جستجوی استاد راهنمای دانشگاهی

• Cyber Law
• Artificial Intelligence and Law
• Business Law
• +۷ مورد دیگر

Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.

• Information Technology Governance
• Cybersecurity
• Blockchain
• +۸ مورد دیگر

Dalibor Radovanović is a researcher at Singidunum University , affiliated with the Faculty of Business Informatics . His work spans cybersecurity, blockchain technologies, and their applications in business and IoT systems. Education: Doctoral Dissertation (2016, Singidunum University) Master's & Basic Studies (Faculty of Business Informatics) Secondary Education: ETŠ Nikola Tesla Research Interests include: Security frameworks for IoT and blockchain integration Smart card and wireless network vulnerabilities E-governance and corporate IT audit methodologies Machine learning applications in cybersecurity Environmental performance optimization in agribusiness Publication Trends reveal a focus on blockchain (2022), cybersecurity (2009-2022), and IT governance (2010-2017). His work bridges theoretical analysis with practical implementations in Serbia's digital economy. Collaborations with scholars like Marko Šarac and Saša Adamović highlight interdisciplinary approaches to securing financial systems, educational institutions, and industrial IoT applications.

Nemanja Stanišićمشاهده پروفایل

استاد

• Corporate Finance
• Banking
• Audit
• +۳ مورد دیگر

Dr. Nemanja Stanišić is a Full Professor at Singidunum University's Faculty of Business, with a distinguished academic career spanning over 15 years. He holds a Ph.D. in Corporate Finance from Singidunum University (2010), an MBA in Finance from Lincoln University (2007), and a Bachelor's in Accounting from the University of Belgrade (2005). His expertise focuses on Corporate Finance, Banking, Audit, and Applied Statistical Analysis. His research integrates quantitative methods with economic theory, addressing topics such as audit opinion prediction using AI, tourism destination competitiveness, financial distress dynamics, and air pollution health impacts. He co-authored textbooks including Contemporary Exchange and E-business (2010) and Financial Statement Analysis (2024), and served as Editor-in-Chief of The European Journal of Applied Economics . He teaches courses from Financial Accounting to Advanced Financial Engineering at undergraduate, master's, and Ph.D. levels. The 15 most recent publications highlight his interdisciplinary approach: 7 in Finance/Audit, 5 in Tourism/Hospitality, and 3 in Environmental Health. Key trends include applying machine learning to audit quality (2023), multilevel modeling for hospitality satisfaction (2015-2019), and air pollution mortality analysis (2016). His work appears in high-impact journals like Tourism Management (IF 10.125) and Environmental Health (IF 4.986). He held administrative roles including Rector (2020-2021) and Vice President of Singidunum University. He served as Vice Dean for Student Affairs (2010-2011) and participated in TEMPUS projects for educational reform. He mentors graduate students extensively, advising 100+ bachelor's, 57 master's, and 4 doctoral theses, including international candidates. His visiting professorship at Bangkok's ICO NIDA and teaching in Austria-Singidunum joint programs reflect global engagement. Professional development includes advanced training at Utrecht University (Bayesian Modeling, 2019), Stanford (Mentoring, 2010), and NYU (Valuation, 2012). He reviews for top journals like Annals of Tourism Research and Cornell Hospitality Quarterly , with 1017 Google Scholar citations and 349 Scopus citations. Current research involves the Science Fund of Serbia's TOURCOMSERBIA project evaluating tourism competitiveness models.

Svetlana Stanišićمشاهده پروفایل

دانشیار

• Environmental Science
• Air Pollution Modeling
• Artificial Intelligence Applications
• +۷ مورد دیگر

Svetlana Stanišić is an Associate Professor at Singidunum University's Faculty of Informatics and Computer Science, Department of Applied Artificial Intelligence. She holds a dental degree from the University of Belgrade's Dental Faculty (1998-2004) and a PhD in Physical Chemistry from the University of Belgrade's Faculty of Physical Chemistry (2007-2011). Her interdisciplinary research bridges environmental science, artificial intelligence, and public health. Her research interests focus on environmental science, air pollution modeling, and artificial intelligence applications . She investigates the atmospheric fate of pollutants using advanced machine learning techniques, with particular emphasis on polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and particulate matter. Her work combines environmental chemistry, computational modeling, and public health impact assessment to address urban air quality challenges. Analysis of her recent publications reveals a clear trend toward explainable AI applications in environmental science . She has pioneered the use of SHAP (SHapley Additive exPlanations), XGBoost, and metaheuristic optimization for pollutant fate prediction and source apportionment. Her research spans indoor and outdoor environments, with particular attention to health implications of air pollution exposure in urban settings like Belgrade. Dr. Stanišić leads significant research projects including "crAIRsis" (2024-2026) , which characterizes crisis-caused air pollution alternations using AI frameworks, and "ATLAS" , focusing on artificial intelligence theoretical foundations for spatio-temporal modeling. She has also authored influential books including "Ako je hrana Vaš porok" (2024) and "Ishrana i zdravlje" (2018). Her research group focuses on environmental informatics , developing computational tools to understand pollutant behavior in complex urban environments. The team combines atmospheric chemistry measurements with advanced machine learning techniques to create predictive models with practical applications for urban air quality management and public health protection.

Akanksha Agrawalمشاهده پروفایل

استادیار

• Parameterized Complexity & Algorithms
• Graph Algorithms
• Computational Geometry
• +۳ مورد دیگر

Akanksha Agrawal is an Assistant Professor and Veena and Induprakas Keri Faculty Fellow at the Department of Computer Science and Engineering, Indian Institute of Technology Madras. Her research focuses on Parameterized Complexity & Algorithms, Graph Algorithms, Computational Geometry, Exact Algorithms, and Fine Grained Algorithms & Complexity. She has held postdoctoral positions at Ben-Gurion University of the Negev (Israel) and the Hungarian Academy of Sciences, funded by a PBC Fellowship. She earned her Ph.D. from the University of Bergen under Professors Saket Saurabh and Daniel Lokshtanov. Education: Ph.D., University of Bergen, Norway (2017-2021) Postdoctoral Researcher, Ben-Gurion University of the Negev, Israel (2021-2022) Postdoctoral Researcher, Hungarian Academy of Sciences, Hungary (2022-2023) Research Interests: Her work emphasizes parameterized complexity, graph algorithms, and computational geometry. She explores exact algorithms, fine-grained complexity, and algorithmic approaches to NP-hard problems. Her research bridges theoretical insights with practical algorithmic solutions. Recent Activities & Awards: ACM-India Eminent Speaker (2024-2026) Invited Talks: COMSNETS 2025, IISc Bengaluru (2024), Bali Parameterized Graph Algorithms (2024) Program Committee Roles: IPEC 2025 (Co-Chair), WG 2025, CALDAM 2025 Grants & Academic Services: Organizing Dagstuhl Seminar (Jan. 2025) with Maria Chudnovsky, Daniel Paulusma, and Oliver Schaudt Contributions to international conferences and workshops Teaching: Courses include Combinatorial Objects, Parameterized Algorithms, Design & Analysis of Algorithms, Approximation Algorithms, and Advanced Data Structures.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

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.

• Artificial Intelligence
• Optimization
• Machine Learning
• +۷ مورد دیگر

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.

Michael Krivelevichمشاهده پروفایل

استاد

• Probabilistic Methods in Combinatorics
• Random Structures
• Hypergraphs
• +۷ مورد دیگر

Prof. Michael Krivelevich holds the Baumritter Chair in Combinatorics at the School of Mathematical Sciences, Tel Aviv University. His research focuses on probabilistic methods in combinatorics, random graphs, and positional games. He has authored influential books such as Positional Games and contributed to foundational work in random graph theory. Currently teaching Introduction to Combinatorics and Graph Theory (Spring 2025), he has extensive experience in courses like Graph Theory and Hypergraph Coloring. His work bridges theoretical computer science, coding theory, and combinatorics, with over 150 publications. Recent research explores game-theoretic thresholds, random graph evolution, and equitable coloring algorithms. Education: Ph.D. in Mathematics, Tel Aviv University (not explicitly stated, inferred from career trajectory). Research Interests Krivelevich's work emphasizes random structures , extremal graph theory , and probabilistic combinatorics . He investigates phase transitions in random graphs, positional game strategies, and algorithmic challenges in graph coloring. His contributions include proving sharp thresholds for Hamilton cycle games and analyzing WalkSAT performance on smoothed k-CNF formulas. Collaborations span theoretical computer science and discrete mathematics. Publications Recent articles address Hamiltonicity in Maker-Breaker games, equitable coloring of random graphs, and smoothed analysis of satisfiability processes. His work often combines rigorous proofs with algorithmic insights. Teaching & Mentorship Guides students through advanced combinatorial topics and has taught foundational courses since 2002. No explicit student listings available in provided texts.

• Optimization for Machine Learning
• Collaborative Learning
• Federated Learning
• +۶ مورد دیگر

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.

Aleksandar Mihajlovicمشاهده پروفایل

پژوهشگر

• Visual Communication
• Graphic Design
• Digital Marketing
• +۵ مورد دیگر

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.

• Control Systems
• Signal Processing
• Neuro-Fuzzy Systems
• +۳ مورد دیگر

Dragan S. Antic is a Professor at the Department of Automation, Faculty of Electronics, University of Niš. He earned his PhD in Automation from the same institution in 1994, following a Master's (1991) and Bachelor's (1987) degree in the same field. His research focuses on control systems, signal processing, and neuro-fuzzy systems, with a strong emphasis on sliding mode control, orthogonal function-based modeling, and adaptive neural networks. He has published 51 papers in journals with impact factors and currently participates in 2 national and 8 international research projects. Key research areas include dynamic system modeling, quasi-orthogonal filters, and finite-time stability analysis of time-delay systems. His work often integrates mathematical techniques with engineering applications, such as anti-lock braking systems and PID control optimization. Contact details include his office at Aleksandra Medvedeva 4, Niš, and the email dragan.antic@elfak.ni.ac.rs. Professional contributions include foundational research in endocrine neural networks and low-pass filter design, with notable publications in journals like Electronics Letters and Journal of the Franklin Institute . His efforts bridge theoretical advancements with practical engineering solutions in automation and control.

• Theoretical Computer Science
• Algorithm Design
• Distributed Computing
• +۶ مورد دیگر

Yannic Maus is a University Professor at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology (TU Graz), Austria, where he heads the newly founded Institute of Algorithms and Theory (established in 2025). He also serves as co-leader of one of the five fields of expertise at TU Graz (FoE Information, Communication & Computation). His academic journey includes: PhD in Computer Science from University of Freiburg, Germany (2014-2018) MSc in Mathematics from RWTH Aachen, Germany BSc in Mathematics and Computer Science from RWTH Aachen, Germany (with a year at National University of Singapore) Professor Maus specializes in theoretical computer science and algorithm design, with a particular focus on distributed computing. His research spans distributed graph algorithms, efficient algorithms, data structures, complexity theory, and geometric algorithms. He approaches problems with both theoretical rigor and practical applications in mind, seeking clean mathematical solutions to questions motivated by real-world systems. His recent publications show a strong focus on distributed and parallel algorithms, particularly in graph theory. The research trends include distributed graph coloring, symmetry breaking, vertex cover problems, and massively parallel computing models. His work often bridges theoretical computer science with practical distributed systems considerations, with applications to large-scale networks and highly parallel systems. Professor Maus has received numerous accolades for his research: 2020 Principles of Distributed Computing Doctoral Dissertation Award Wolfgang-Gentner-Nachwuchsförderpreis 2019 GI Dissertationspreis 2018 Best Paper Awards at SIROCCO 2016, DISC 2016, and DISC 2017 Professor Maus actively mentors PhD students and has secured significant research funding, including FWF grants P36280-N (2023-2027), DOC 183 (2024-2028), I6915 (2024-2028), and FFG grant No. 59263962. His research group maintains strong international collaborations with institutions across Germany, Finland, Iceland, Israel, and beyond, providing students with opportunities for international research visits. He leads the Algorithms & Complexity research group at TU Graz, which includes PhD students Manuel Jakob, Florian Schager, Malte Baumecker, and Kritika Kashyap, as well as postdoc Tijn de Vos. The group is actively involved in theoretical computer science research with a focus on distributed and parallel algorithms, particularly for large-scale networks and highly parallel systems.

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +۵ مورد دیگر

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 Rackovićمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Databases
• +۴ مورد دیگر

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
• +۶ مورد دیگر

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.