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Aldo Gonzalez-LorenzoView profile

Associate Professor

• Computational Topology
• Discrete Geometry
• Algorithmic Topology
• +3 more

Aldo Gonzalez-Lorenzo is an Associate Professor (maître de conférences) at Aix-Marseille University in Arles, France, where he conducts research at the GMOD team of the Laboratoire d'Informatique et des Systèmes (LIS). His academic work bridges theoretical computer science with practical applications in computational topology and discrete geometry. His primary research interests focus on computational topology and discrete geometry, with particular emphasis on homology computation, digital topology, and geometric modeling. Gonzalez-Lorenzo develops algorithms for analyzing topological features in digital objects, with applications ranging from 3D mesh processing to motion planning for robotics. His work combines theoretical foundations with practical implementations, often resulting in efficient computational methods for complex topological problems. Analysis of his recent publications reveals a strong trend toward practical applications of topological methods. His work spans from theoretical contributions in Alexander duality and discrete Morse theory to applied research in motion planning, CO2 storage analysis, and 3D mesh processing. A notable pattern is his development of efficient computational methods for measuring and analyzing topological holes across various domains, demonstrating the versatility of topological approaches in solving diverse computational problems. 2022 CG:SHOP Geometric Optimization Challenge winner (with team Shadoks) for coordinated motion planning Best paper award at GTMG 2021 for research on measuring holes in 3D meshes Gonzalez-Lorenzo actively participates in multiple research groups including IAPR-TC18, GT GDMM, and GTMG. His work at the GMOD team within LIS focuses on developing computational methods that bridge theoretical topology with practical applications in computer graphics, robotics, and scientific computing. He has developed several interactive tools and web applications to demonstrate and apply his research, including visualizations of Parcoursup algorithms and interactive tools for working with HDVFs (Homological Discrete Vector Fields).

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +4 more

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

• Computational Design
• Timber Architecture
• Digital Fabrication
• +3 more

Hans Jakob Wagner is a Research Group Leader and Doctoral Candidate at the Institute for Computational Design and Construction (ICD) , University of Stuttgart. His work focuses on Computational Wood Architecture , specializing in advanced computational timber building systems and associated robotic fabrication processes. Research Interests : Computational Design for Timber Systems Robotic Fabrication and Assembly Sustainable Construction Methods Hybrid Material Applications (CFRP, Clay, Bamboo) Co-Design Workflows Digital Quality Control in Timber Production Recent Publications explore circular construction with reclaimed timber, adaptive fabrication for bamboo, point-supported timber slabs, and data-informed co-design. His work bridges Computer Science and Structural Engineering through Neural Networks , Augmented Reality , and Agent-Based Modeling . Scientific Awards : Deutscher Holzbaupreis 2025 (Outstanding Scholarship) Best Poster Award (2023) As an ITECH MSc Thesis Advisor , he mentors students in Gradient Density and Terra [In]Cognita projects. His contributions include 15+ peer-reviewed publications and keynote presentations at international conferences like WCTE and IASS.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +2 more

Professor Marc Cavazza holds the position of Professor in Artificial Intelligence at the Department of Computing Science , University of Stirling . His research focuses on Artificial Intelligence , Machine Learning , and Data Analysis with applications in cryptocurrency, drone imagery, and human-agent interaction. Research Interests Professor Cavazza's work spans technical and interdisciplinary domains including: Causal inference in financial time series Computer vision for UAV systems Neuroergonomics in AI-human conversations Multi-agent scheduling optimization Publications Recent work includes Bitcoin time series analysis , drone image processing , UAV traffic management , and neural activity in social interaction studies across Expert Systems with Applications , Sensors , IEEE Intelligent Transportation Systems Magazine , and Frontiers in Neuroergonomics . Contact Email: marc.cavazza@stir.ac.uk

• Control Systems
• Marine Robotics
• Autonomous Systems
• +3 more

Henrik Schmidt-Didlaukies is a Research Fellow in the Department of Engineering Cybernetics at NTNU. His work focuses on advanced control systems for marine and robotic systems, with a particular emphasis on path following, navigation, and robust control methodologies. He has contributed to projects involving underwater vehicles, autonomous marine systems, and sensor-based control strategies. His research integrates theoretical frameworks with practical applications, including experiments in confined maritime environments and cooperative robotic systems. Key research areas include: Nonlinear control and adaptive systems Path planning and guidance algorithms Underwater robotics and vehicle dynamics Autonomous navigation and sensor fusion Collision avoidance and safety-critical systems Recent work highlights include developing anti-windup guarantees for curved path guidance, learning-based optimization for marine vehicles, and experimental validation of obstacle-aware collision avoidance. He has presented findings at major conferences including IFAC World Congress and IEEE OES events.

Inkyung SungView profile

Associate Professor

• Artificial Intelligence
• Operations Research
• Optimization Algorithms
• +5 more

Inkyung Sung is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. Her research focuses on Artificial Intelligence for Operations Research, particularly in optimization algorithms, UAV operations, and scheduling systems. She leads projects on maritime search and rescue planning, autonomous vehicle path-finding, and cloud computing resource management. Her work integrates AI-driven solutions with real-world applications in emergency services, manufacturing, and logistics. Key research areas include UAV-based service delivery optimization, dynamic environment adaptation for autonomous systems, and scheduling algorithms for robotic and cloud environments. She has collaborated on datasets for generalized assignment problems and developed methodologies for zone partitioning in UAV operations. Sung’s contributions span 33 peer-reviewed publications and three datasets, emphasizing practical applications of operations research in safety-critical and efficiency-driven domains.

• Data Science
• Artificial Intelligence
• Algorithm Design
• +7 more

Bojie Shen is a Research Fellow in the Department of Data Science & AI at Monash University. His research lies at the intersection of artificial intelligence, data science, and algorithmic transportation systems, with a focus on efficient and eco-friendly route planning for electric vehicles, public transport, and multi-agent systems. His primary research interests include: Artificial Intelligence and Multi-Agent Path Finding Graph Algorithms and Route Planning Energy-Efficient Navigation for Electric Vehicles Public and Multi-Modal Transport Routing Combinatorial Search and Optimization Sustainable Mobility and Green Logistics The analysis of his recent publications reveals a consistent trajectory in developing scalable, exact, and energy-aware routing algorithms. His work frequently appears in high-impact venues such as IEEE Transactions on Intelligent Transportation Systems and AAAI conferences, demonstrating strong technical rigor and relevance to real-world transportation challenges. A significant portion of his research contributes to UN Sustainable Development Goals related to sustainable cities and climate action. Notable scientific contributions include: Taxonomy and survey of eco-friendly route planning algorithms Efficient public transport routing using transfer connection databases Advancements in multi-agent path finding beyond pairwise constraints Exact and dynamic shortest path computation for electric vehicles Dr. Shen actively collaborates with leading researchers including Muhammad Aamir Cheema, Daniel Harabor, and Peter Stuckey. While no formal advising or grant information is available in the text, his publication volume and co-authorship network suggest active involvement in funded research projects and potential mentorship roles within the department. He is affiliated with a vibrant research group in data science and AI at Monash University, contributing to advancements in intelligent transportation systems and combinatorial search algorithms.

• Artificial Intelligence
• Multi-Agent Systems
• Privacy and Security
• +3 more

Arthur Jakobsson is a Course Instructor at the Computer Science Department of Carnegie Mellon University. His research focuses on multi-agent systems, privacy and security, and AI applications in robotics. He has contributed to improving pathfinding algorithms for autonomous systems and developing techniques to combat deepfakes and communication risks. Recent publications highlight his work on imitation learning for multi-agent pathfinding (MAPF), heuristic search enhancements, and AI-assisted methods for detecting deepfake audio. His privacy research includes permission management frameworks and mitigating tracking risks in digital communications. He holds a strong interest in cybersecurity applications of machine learning. No academic awards or grants were explicitly mentioned in the provided materials. Advising relationships remain unreported in the current dataset. His work bridges theoretical computer science with practical security implementations in autonomous systems.

Volkan PatogluView profile

Professor

• Physical Human-Robot Interaction
• Cognitive Robotics
• Haptic Interfaces
• +7 more

Volkan Patoglu is a Full Professor at Sabancı University 's Mechatronics Program within the Faculty of Engineering and Natural Sciences . He co-founded and serves as Chief Scientific Officer at Interact Medical Technologies Inc. , while maintaining academic roles since 2005. His research spans two laboratories: the Human-Machine Interaction Laboratory and Cognitive Robotics Laboratory . B.Sc., Middle East Technical University (1999) M.Sc., University of Michigan in Mechanical Engineering (2000) and Electrical Engineering (2002) Ph.D., University of Michigan (2005) Research Focus: Physical human-robot interaction (pHRI) with emphasis on: Force-feedback exoskeletons for rehabilitation and augmentation Hybrid dynamical systems control Haptic interface design and stability analysis Cognitive robotics with symbolic reasoning integration Medical robotics applications Publication Trends: Recent works focus on series elastic actuation optimization, hybrid planning algorithms, and advanced haptic rendering techniques. His research bridges control theory with practical applications in rehabilitation robotics and human augmentation systems. Honors: TÜBİTAK Career Award (2008) Science Academy Young Scientist Award (2015) Multiple IEEE Associate Editor Awards Best Application Paper Awards (2020, 2013) NATO Science Fellowship (1999) Key Contributions: Developed original methods for hybrid dynamical system control, designed multiple rehabilitation exoskeletons (AssistOn series), and pioneered integration of answer set programming with robotic planning. Serves as Associate Editor for IEEE Robotics and Automation Letters and IEEE Transactions on Neural Systems & Rehabilitation Engineering .

Khaled AbdelghanyView profile

Professor

• Transportation System Modeling
• Smart City Applications
• Emerging Mobility Systems
• +4 more

Dr. Khaled Abdelghany is a Professor in the Department of Civil and Environmental Engineering at Southern Methodist University (SMU) and a Fellow at the Stephanie and Hunter Hunt Institute for Engineering and Humanity. He holds a Ph.D. from the University of Texas at Austin (2001) and has been at SMU since 2004, serving in various roles including Interim Chair (2022) and Chair (2011–2016). Affiliation: SMU Bobby B. Lyle School of Engineering Education: Ph.D. (UT Austin), M.S. & B.S. (Cairo University) His research spans transportation system modeling, smart city applications, emerging mobility systems, city logistics, and crowd dynamics. He has pioneered work in airline operations, real-time traffic management, and transportation electrification. Recent publications focus on airline capacity allocation, emergency response optimization, and machine-learning applications in transportation. His work has been cited for integrating deep learning and network competition analysis. Scientific Awards: IEEE Outstanding Service Award, SEAS Outstanding Teaching Award, and multiple honor societies Professional Roles: Associate Editor (IEEE Transactions on Transportation Electrification), Co-Chair (TRB Subcommittee on Ethics and Equity in AI)

Satish Kumar ThittamaranahalliView profile

Professor

• Artificial Intelligence
• Multi-Agent Systems
• Multi-Robot Systems
• +10 more

Prof. Satish Kumar Thittamaranahalli (T. K. Satish Kumar) is a faculty member at the University of Southern California (USC) with appointments in the Departments of Computer Science, Physics and Astronomy, and Industrial and Systems Engineering. He leads the Collaboratory for Algorithmic Techniques and Artificial Intelligence (CATAI) at USC’s Information Sciences Institute, focusing on combinatorial problems in AI. Ph.D., Computer Science, Stanford University (2005) M.S., Computer Science, Stanford University (2003) B.Tech., Computer Science and Engineering, Indian Institute of Technology Guwahati (1999) His research spans multi-agent systems , combinatorial optimization , and constraint satisfaction , with applications in robotics, quantum computing, and data informatics. Recent work includes FastMap-based architectures and algorithms for pathfinding and facility location problems. Notable awards include the Best Student Paper Award (2005) at ICAPS. His lab has produced peer-reviewed publications in venues like AAAI, AAMAS, and Nature Communications Engineering.

Panagiotis TsiotrasView profile

Professor

• Nonlinear Control
• Optimal Control
• Artificial Intelligence
• +5 more

Panagiotis Tsiotras holds the David & Andrew Lewis Endowed Chair in the Daniel Guggenheim School of Aerospace Engineering at Georgia Tech and serves as Associate Director at the Institute for Robotics and Intelligent Machines (IRIM). His research focuses on nonlinear/optimal control, AI integration, planning, and decision-making for autonomous ground, aerial, and space systems. Education: Dipl.Eng. Mechanical Engineering, National Technical University of Athens, 1986 M.S. Aerospace Engineering, Virginia Tech, 1987 M.S. Mathematics, Purdue University, 1992 Ph.D. Aeronautics and Astronautics, Purdue University, 1993 Research: Primary interests include autonomous vehicle control, robotics, AI-driven decision-making, and multi-agent systems. His work bridges theoretical control frameworks with applications in aerospace robotics, featuring advanced algorithms for navigation, safety verification, and large-scale system coordination. Recent themes involve stochastic control, distribution steering, and real-time safety guarantees. Awards: IEEE Technical Excellence Award in Aerospace Controls NSF CAREER Award Outstanding Aerospace Engineer Award (Purdue) Sigma Xi Excellence in Research Award Fellow of AIAA, IEEE, and AAS Leadership: Directs the Dynamics and Control Systems Laboratory (DCSL) and collaborates with the Vertical Lift Research Center of Excellence (VLRCOE) and Decision and Control Laboratory (DCL). Editorial roles include Chief Editor of Frontiers in Robotics & AI and Associate Editor for multiple control journals.

Esra ErdemView profile

Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Answer Set Programming
• +6 more

Esra Erdem is a Professor in the Department of Computer Science and Engineering at Sabanci University, Faculty of Engineering and Natural Sciences. Her research focuses on the mathematical foundations of knowledge representation, automated reasoning, and answer set programming, with applications in bioinformatics, logistics, cognitive robotics, and economics. She has developed frameworks for hybrid planning, spatial-temporal reasoning, and multi-agent coordination using logic-based methods. She earned her Ph.D. in Computer Sciences (2002) at the University of Texas at Austin under Vladimir Lifschitz. Her academic genealogy traces back to Nikolai Shanin, Pavel Aleksandrov, and Karl Weierstrass. Her recent publications emphasize answer set programming for robotics, including collaborative assembly, path finding, and spatial reasoning. She applies these techniques to real-world challenges in cognitive factories and rehabilitation robotics. Erdem co-edited proceedings for KR 2021 and ICLP 2019 Technical Communications. She serves as an academic advisor and has contributed to educational tools like ReAct! for AI planning in robotics.

Marco CaminatiView profile

Lecturer

• Computer Science
• Formal Methods
• Theorem Proving
• +5 more

Dr. Marco Caminati is a Lecturer in Computer Science at Lancaster University, affiliated with the Security Lancaster research group, specializing in Distributed Systems and Software Security. His research focuses on formal methods, theorem proving, and their applications in areas like auction theory, quantum computing, and cybersecurity. He has contributed to verified algorithms, conflict resolution frameworks, and formalizations in Isabelle/HOL. His work bridges theoretical foundations with practical implementations, addressing challenges in software verification, healthcare informatics, and distributed systems. Key research interests include automated theorem proving, formal verification of concurrent systems, and applying formal methods to real-world problems such as medical guidelines and combinatorial optimization. He actively publishes in venues like the Journal of Automated Reasoning and has explored topics ranging from quantum information theory to SMT-based concurrency analysis. His publications often emphasize rigorous validation through tools like Isabelle and Mizar, ensuring correctness in complex systems. While no specific awards are listed, his contributions to formal methods have advanced applications in both academia and industry.

Myounggyu WonView profile

Associate Professor

• Cyber Physical Systems
• Intelligent Transportation Systems
• Mobile Computing
• +4 more

Dr. Myounggyu Won is an Associate Professor in the Department of Computer Science at the University of Memphis, leading the Connected Smart Sensor Systems (CS3) Lab. He holds a PhD from Texas A&M University (2013) and a BE from Sogang University, South Korea (2007). His research focuses on cyber-physical systems, intelligent transportation systems, and mobile computing, emphasizing algorithmic innovation for safer, efficient transportation through advanced sensing and communication technologies. Key research contributions include developing reinforcement learning-based solutions for vehicle routing with drones, adaptive cruise control systems using deep learning, and pedestrian safety prediction via wearable sensors. His work bridges theoretical advancements with practical applications, published in top-tier venues like ICRA, MM, and IEEE journals. Dr. Won previously served as an Assistant Professor at South Dakota State University before joining the University of Memphis in 2018. His lab explores cutting-edge topics such as autonomous vehicle coordination, wireless charging optimization, and low-power IoT communication for smart cities. Despite no explicit grants or awards listed, his prolific publication record reflects sustained research excellence in transportation and sensor systems.