Mladen Vidović is a researcher at the Faculty of Informatics and Computing, Singidunum University. His work focuses on artificial intelligence, machine learning, and applied informatics, with a particular emphasis on synthetic dataset generation, remote rendering, and educational technology. Articles : 12 recent publications covering AI applications, semantic segmentation, and data science Research Interests : Synthetic data creation, machine learning workflows, and interactive software systems His research outputs include collaborations on tools like Autonomous Grader for educational assessment and Microraptor Gui for remote rendering monitoring. Notable projects involve leveraging Kohonen Self-Organizing Maps for anomaly detection and developing end-to-end learning approaches for stereo vision tasks. While no explicit awards or student advisories are documented in the available texts, his publications indicate active participation in academic conferences such as Sinteza and ICIST, contributing to advancements in AI and informatics.
Andriana Gaborović is a Lecturer at the Department of Information Technologies , Faculty of Technical Sciences, University of Kragujevac , Serbia. She holds a Master of Information Technology Engineer degree (2021) and is currently pursuing her PhD in Information Technology at the same faculty. Bachelor of Science in Organizational Sciences (2018), Faculty of Organizational Sciences, Belgrade Master of Information Technology Engineer (2021), Faculty of Technical Sciences, Čačak Her research focuses on Information Technologies and Systems , with expertise in: Business Intelligence : Data warehousing, machine learning applications Cloud Computing : Agile portfolio management, cloud analytics Data Science : Regression modeling, sentiment mining, demographic forecasting Recent publications highlight trends in cloud-based systems , predictive analytics for demographics and education, and IoT standards . She actively contributes to accreditation processes and conference organization , including TIE2022 and TIE2024. Awards include the Vuk’s Diploma and Borko Nikitović Foundation Award for academic excellence. She served as Secretary of the Department of Information Technologies (2023–2024).
Аnđeliјa Mitrović is an Associate Professor in the Department of Mehatronics at the Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. Her office is located at 65 Svetog Save St. in Čačak (Office No. 136), and she can be contacted via phone (+381 32/302-792) or email (andjelija.mitrovic@ftn.kg.ac.rs). Education PhD in Mechanical Engineering (2016) from Faculty of Technical Sciences Novi Sad with dissertation: "Modeling of Cutting Processes" MSc in Information Technology (2010) from Technical Faculty Čačak with thesis: "Implementation of Virtual Environment in MOODLE LMS for Collaborative Learning of 3D Modeling" Diploma in Mechanical Engineering (1996) from Faculty of Mechanical Engineering Belgrade Diploma in Technique and Informatics (2006) from Technical Faculty Čačak Research Focus Her research spans mechanical engineering with emphasis on metal cutting processes, CAD/CAM systems, and manufacturing technology. She has significant expertise in finite element analysis of machining operations, thermal modeling in cutting processes, and the development of virtual learning environments for engineering education. Recent work integrates AI techniques for process optimization. Publication Trends Her 15 most recent publications (2014-2024) show strong focus on computational modeling of manufacturing processes, particularly thermal analysis in milling/turning operations using FEM. Since 2018, she has increasingly explored educational applications of virtual reality and 3D environments. Recent works incorporate machine learning for predictive modeling in machining. Professional Experience 2021-present: Associate Professor, Faculty of Technical Sciences Čačak 2017-2020: Professor of Vocational Studies 2014-2017: Lecturer 2011-2014: Assistant Professor 2006-2011: Professional Associate 2000-2003: Tool Design Engineer at Cutting Tools Factory Čačak 1997-2004: Mechanical Engineering Professor at various technical schools
Vuk Gajic is an Assistant Professor at the Faculty of Applied Ecology within Singidunum University , Serbia. He has been employed at the university since 2016 in progressive roles including teaching associate (2016), assistant (2019), and assistant professor (2023). Doctoral Dissertation : Environment and Sustainable Development (2019-2022) Master's Degree : Sustainable Development in the Environment (2016-2019) Undergraduate Degree : Environmental Risk Management (2015-2016) Specialization : Environmental Protection (2010-2014) His research focuses on environmental sustainability , ecology , and applied artificial intelligence for environmental monitoring. Key areas include: Ionizing radiation applications in environmental protection Geospatial analysis for pollution mapping AI/ML optimization via metaheuristics Sustainable agriculture practices Renewable energy forecasting Disaster risk management Recent publications highlight his interdisciplinary work at the intersection of environmental science and computational technologies , particularly in radiation-based remediation and AI-driven sustainability solutions. Current affiliations : Assistant Professor, Singidunum University Faculty of Applied Ecology Postgraduate Studies Department
Ivan Radosavljevic is a researcher at Singidunum University's Department of Postgraduate Studies, actively contributing to the fields of Artificial Intelligence, Machine Learning, and Software Engineering. His work spans diverse applications including environmental monitoring, educational technology, medical diagnostics, and geospatial systems. Research interests focus on AI-driven solutions for air quality assessment, synthetic dataset generation for semantic segmentation, real estate price prediction from internet ads, and medical imaging analysis. He has developed software tools for remote rendering monitoring, automated grading, and eye-tracking-based code interaction studies. His publications demonstrate expertise in integrating fuzzy logic with GIS for environmental applications, creating innovative educational technologies, and applying machine learning to healthcare diagnostics. Ivan collaborates with colleagues across multiple domains while maintaining a strong focus on practical implementations of artificial intelligence.
Miloš Pavković is a researcher affiliated with Singidunum University in Serbia. He holds a doctoral degree in Software Engineering from the Faculty of Electrical Engineering, earned after 9 years of study (2012–2021), and a master’s degree in the same field from the same institution (2004–2009). His work spans artificial intelligence, software engineering, environmental modeling, and educational technology. Education : Basic studies in Software Engineering (Faculty of Electrical Engineering, 2004–2009); Doctoral studies in Software Engineering (Faculty of Electrical Engineering, 2012–2021). Pavković’s research focuses on AI applications, including recurrent neural networks for pollution forecasting, metaheuristic optimization, and large language models (LLMs) for web scraping. He has also contributed to software engineering tools for forum crawling, cybersecurity techniques for SQL injection detection, and computer vision applications in agriculture. His publications since 2013 reflect expertise in internet data retrieval, educational technology, and cross-disciplinary applications of AI in environmental and agricultural contexts. Recent works (2025) highlight advancements in pollution prediction and LLM-driven automation.
Nikola Savanovic serves as an Assistant Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic work centers on cybersecurity, machine learning, and IoT systems within the Department of Informatics and Computing. Research Focus: His primary fields include cybersecurity (particularly intrusion detection in healthcare IoT systems), metaheuristic optimization of machine learning models, web technologies, and healthcare informatics. His recent work demonstrates strong interdisciplinary connections between computer science, medical technology, and financial systems. Publication Trends: Analysis of his 15 most recent articles reveals a dominant focus on cybersecurity applications (40%), machine learning optimization (30%), and healthcare technology (20%), with growing emphasis on metaheuristic algorithms for solving complex security and diagnostic problems. His work frequently appears in high-impact journals like Mathematics and Sustainability . Academic Background: PhD in Advanced Protection Systems (2016-2024), University of Singidunum Master's in Contemporary Information Technologies (2013-2016), Singidunum University Bachelor's in Informatics and Computing (2007-2013), Singidunum University Mechanical Technician for Computer Engineering (2003-2007), Polytechnic High School - School for New Technologies Authorship: He has co-authored 5 academic books including Web Design and Multimedia Systems (2023) and Knowledge Discovery in the Cyberspace (2017), demonstrating expertise in both theoretical and applied computing fields. His collaborative research spans multiple international institutions with consistent publication output since 2013. Technical Leadership: His conference presentations reveal active involvement in Serbia's academic technology community through events like IcETRAN and SINTEZA conferences, focusing on practical implementations of cybersecurity frameworks and educational technology solutions.
Nemanja Milošević is an Assistant Professor at the Department of Mathematics and Informatics , Faculty of Sciences, University of Novi Sad, Serbia. He actively contributes to open-source communities as a Fedora Project Developer & Ambassador and maintains a personal blog related to academic topics. PhD in Computer Science Specializes in Deep Learning, Federated Learning, and Edge Computing Lectures courses: Deep Learning, Distributed Deep Learning, Business Software Development, Scientific Programming, and Mobile Application Development Research focuses on neural network architectures, system security, and toxicology applications His research spans multiple domains including: Deep Learning optimization techniques Industrial IoT security frameworks Big Data analytics across manufacturing and banking sectors Toxicology modeling with machine learning Edge computing resource management Software quality analysis methodologies Recent publications demonstrate expertise in federated learning implementations, negative feature classification, and deep learning applications in toxicology. He maintains active collaborations across Europe and contributes to IEEE and arXiv publications. As part of his academic practice, Nemanja: Develops open-source software solutions Participates in international research workshops Utilizes modern e-learning platforms (PMF Moodle) Engages in cross-disciplinary research projects Contact: nmilosev@dmi.rs | nmilosev@fedoraproject.org
GAO Yihang is a Research Fellow in the Department of Mathematics at the National University of Singapore (NUS), working under the supervision of Prof. Vincent Y. F. Tan. He obtained his Ph.D. in Mathematics from The University of Hong Kong (HKU) in 2024 under Prof. Michael K. Ng and holds a B.S. in Mathematics from Zhejiang University (2020). His educational background: Ph.D. in Mathematics, The University of Hong Kong, 2024 B.S. in Mathematics, Zhejiang University, 2020 GAO Yihang's research spans Scientific Machine Learning, Optimization, Large Language Models, Data Science, and Tensor Computation. His work bridges mathematical theory and machine learning applications, focusing on neural network architectures, optimization algorithms, and generative modeling for scientific computing. Key contributions include theoretical analyses of Kolmogorov-Arnold Networks and efficient transformer frameworks. His publication record (2022-2025) reveals a strong interdisciplinary trajectory across top mathematics journals and AI conferences. Research trends emphasize physics-informed neural networks, transformer optimization, and tensor methods, with notable work on length extrapolation techniques and Wasserstein-based uncertainty quantification. Collaborations frequently involve HKU and NUS researchers. Scientific Awards: No awards listed in the provided information No information is available regarding students advised or research grants. His current work appears integrated within Prof. Tan's research group at NUS, focusing on information-theoretic aspects of machine learning. GAO Yihang maintains active engagement with computational mathematics communities through publications in venues like IEEE Transactions and NeurIPS, with recent work addressing transformer efficiency and neural network convergence properties.
Мирослав Поповић is a Full Professor at the University of Novi Sad's Faculty of Technical Sciences (Department of Computer Technology). He holds a PhD (1990), MSc (1988), and BSc (1984) from the same institution. His primary responsibilities include teaching parallel programming, real-time systems programming, and computer networks/communications. Key roles: Head of the Computer Technology Chair Research focuses on embedded systems, real-time systems engineering, and cybersecurity Author of internationally recognized textbook Communication Protocol Engineering (2nd Ed., CRC Press, 2018) Scopus h-index 12 with 36 SCI journal articles and 120+ conference papers Has led numerous industry projects involving real-time systems and embedded solutions. Holds 5 patents and maintains active international collaboration through ORCID (http://orcid.org/0000-0001-8385-149X).
Ненад Четић is an Associate Professor at the Department of Computer Communications, Faculty of Technical Sciences, University of Novi Sad. He obtained his Master's degree in 2007 and PhD in 2007-2019, focusing on embedded systems and voice-controlled IoT solutions. Parallel to his academic role, he serves as Program Director at TTTech Auto, developing software platforms for advanced driver-assistance systems (ADAS) and autonomous driving. Education: Bachelor's: Faculty of Technical Sciences, University of Novi Sad (2006) Master's: "Spectral Expansion Solution for Multichannel Digital Audio Decoders" (2007) PhD: "Wireless Acoustic Sensor Model for Voice Control in Smart Homes" (2007-2019) Research Focus: Computer vision, AI in automotive systems, voice interfaces, distributed system architectures, and embedded systems optimization. His research integrates machine learning into sensor networks and develops real-time distributed systems for automotive applications. He has authored numerous scientific publications on automotive software platforms and ADAS systems.
Suzana Đorđević is an Assistant Professor at the Department of Natural Sciences, Mathematics and Computer Sciences, Faculty of Pedagogical Sciences, University of Kragujevac (Jagodina). Her work focuses on integrating information technologies into education, particularly in informatics and teacher training. She holds a Doctorate in Informatics with Teaching Methodology (2021) and prior degrees in theoretical mathematics (2016–2017). Education: Doctorate: Informatics with Teaching Methodology, Faculty of Pedagogical Sciences, University of Kragujevac (2021) Master’s: Mathematics – Theoretical Mathematics, Faculty of Natural Sciences and Mathematics, Kragujevac (2017) Bachelor’s: Mathematics – Theoretical Mathematics, Faculty of Natural Sciences and Mathematics, Kragujevac (2016) Research Interests: Cryptography in educational collaboration, ICT competency development for teachers, AI applications in STEM education, and gamification strategies for coding education. She actively participates in projects like the "Raising Digital Competencies of Teachers" (PODIKOM) and bilateral collaborations with Montenegro and Slovenia. Professional Contributions: Teaches courses such as Informatics in Education, Programming Methodology, and Web Development. Consultations are held Tuesdays at 2:15 PM. She has authored a textbook on Python programming and contributed to over a dozen peer-reviewed publications and conference proceedings. Awards: None explicitly listed, though her work aligns with recognitions in educational technology innovation.
Borislav Savković is an Associate Professor at the Faculty of Technical Sciences , University of Novi Sad , specializing in Production Engineering . He has held academic roles since 2008, including Teaching Associate, Assistant Professor, and Secretary of the Chair of Machining. Research Focus : Machining optimization, cutting temperature modeling, tool wear prediction, and sustainable production techniques. Methodologies : Neural networks, fuzzy logic, genetic algorithms, and factorial experimental design. His work spans materials science (ductile iron, aluminum alloys) and precision manufacturing, with publications in journals like Materials , Measurement , and Applied Sciences . He contributed to international conferences and co-authored a textbook on machining technology.
Cvijetin Mlađenović is an Assistant Professor at the Faculty of Technical Sciences, University of Novi Sad , specializing in machine tools, technological systems, and CAD/CAE/CAM integration. His research focuses on machining process optimization, thermal behavior modeling of machine components, and vibration analysis for manufacturing stability. Department: Chair of Computer Aided Technological Systems and Design His work leverages neural networks and machine learning to address challenges in machining parameter optimization and predictive maintenance. Recent publications (2022-2025) highlight applications in Ti-6Al-4V alloy machining, spindle thermal modeling, and small dataset-driven optimization. Key trends in his research include integrating artificial intelligence with traditional manufacturing methods, improving geometric accuracy via laser systems, and advancing hybrid mechanism design for machine tools. Notable projects include stability lobe diagram modeling and development of workpiece manipulation devices (patent 2012).
Prof. Mijodrag Milošević is a Full Professor at the Faculty of Technical Sciences, University of Novi Sad, where he serves as Head of the Chair of Machine Tools, Process Planning, Flexible Manufacturing Systems and Design Processes. With over 160 publications including more than 20 in SCI journals, his research significantly contributes to advanced manufacturing technologies and Industry 4.0 applications. His educational background includes: Bachelor's thesis: "Razvoj programske podrške za realizovanje metodologije za proveru radne tačnosti koordinatnih mernih mašina" (1997) Magister thesis: "Razvoj specijalizovanog CAD/CAPP/CAM rešenja primenom savremenih programskih sistema opšte namene" (2005) PhD dissertation: "Kolaborativni sistem za projektovanje tehnoloških procesa izrade proizvoda baziran na internet tehnologijama" (2012) Prof. Milošević's research focuses on cutting-edge manufacturing technologies with particular emphasis on Process Planning, Manufacturing Optimization, and Virtual Design. His work bridges traditional manufacturing with digital transformation through Industry 4.0 technologies, Smart Manufacturing, and Collaborative Engineering approaches. His expertise spans from fundamental process optimization to advanced applications of artificial intelligence and cloud computing in manufacturing environments. Analysis of his recent publications reveals a strong trend toward smart manufacturing solutions, with increasing focus on AI-driven optimization, digital twins, and cloud-based manufacturing systems. His research has evolved from traditional CAPP systems to sophisticated integration of metaheuristic algorithms, machine learning, and IoT technologies in production engineering, reflecting the broader industry shift toward intelligent, data-driven manufacturing processes. Among his professional recognitions: Certificate of Innovation Consultant in the Innovation Consultants Development Programme Prof. Milošević has participated in over 20 scientific research projects from national and international programs, including those from the Ministry of Education, Scientific and Technological Development of Republic of Serbia, TEMPUS, and CEEPUS initiatives. He has coordinated significant research projects including "Application of collaborative engineering for improving sustainable manufacturing process," "Application of smart manufacturing in Industry 4.0," and "Trends of development and application of the Industry 4.0 model in SMEs." Currently, he is involved in the research project "Application of edge computing and artificial intelligence methods in smart products" and several CEEPUS projects. He previously served as head of the Laboratory of Process Planning, Manufacturing Optimisation and Virtual Design before assuming his current role as head of the Chair of Machine Tools, Process Planning, Flexible Manufacturing Systems and Design Processes, leading a team focused on advancing manufacturing technologies through research and innovation.
