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Xavier GoaocView profile

Professor

• Computational Geometry
• Discrete Mathematics
• Combinatorial Convexity
• +5 more

Xavier Goaoc is a Professor of Computer Science at Université de Lorraine, affiliated with the Department of Computer Science & Engineering at École des Mines de Nancy and the Gamble research team (joint between LORIA and INRIA). His research focuses on algorithms and discrete mathematics, particularly discrete and computational geometry, including convex hulls, intersection patterns, topological generalizations, and geometric transversal theory. University: Université de Lorraine School: School of Engineering Department: Department of Computer Science & Engineering Research Team: Gamble (LORIA/INRIA) Emails: xavier.goaoc@loria.fr , xavier.goaoc@univ-lorraine.fr His research spans computational geometry, combinatorial convexity, geometric transversal theory, and random geometric structures. Key topics include homological minors, order types of point sets, and geometric optimization. His 15 most recent publications highlight advancements in computational geometry algorithms, structural complexity, and topological constraints. Notably, his work has received Best Paper Awards at SoCG 2020, 2018, 2016, and 2012. Administrative roles include heading the computer science & engineering department at Mines Nancy and co-chairing the computer science department of the IAEM doctoral school. He is also a member of the Université de Lorraine's ‘pôle AM2I’ council. Teaching activities encompass courses in algorithms, computer architecture, blockchains, and geometric models for vision, with publications and grants reflecting his interdisciplinary impact in computer science and mathematics.

François PirotView profile

Associate Professor

• Graph Theory
• Combinatorics
• Probabilistic Method
• +4 more

François Pirot is an Associate Professor (Maître de Conférences) at Université Paris-Saclay since September 1, 2021. He conducts research at the LISN laboratory within the GALaC team and teaches at the Faculty of Science of Orsay. PhD in Mathematics (Radboud University) and Computer Sciences (Université de Lorraine), 2019 Postdoctoral experience: ULB (2019), G-SCOP (2019-2020), Inria Sophia Antipolis (2020-2021) His research focuses on graph coloring problems in diverse contexts such as graph powers, locally sparse graphs, and distributed algorithms, utilizing probabilistic methods and connections to bio-informatics through circular codes. He has advanced bounds for h -conflict-free coloring, acyclic coloring, and dichromatic numbers in oriented graphs, with applications to minor-closed families and geometric group theory. Scientific contributions include: Asymptotically tight bounds for chromatic numbers in sparse graphs Efficient fractional coloring algorithms for K_t-minor-free graphs Structural analysis of comma-free and mixed circular codes in genetic alphabets Charles Delorme Prize for outstanding thesis in Graph Theory (2019) Collaborations span institutions like ULB, G-SCOP, Inria, and cross-disciplinary fields from computer science to mathematical biology.

• Control Theory
• Automotive Engineering
• Quantum Computing
• +3 more

Uroš Kalabić is a Teaching Professor at Singidunum University in Serbia. He holds a doctoral degree in Electrical and Computer Engineering from the University of Michigan (2015), a Master's degree from the same university (2011), and a Bachelor's degree in Electrical and Computer Engineering from the University of Toronto (2010). His research focuses on advanced control systems and their applications in automotive engineering, aerospace, and quantum computing. Doctoral studies: University of Michigan, Electrical and Computer Engineering (2015) Master's studies: University of Michigan, Electrical and Computer Engineering (2011) Undergraduate studies: University of Toronto, Electrical and Computer Engineering (2010) Kalabić's research interests span stochastic model predictive control , quantum optimization , autonomous traffic systems , and nonlinear control on manifolds . His work includes applications in automotive engine control, spacecraft attitude control, quantum search algorithms, and sustainable transportation schemes. He has published in top journals like AUTOMATICA and the Journal of Dynamic Systems, Measurement, and Control, focusing on control theory and its interdisciplinary applications. His publications reveal expertise in stochastic control , reinforcement learning , quantum computing , and transportation optimization . Notable contributions include constraint-separation principles in MPC, reference governors for probabilistic systems, and deep learning-based traffic management frameworks. Currently, no scientific awards or honors are documented in the provided materials. Similarly, there is no mention of student advising roles, laboratory leadership, or active research grants in the available descriptions.

• Neural Networks
• Wireless Communications
• Signal Processing
• +8 more

Ivan Milovanović is a researcher at the Department of Postgraduate Studies , University of Singidunum, Belgrade, Serbia. He holds a doctoral degree in Advanced Protection Systems from Singidunum University (2015) and has previously completed master's and bachelor's studies in Contemporary Information Technologies and Graphics and Design at the same institution. His career spans roles in software development, wireless communication research, and neural network applications for electromagnetic signal processing. B.Sc. in Graphics and Design (2008), Singidunum University M.Sc. in Contemporary Information Technologies (2009), Singidunum University Ph.D. in Advanced Protection Systems (2015), Singidunum University Milovanović's research focuses on applying neural networks to solve complex problems in wireless communications, particularly in direction-of-arrival (DOA) estimation for stochastic electromagnetic sources. His work addresses challenges in localization accuracy, signal correlation, and power variation. He has contributed to advancements in 2D/1D DOA estimation using hybrid neural models, experimental validation of antenna array measurements, and noise interference analysis in wireless systems. Publications span topics like wireless sensor networks, channel modeling, and MESH network security. Recent publications highlight a strong interdisciplinary focus combining neural networks with electromagnetic signal processing , antenna array optimization , and wireless communication systems . His work explores stochastic source tracking, coherent signal resolution, and noise mitigation techniques, often collaborating with researchers like Z. Stanković and N. Dončov. While no scientific awards are explicitly mentioned in the provided text, his extensive publication record indicates active contributions to applied electromagnetics and telecommunications engineering.

Miloš PavkovićView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +5 more

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.

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)
• +3 more

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.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +3 more

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.

Hristina Delibasic MarkovicView profile

Assistant Professor

• Atomic, molecular and optical physics
• Analog and digital electronics
• Teaching methodology

Hristina Delibasic Markovic is an Assistant Professor at the Institute of Physics , part of the Faculty of Science and Mathematics at the University of Kragujevac. She earned her doctorate in 2023 and specializes in Atomic, Molecular, and Optical Physics with additional expertise in analog/digital electronics and teaching methodology. Her research focuses on Ultrafast laser-matter interactions Coulomb effects in ionization dynamics Plasma formation in biological materials Quantum trajectory analysis in elliptical polarization She has published extensively on theoretical and computational aspects of laser-induced ionization processes. Key laboratory affiliations include the Laboratory for Atomic and Nuclear Physics and Laboratory for Physics Methodology and Teaching Aids . Her work has been presented in international collaborations and Erasmus+ exchange programs.

Lazar KrsticView profile

Academic

• Artificial Intelligence
• Machine Learning
• Informatics
• +3 more

Lazar Krstic is an Assistant at the Institute of Mathematics and Informatics within the Faculty of Science and Mathematics at the University of Kragujevac. His research focuses on Artificial Intelligence and Machine Learning, operating in the Informatics field. He completed his Master's degree in 2017 and earned his Doctorate in 2019. His work integrates advanced computational techniques with applications in medical physics, education, and environmental modeling. Research Trends: Recent publications highlight expertise in radiation therapy optimization, physics-informed neural networks, and evolutionary algorithms. Articles demonstrate interdisciplinary applications spanning healthcare, education, and computational hydrology.

• Computer Communications
• Fluid Modeling
• Parallel Computing
• +5 more

Miloš Ivanović serves as an Assistant Professor at the Institute of Mathematics and Informatics within the Faculty of Natural Sciences and Mathematics at the University of Kragujevac. His academic career is deeply rooted in computer science and computational mathematics, with research spanning multiple interdisciplinary domains that bridge theoretical computer science with practical applications in engineering, physics, and biology. Dr. Ivanović earned his Doctorate in Computer Science from the Faculty of Natural Sciences and Mathematics (PMF) at the University of Kragujevac, establishing a foundation for his subsequent research career focused on computational methods and high-performance computing. His research interests represent a sophisticated convergence of computational theory and practical applications. Ivanović specializes in advanced computing paradigms including grid computing, GPGPU computing, and cloud-based high-performance computing systems. His work in fluid modeling employs meshless methods and particle dynamics approaches, particularly SPH and DPD techniques applied to microfluidic systems. The breadth of his research extends from theoretical mathematics (graph theory and chemical indices) to practical applications in hydrology, biomedical engineering, and nuclear physics. His interdisciplinary approach enables innovative solutions across diverse scientific domains that require intensive computational resources. The collection of his publications reveals a consistent trajectory of research focused on computational methods with applications spanning multiple scientific disciplines. His work demonstrates strong expertise in parallel computing architectures and their application to complex scientific problems. The publications show a progression from fundamental computational mathematics toward increasingly complex multi-domain applications, particularly in biomedical and environmental systems. His research consistently addresses the challenge of implementing computationally intensive methods on distributed and parallel architectures to solve real-world scientific problems. Throughout his career, Ivanović has participated in numerous national and European research and infrastructure projects, including FP6, FP7, and Tempus initiatives. His collaborative approach is evident in the diverse range of co-authors spanning multiple institutions and disciplines, reflecting his ability to bridge computational science with domain-specific applications. Dr. Ivanović is affiliated with the Group for Mathematical Modeling and Computer Simulations, which develops computational solutions across various domains including fluid dynamics, biomedical applications, and environmental modeling. The group's work encompasses software development for simulation purposes, as evidenced by projects like the SPH07 software package and various specialized simulation tools for engineering and biomedical applications.

Marija ĐurkovićView profile

Professor

• Woodworking Machinery
• Tool-Workpiece Interaction
• Computer Numerical Control (CNC) Systems
• +1 more

Marija Đurković is a Full Professor at the University of Belgrade's Faculty of Forestry, Department of Wood Processing. Her career began in 2009 as an assistant professor, progressing to associate professor in 2020 and full professorship. She chairs the Wood Technologies Department Council and Serbia's M039-4 Woodworking Machines Standards Committee. Education: University of Belgrade - Faculty of Forestry (PhD in Wood Processing) Teaching: Undergraduate, master's, and doctoral courses in Woodworking Machines, CNC Technologies, and Vibrational Diagnostics Research Focus spans woodworking machinery, tool-workpiece interactions, and computer numerically controlled systems. Recent work explores cutting force modeling, acoustic emission monitoring, and sustainable energy practices in wood processing. Scientific Output includes SCI-indexed journals like BioResources and conference proceedings. Her 2021 studies on oak milling and beech sawing represent key contributions to CNC machining optimization. Professional Contributions include two Ministry-funded projects, 9 SCI articles, 2 technical solutions, and a co-authored textbook used in master's and doctoral programs. She also mentors over 70 diploma thesis students annually.