses on algorithms, computational geometry, data structures, complexity theory, discrete mathematics, and interdisciplinary areas like biology (protein folding) and computational archaeology. He is renowned for his interdisciplinary work, blending mathematics, computer science, and art. Notable contributions include the Carpenter's Rule Theorem, which proves planar linkages can be straightened without intersection, and foundational work on folding and unfolding problems. His collaborative approaches, including 'supercollaboration,' emphasize open problems and interdisciplinary methods. He has been featured in documentaries such as NOVA's The Origami Revolution and authored influential books like Geometric Folding Algorithms and Games, Puzzles, and Computation . Awards: MacArthur Fellowship (2003) Tetris Master Key Projects: Curved-crease sculptures in MoMA's permanent collection Virtual Glass software for glass design Origami-based computational models Demaine's work bridges academia and art, with notable exhibitions at the Museum of Modern Art (MoMA) and collaborative projects with his father, Martin Demaine. His research often explores playful yet rigorous computational challenges, reflecting his belief in the interplay between theory and creativity.

Mladen VeinovićView profile

Professor

• Cybersecurity
• Network Security
• Data Protection
• +7 more

Mladen Veinović is a Professor at Singidunum University, affiliated with the Faculty of Informatics and Computing. He holds a Doctorate in Systems Theory from the School of Electrical Engineering, University of Belgrade (1996), along with postgraduate and bachelor's degrees in Automatic Control and Electronics from the same institution. His research focuses on cybersecurity, network security, signal processing, and machine learning. Key contributions include work on malware C2 infrastructure, IoT security, steganography, and robust speech processing algorithms. He has authored/co-authored over 40 books and numerous peer-reviewed articles in journals like IEEE Access, Sensors, and Computers & Security. Veinović is actively involved in education technology, developing secure communication tools and problem-based learning approaches. His work spans cloud forensics, blockchain applications, and pandemic-era teaching strategies. He has been a key contributor to projects like the WIDE web development framework and cybersecurity tools for sensor networks. Professional affiliations include Singidunum University's research institute and participation in international conferences like Sinteza and TELSIKS. His expertise bridges theoretical research and practical applications in digital systems, IoT, and information security.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +8 more

Marina Marjanović is a Professor at Singidunum University's Faculty of Informatics and Computing, specializing in Artificial Intelligence, Machine Learning, and Computer Vision. Her research focuses on neural networks, adversarial attacks, emotion recognition, and healthcare applications. She holds a PhD in Signals and Systems from Universidad Politécnica de Madrid (2007) and has extensive publications in top-tier journals and conferences. Her work spans diverse areas: developing datasets for Serbian Sign Language, addressing demographic bias in age estimation models, and optimizing algorithms for phishing detection. Notable contributions include hybrid machine learning models for early disease diagnostics and adversarial attack vulnerability analysis on CNNs. Research Highlights : Age Estimation Bias Mitigation Chest X-ray Abnormalities Detection Phishing Website Detection Healthcare 4.0 Applications Marjanović collaborates on EU-funded projects and actively participates in international conferences like IEEE FG and Sinteza. She chairs Singidunum’s AI research group, focusing on explainable AI and ethical machine learning practices.

• Information Systems Analysis and Design
• Business Intelligence Systems
• Data Governance
• +5 more

Angelina Njeguš is a Full Professor at Singidunum University. She holds a B.Sc. in Information Systems from the Faculty of Organizational Sciences (FON), University of Belgrade (1994), an M.Sc. in Industrial Engineering (FON, 1999), and a Ph.D. in Virtual University System Development from the Faculty of Business Studies, Belgrade (2003). Her expertise spans Information Systems Analysis, Business Intelligence, Data Governance, and Big Data Analytics. Recently, her research focuses on Affective Computing, HCI, and pattern recognition using Deep Learning. She has published over 100 articles and authored seven books, including titles on software design patterns and information systems in tourism. Research Interests: - Advanced analytics and AI-driven systems in hospitality - Cybersecurity in cloud environments - Emotion recognition through multimodal data analysis - Agile methodologies in software development Key Collaborations: - IBM (AI and IDR consulting) - Microsoft/Oracle partnerships in certification and internship programs Her work bridges academia and industry, with contributions to: - Blockchain applications in tourism - Employee satisfaction models using ML - Digital transformation strategies in hospitality

• Cryptography
• Information Security
• Educational Technology
• +3 more

Violeta Tomašević is a Professor at Singidunum University, Faculty of Informatics and Computing. Her research focuses on cryptography, cybersecurity, educational technology, and data analysis. She holds a Doctorate in Cryptanalysis from the School of Electrical Engineering, University of Belgrade (2005), and has extensive experience in software development for educational and security applications. Her work includes designing anti-plagiarism systems, optimizing RFID protocols, and developing tools for student progress analysis using machine learning. She has authored multiple textbooks on computer architecture and programming, and her contributions span over 50 peer-reviewed articles and conference papers. Education: Doctoral studies: Cryptanalysis, School of Electrical Engineering, University of Belgrade (1998-2005) Postgraduate: Digital Signal Processing, School of Electrical Engineering, University of Belgrade (1988-1994) Bachelor's: Electronics, School of Electrical Engineering, University of Belgrade (1983-1988) Research Interests: Cryptography (RC4, RFID security), educational software development, cybersecurity in insurance, and decision support systems. Her work integrates theoretical cryptanalysis with practical applications in industry and academia. Grants & Projects: Led projects on cloud computing in business, investment decision support systems (e.g., FIDES), and virtual labs for computing courses. Collaborated with institutions like the University of Belgrade and industry partners on software tools for risk management and academic integrity. Labs/Teams: Involved in Singidunum's research groups focused on cybersecurity, educational technology, and algorithm optimization. Active in organizing conferences like Sinteza and SITCON.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Dr. Miljan Vučetić holds the title of Principal Research Fellow in Information Technologies at the School of Electrical Engineering, University of Belgrade. He earned his PhD in Computer Science from the University of Belgrade (2013) and has extensive industry experience in roles such as Data Scientist at Drey Corporation, Software Engineer at Vlatacom Institute (2014–), and team leader of its AI group since 2019. His research focuses on artificial intelligence, machine learning, data mining, and fuzzy systems, with over 70 publications and 14 technical solutions. He is a Senior Member of IEEE (2024) and has contributed to FP7, H2020, and COST projects. Education: PhD in Computer Science, University of Belgrade (2009–2013) M.Sc. in Telecommunication, Faculty of Transport and Traffic Engineering, University of Belgrade (2003–2008) High School Diploma, High School Sokolac (1999–2003) Research emphasizes computational intelligence, with notable work on distributed algorithms, fuzzy query engines, and AI applications in telecommunications and smart cities. His articles span topics like consensus protocols, reinforcement learning, and sensor network optimization. He serves as a journal reviewer and conference committee member. Scientific achievements include the IEEE Senior Member status (2024), Best Student Award (2008), and leadership in projects like FP7 and H2020. His technical solutions address spectrum management, vehicle actuation, and disaster preparedness systems. He advises multiple collaborative research groups and leads the Vlatacom AI team.

Ivana IvanovićView profile

Professor

• Mechanical Engineering
• Thermal Science
• Materials Science
• +4 more

Ivana Ivanović is a Professor affiliated with Singidunum University. She holds a Doctorate in Aerospace Engineering from the Faculty of Mechanical Engineering, University of Belgrade (2008), a Master's in Mechanical Engineering from Polytechnique Montréal (2001), and a Bachelor's in Theoretical Mechanics from the Faculty of Mathematics, University of Belgrade. Her research focuses on thermal science, materials science, numerical simulation, and fracture mechanics, with applications in welding processes, material degradation analysis, and aerospace engineering. She has published extensively in peer-reviewed journals and conference proceedings, contributing to advancements in computational mechanics and engineering software development. Her work emphasizes bridging experimental and numerical methods, particularly in analyzing temperature fields during hard-facing processes, crack growth under thermal and mechanical stress, and laser beam welding simulations. Recent contributions include exploring open-source engineering software for numerical simulations and optimizing low Reynolds number airfoils. Ivana has collaborated on projects involving thermo-chemical erosion in gun steel, shock wave dynamics in mortar systems, and environmental impact studies of river temperature changes. Notable collaborations include co-authorship with institutions like Singidunum University and international journals such as Thermal Science and the Technical Gazette . Her research demonstrates a strong interdisciplinary approach, integrating mechanical engineering principles with computational tools to solve complex material and thermal challenges.

• Machine Learning
• Distributed Systems
• Sensor Networks
• +7 more

Miloš Stanković is a Professor at the Faculty of Informatics and Computing, Singidunum University. His research focuses on distributed systems, reinforcement learning, machine learning applications, and sensor networks. He has contributed significantly to areas like multi-agent systems, optimization algorithms, and cybersecurity. Recent work includes distributed sensor network calibration, adaptive consensus algorithms, and AI-driven solutions for healthcare and infrastructure monitoring. Education: Not explicitly listed in provided texts. Likely holds advanced degrees in computer science or electrical engineering based on his publications. Research Interests: His primary areas include distributed multi-agent reinforcement learning, metaheuristic optimization for machine learning models, and real-world applications of AI in healthcare, surveillance, and smart infrastructure. He explores topics like sensor fusion, fault detection in networks, and consensus-based algorithms for decentralized systems. Article Trends: Recent publications emphasize AI-driven solutions (e.g., Parkinson’s disease detection via LSTM tuning, student dropout analysis with explainable AI) and distributed algorithms for multi-agent systems. His work bridges theoretical advancements in reinforcement learning with practical applications in sensor networks and cybersecurity. Grants/Projects: Not explicitly detailed, but involvement in EU-funded projects like Sinteza and SITCON conferences suggests external funding. Labs/Teams: Likely part of Singidunum’s research groups in AI, distributed systems, and cybersecurity. Collaborates with institutions like KTH Royal Institute of Technology (via co-author K.H. Johansson) on sensor networks and control systems.

• Photonics
• Machine Learning
• Quantum Dots
• +4 more

Nikola Djordjević is a researcher affiliated with Singidunum University in Belgrade, Serbia. Holding a PhD in Electrical Engineering from ETH Zurich (2020), his work bridges photonics and machine learning applications. Bachelor's: Faculty of Electrical Engineering, Belgrade (2012) Master's: ETH Zurich (2014) PhD: ETH Zurich (2020) His research focuses on: Colloidal quantum dot photonics Time-resolved luminescence analysis using AI Plasmonic device optimization Nanocrystal structure characterization Advanced semiconductor fabrication techniques Key publication trends show interdisciplinary work across applied physics , computational photonics , and nanomaterials .

• Machine Learning
• Optimization Algorithms
• Computer Networks
• +3 more

Dušan Marković is an academic affiliated with Singidunum University, specifically within the Faculty of Informatics and Computing's Department of Computer Science. His primary role is as a Researcher, focusing on interdisciplinary research areas including machine learning, optimization algorithms, and cybersecurity. He has contributed to both theoretical advancements and practical applications in these fields. His research interests span optimization algorithms (e.g., firefly, monarch butterfly, moth search), their applications in machine learning (e.g., medical data classification), environmental impact assessment in agricultural systems, and educational technology (e.g., virtualization in learning environments). His work often bridges computational methods with real-world challenges in healthcare, urban planning, and disaster management. Notable contributions include improving medical data analysis through hybrid algorithms, assessing thermal comfort in urban areas, and developing cybersecurity measures for network infrastructures. While no specific awards are listed, his active publication record indicates sustained academic engagement. No grants or advising activities are explicitly documented here. He is part of Singidunum University's academic community, contributing to both teaching and research in informatics and computing disciplines.

• Wireless Communications
• Signal Processing
• Neural Networks
• +3 more

Ivan Milovanović is a faculty member at Singidunum University, affiliated with the Faculty of Technical Sciences under the Department of Informatics and Computing. He holds a doctoral degree in Advanced Protection Systems from Singidunum University (2009-2015), following a master's in Contemporary Information Technologies (2008-2009) and a bachelor's in Graphic Design (2004-2008). His research focuses on wireless communications, signal processing, neural networks, and telecommunications, with an emphasis on applications such as direction-of-arrival estimation, stochastic source localization, and channel modeling. He has published extensively in journals like the International Journal of RF and Microwave Computer-Aided Engineering and presented at conferences including TELSIKS and ICEST. Education: PhD: Advanced Protection Systems, Singidunum University (2009–2015) Master's: Contemporary Information Technologies, Singidunum University (2008–2009) Bachelor's: Graphic Design, Singidunum University (2004–2008) High School: Stevan Sremac Gymnasium (2000–2004) Research Interests: His work integrates neural networks with electromagnetic systems to address challenges in wireless communications, including signal localization, noise analysis, and adaptive antenna systems. He explores hybrid models for indoor/outdoor path loss calculation and secure wireless sensor networks for critical infrastructure protection. Recent studies focus on stochastic processes in far-field environments and the design of efficient neural network architectures for real-time signal processing. Publications: Key contributions include papers on DoA estimation using hierarchical neural models, analysis of WSN-FSO systems, and performance benchmarks for DBMS engines. His research bridges theoretical advancements with practical applications in telecommunications and data systems. Labs/Teams: Active in Singidunum University's research groups focusing on telecommunications and neural network applications. Collaborates with institutions like the Faculty of Electronic Engineering in Niš and international partners such as TU Dresden and TU Ilmenau.

• Cybersecurity
• Machine Learning
• Web Development
• +3 more

Nikola Savanović is a researcher and academic at Singidunum University, specializing in cybersecurity, machine learning, and web technologies. He holds a PhD in Advanced Security Systems (2024) from Singidunum University, following a Master's in Contemporary Information Technologies (2016) and a Bachelor's in Informatics and Computing (2013), both from the same institution. He also completed high school in Computer-Aided Design at Politehnika School for New Technologies (2003-2007). His research focuses on intrusion detection systems, machine learning optimization for cybersecurity applications, and adaptive web technologies. He has contributed to educational technology through virtual learning environments and game-based learning. His mathematical work includes fixed point theory in b-metric spaces. He has authored/co-authored multiple books on web design, multimedia systems, and digital marketing. Publications highlight interdisciplinary work: hybrid CNN-XGBoost models for medical diagnostics, metaheuristic algorithms for software defect detection, and cybersecurity innovations in healthcare IoT systems. His articles span computer science, mathematics, and data science, emphasizing practical applications in education and healthcare. Despite no listed awards, his extensive publication record and teaching roles reflect significant academic contributions. He actively collaborates on university projects and advises students in informatics and computing disciplines, though no formal student list is provided.

Milan TairView profile

Researcher

• Computer Science
• Artificial Intelligence
• Web Development
• +3 more

Milan Tair is a researcher at the University of Singidunum, affiliated with the Faculty of Informatics and Computing and the Department of Postgraduate Studies . His academic work spans topics in computer science, web technologies, and optimization algorithms. Doctoral Studies : Electrical Engineering and Computing (2016–2022) Master's Studies : Contemporary Information Technologies (2012–2013) Undergraduate Studies : Programming and Design (2008–2012) His research interests focus on artificial intelligence , web development , cybersecurity , and optimization algorithms , as evidenced by his publications in journals and conference proceedings. Recent publications highlight his work on hybrid metaheuristic algorithms applied to intrusion detection, medical diagnostics, and web quality evaluation. His studies also explore adaptive web technologies, database systems, and mobile computing solutions. Milan has authored or co-authored works on topics including: Optimization of machine learning models (XGBoost tuning, swarm intelligence) Web performance and security (AES encryption, HTTP header licensing) Adaptive web formats (multi-layered image compression) Usability in multilingual web design

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

Eva Tuba is a researcher at Singidunum University , Serbia. Her work focuses on swarm intelligence , metaheuristic optimization , and machine learning applications across domains like medical imaging, wireless sensor networks, and digital forensics. Research Interests: Swarm optimization algorithms (bat, firefly, fireworks), machine learning, image/signal processing, cloud computing, and explainable AI. Publications: 15+ recent papers in journals such as Journal of King Saud University , IEEE Transactions , and Springer volumes, often in collaboration with researchers like Milena Tuba and Nebojsa Bacanin. Her academic contributions include hybridized algorithms for convolutional neural network tuning , medical diagnostics , and environmental monitoring . While she has no listed awards, her work spans both theoretical and applied computational intelligence.

• Information Systems
• Data Warehousing
• Database Management
• +5 more

Dr. Nataša Goјgić is a Professor of Applied Studies at the Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac. Her academic career spans decades of teaching and research in information systems, databases, and technology-enhanced education. Military Technical Faculty in Zagreb (Mechanical Engineering) Master's in Information Systems at Technical Faculty Čačak PhD in Information Systems Integration and Quality Management Dr. Goјgić's research focuses on information systems , data warehousing , and educational technology . Her publications reveal strong interests in: Business Intelligence and OLAP systems Regression algorithms in healthcare E-learning platforms and teaching methodologies Quality Management Systems (QMS) integration Database monitoring and financial technology She has pioneered the use of information systems in academic quality assurance and developed teaching materials for database systems. Her work bridges industrial engineering and modern information technologies.