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Sebastian U. Stich is a tenured Professor at CISPA Helmholtz Center for Information Security and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on optimization for machine learning, collaborative learning (distributed, federated, and decentralized methods), efficient optimization techniques, adaptive stochastic methods, and privacy/security in machine learning. Recent appointments include ERC Consolidator Grant 2024 for the CollectiveMinds project. Key contributions in federated/decentralized learning, uncertainty estimation, and communication-efficient optimization. Active in workshop organization, including the Optimization for Machine Learning workshop at NeurIPS 2024. Teaching modern optimization methods at Saarland University (2023-2025). His work has been recognized with awards such as the Google Research Scholar Award (2023) and Meta Privacy-Enhancing Technologies Award (2022) . His research group includes postdocs Dr. Anton Rodomanov and Dr. Rotem Mulayoff, and PhD students Xiaowen Jiang and Yuan Gao.

• Optimization for Machine Learning
• Collaborative Learning
• Federated Learning
• +6 more

Aleksandar Mihajlovic is a researcher and Art Director at Singidunum University, Serbia. With a doctoral degree in Contemporary Business Decision-Making (2022), a master's in Business Economics (2014), and a bachelor's in Computer Graphics and Design (2008), he combines academic rigor with creative leadership in the university's marketing strategy. Doctoral studies: Contemporary Business Decision-Making, Singidunum University (2022) Master studies: Business Economics, Singidunum University (2008–2014) Undergraduate: Computer Graphics and Design, Faculty of Informatics and Management (2005–2008) High school: Robotics and Flexible Production Systems Technician, Polytechnic Academy (1995–1999) His research spans visual communication , digital marketing , and artificial intelligence applications in creative industries. Key contributions include Co-authoring 11 academic papers (2015–2025) on topics like Instagram ad effectiveness, techno-feudalism, and responsive logo design. Developing the scientific research portal 'Singipedia' and international magazine 'SingiLogos'. Participating in 7 global projects including Erasmus+ and TEMPUS initiatives. His scientific awards include the JISA Discobolos Special Award (2010), IT Globus Award (2010), and Grafima Fair Special Award (2025). He serves on the organizing committee for conferences like Sinteza and Sitcon , and has judged marketing competitions while volunteering for NGOs like the City Organization of the Deaf of Belgrade.

• Visual Communication
• Graphic Design
• Digital Marketing
• +5 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.

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

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

Mirjana Kijevčanin is a Full Professor in the Department of Chemical Engineering at the Faculty of Technology and Metallurgy, University of Belgrade. She was elected to this position on January 16, 2013, and maintains an active research and teaching program. Her office is located in the TMF large building, room 28/461, and she can be reached at mirjana@tmf.bg.ac.rs or by phone at 011/3370523. Professor Kijevčanin's research focuses on chemical engineering thermodynamics, particularly high-pressure fluid properties, biodiesel and alternative fuels, process integration, and phase equilibria. Her work involves extensive experimental measurements of volumetric, viscometric, and spectroscopic properties of various binary and ternary mixtures, including esters, alcohols, ionic liquids, and biodiesel components. She has developed new parameters for UNIFAC-VISCO and ASOG-VISCO models and has contributed significantly to understanding molecular interactions in complex fluid systems. Analysis of her publication record shows a consistent focus on thermodynamic properties at elevated pressures, with particular emphasis on biodiesel and petro-diesel blends, alkane-alcohol systems, and poly(ethylene glycol) solutions. Her research bridges fundamental thermodynamics with practical applications in energy efficiency and alternative fuel technologies. The majority of her publications appear in high-impact journals such as The Journal of Chemical Thermodynamics, Fluid Phase Equilibria, and Energy & Fuels. Professor Kijevčanin has supervised numerous doctoral, master's, and undergraduate theses, demonstrating her commitment to education and mentorship. Her students have worked on diverse topics including thermodynamic characterization of multicomponent systems, energy integration of industrial processes, and experimental determination of fluid properties at high pressures. She has also contributed to educational resources, co-authoring the textbook 'Chemical Engineering Thermodynamics' published in 2013. Her teaching responsibilities include courses on thermodynamics, energy integration of processes, and professional practice in chemical engineering.

• Chemical Engineering
• Thermodynamics
• High Pressure Fluid Properties
• +7 more

Dr. Vladimir Vukašinović is an Associate Professor at the Department for Energy and Process Engineering, Faculty of Engineering, University of Kragujevac. His research focuses on energy sustainability, industrial process optimization, and environmental engineering. He actively contributes to advancing renewable energy technologies, waste heat utilization, and strategies for decarbonizing industries, particularly in food processing and urban infrastructure. His work emphasizes practical applications such as heat pump integration, biomass utilization, and energy-efficient building systems. Notable projects include studies on net-zero food production facilities and SWOT analyses for urban energy planning in Kragujevac. He also explores emerging technologies like 3D printing in engineering education and AI-driven energy management solutions for public buildings. Publications from 2023–2025 highlight his contributions to global warming analysis in Serbia, carbon offset strategies, and techno-economic evaluations of energy systems. His interdisciplinary approach bridges engineering, environmental science, and policy, addressing both technical and socio-economic aspects of sustainable development.

• Energy Engineering
• Renewable Energy
• Sustainability
• +3 more

Milan R. Dincic is an Associate Professor at the University of Niš, Faculty of Electronic Engineering, Department of Telecommunications. He holds a PhD in Metrology and Measurement Technology (2017) and a Master’s degree in Telecommunications (2012), both from the same institution. His academic career includes research on quantization techniques, signal processing, and metrology applications. Dr. Dincic’s research focuses on optimizing quantizers for measurement signals, including Gaussian and Laplacian distributions. He has contributed to adaptive quantization, lossless coding algorithms, and hybrid systems integrating codecs like ITU-T G.711. His work bridges theoretical signal processing with practical applications in telecommunications and sensor systems. His publications (26 journal articles) emphasize quantizer design, image sampling, and compression techniques. He co-authored the textbook "Sensors in Vehicles" (2014). Current projects include national/international collaborations in metrology and measurement technology. His lab focuses on advancing signal processing methodologies for precision measurement systems.

• Metrology and Measurement Technology
• Signal Processing
• Data Compression
• +3 more

Luis Barba is a Research Fellow in the Machine Learning and Optimization group at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, working under Professor Martin Jaggi. He completed his PhD through a cotutelle program between Carleton University, Ottawa and Université Libre de Bruxelles, Brussels, supervised by Professors Stefan Langerman, Jit Bose, Pat Morin and Vida Dujmović. Prior to that, he earned his master's degree at Universidad Nacional Autónoma de México (UNAM) under Professor Jorge Urrutia. Dr. Barba's research spans computational geometry, algorithms, graph theory, and more recently, machine learning and optimization. His work addresses fundamental problems in geometric data structures, Voronoi diagrams, graph coloring, and distributed learning. He has made significant contributions to understanding time-space trade-offs in geometric algorithms and developing efficient methods for problems like geodesic Voronoi diagrams and dynamic graph coloring. Dr. Barba's publication record demonstrates a clear evolution from theoretical computational geometry to practical applications in machine learning. Early in his career, he focused on fundamental geometric problems including linear-time algorithms for geodesic Voronoi diagrams and efficient convex hull computation in polygonal domains with obstacles. More recently, his work has shifted toward machine learning, where he has developed novel optimization techniques for distributed and federated learning settings, including implicit gradient alignment methods and multilayer lookahead approaches. Dr. Barba has published extensively in top-tier conferences and journals including Symposium on Computational Geometry (SoCG), Canadian Conference on Computational Geometry (CCCG), Algorithmica, and Discrete and Computational Geometry. His collaborative work demonstrates strong connections across the computational geometry and algorithms communities, with frequent co-authorship with leading researchers in these fields. Throughout his career, Dr. Barba has maintained a consistent focus on algorithmic efficiency and computational complexity, whether addressing theoretical geometric problems or practical machine learning challenges. His work exemplifies how deep theoretical insights can inform practical computational approaches across different domains of computer science.

• Computational Geometry
• Algorithms
• Graph Theory
• +4 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.

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

Jelena Nikolic is an Associate Professor in the Department of Telecommunications at the Faculty of Electronics, University of Niš, Serbia. She has been a core faculty member since earning her PhD in 2011 and was promoted to Assistant Professor in 2014 and Associate Professor in 2020. Her academic work is deeply rooted in signal processing, quantization theory, and data compression, with expanding interests in machine learning applications for communications. Research Interests: Her primary research areas include quantization , data and signal compression , approximations of the Q-function , and neural network weight compression . She applies these to telecommunications, digital signal processing, and emerging AI-driven communication systems. Her work bridges theoretical analysis with practical implementation, particularly in companding quantizers, polar quantization, and adaptive coding schemes. Publication Trends: Her recent publications (2020–2022) show a clear evolution toward integrating machine learning with classical signal processing, especially in post-training quantization of neural networks and AI-based compression. Earlier works (2013–2018) focus on theoretical foundations of quantization for Gaussian and Laplacian sources, with rigorous mathematical modeling and performance analysis. The consistent use of terms like 'companding', 'scalar quantizer', 'Gaussian source', and 'SQNR' reflects a strong foundation in information theory and digital communications. Scientific Awards: Prof. Dr. Ilija Stojanović Award (2017) – Best professional work in telecommunications Advising and Grants: She has mentored 2 master’s and 2 diploma students. She has participated in 10 major research projects funded by the Ministry of Education and Science of Serbia, Erasmus+, Norwegian Ministry of Foreign Affairs, and the Science Fund of Serbia, including projects on software-defined radio, dialogic systems, and AI-based quantization (Com-in-AI). She has also served on numerous academic committees for thesis defense, hiring, and academic evaluation across multiple institutions. Labs and Teams: While no specific lab name is mentioned, her involvement in experimental and computational work—such as laboratory exercises in acoustics, signal processing, and VoIP, as well as MATLAB-based practicals—indicates active participation in teaching and research labs at the Faculty of Electronics. She has collaborated extensively with Prof. Zoran Perić and others in the telecommunications group, forming a strong research team focused on quantization and compression.

• Signal Processing
• Telecommunications
• Quantization
• +12 more

Дину Драган is a Professor at the Department of Applied Computer Science, Faculty of Technical Sciences, University of Novi Sad. His academic journey includes a BSc (2003), MSc (2008), and PhD (2013) in Computer Science and Informatics, with a focus on medical imaging, data compression, and multimedia systems. He has been affiliated with the university since 2004, progressing from Assistant Lecturer to Associate Professor (2014). Research interests include compression techniques in healthcare systems, PACS implementation, and multimedia technologies. He has contributed to international journals like Computer Science and Information Systems as an editor and reviewer, and his work on JPEG2000 compression in DICOM standards has been impactful. He was awarded the FESTO Young Research Scholarships (2008) for research presented at the DAAMA International Symposium. Teaching responsibilities include courses on Human-Computer Interaction, Multimedia Systems, Data Compression, and Programming Languages. He actively participates in academic conferences and editorial boards, emphasizing interdisciplinary collaboration in technical and medical informatics.

• Medical Imaging
• Data Compression
• Multimedia Systems
• +3 more

Prof. Dr. Snezana Stanković is a Full Professor at the Department of Textile Engineering within the Faculty of Technology and Metallurgy, University of Belgrade . Appointed in 2022, her work focuses on textile comfort, sustainable materials, and functional clothing design. She teaches courses like "Clothing Construction" and "Thermal Comfort Optimization." Key Research Areas: Textile Engineering, Material Science, Sustainable Textiles Notable Collaborations: Milada Novaković, Dušan Popović, Goran Poparić Her recent publications emphasize eco-friendly cellulose textiles, UV protection, and hemp-based materials. She has mentored 11 students across Basic, Master's, and Doctoral studies, including Ana Mićković (2024) on UV protective hemp textiles and Stefana Milosavljević (2024) on fluid management in knits. Despite her 2000 paper on metal fibers in yarns, no explicit scientific awards are documented in the provided text.

• Textile Engineering
• Material Science
• Clothing Technology
• +3 more

Srđan Trifunović is an Assistant Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad. His research focuses on fluid-structure interaction, thermoelasticity, and nonlinear partial differential equations, with applications in mechanical and applied mathematics. He holds a Ph.D. and is affiliated with the Functional Analysis, Geometry and Topology research group. Education: Doctorate in Mathematics (Ph.D.) His research interests include the theoretical analysis of contact problems in fluid-structure systems, thermomechanical coupling in elastic materials, and the development of weak solution methodologies for complex multiphysics systems. Recent work explores time-periodic solutions in viscoelastic beam interactions and heat exchange dynamics in coupled fluid-plate systems. His articles (2020–2025) address fluid-structure interaction across compressible/incompressible fluids and thermoelastic plates/beams, emphasizing existence proofs, regularity, and asymptotic behavior. He has also contributed to damping analysis in wave equations and contact mechanics. No scientific awards or grants are explicitly listed in the provided data. He advises no students in the given texts. His consultations are conducted via email.

• Partial Differential Equations
• Fluid-Structure Interaction
• Thermoelasticity
• +3 more

Vladimir Zlokolica is a researcher affiliated with Singidunum University, holding a PhD in Telecommunications and Information Processing from Ghent University (2006) and a Master's degree from the same institution (2005). He obtained his Bachelor's degree in Electrical Engineering from Novi Sad (2001). His research focuses on Medical Imaging , Computer Vision , and Automotive Perception Systems , particularly in advanced image processing techniques for 3D medical imaging and motor vehicle environmental modeling. His work includes segmentation algorithms for cardiovascular diagnostics and innovative methods for automotive image generation and enhancement. Publications from 2017-2020 demonstrate expertise in 3D image analysis , medical applications , and automotive vision systems . Notable contributions include techniques for left atrial appendage segmentation, vessel delineation, and quality metrics for 3D visual content. Despite no explicit scientific awards mentioned, his collaborations with institutions like Ghent University and Singidunum University highlight his academic engagement.

• Medical Imaging
• Computer Vision
• Image Processing
• +3 more

Dr. Jelena R. Jovanovic is an Associate Professor at the Faculty of Electronics in Niš, University of Niš, within the Department of Metrology and Measurement Technology. She holds a PhD (2016) and a Master's (2008) from the same institution. Her research focuses on sensor linearization, analog circuit design, and measurement systems, with notable contributions to thermistor compensation and ADC optimization. She has authored 10 journal articles in high-impact metrology and measurement journals. Active in academic projects, she currently participates in 1 national and international collaborative initiatives. Her professional career includes roles such as Research Associate (2018) and has consistently contributed to advancing metrological instrumentation techniques. Education: PhD in Metrology and Measurement Technology (2016), Faculty of Electronics, University of Niš Master's in Telecommunications (2008), Faculty of Electronics, University of Niš Research interests emphasize innovative solutions for sensor signal processing, nonlinear system compensation, and precision measurement technologies. Her work bridges theoretical advancements with practical applications in electronics instrumentation and measurement systems. Advising and grants: While specific grant details are not provided, her academic contributions suggest active participation in research funding initiatives. She has collaborated on multiple projects, though student advising details are not explicitly listed here. Labs/Teams: Not explicitly mentioned, but her research areas imply affiliation with metrology and measurement technology laboratories at the Faculty of Electronics.

• Metrology
• Sensor Technology
• Analog Circuit Design
• +3 more