Find Academic Supervisors

• Artificial Intelligence
• Human-Computer Interaction
• Narrative Systems
• +5 more

Professor Fred Charles serves as Head of Department for Creative Technology at Bournemouth University, specializing in computational intelligence applied to simulated worlds. His expertise spans artificial intelligence, human-computer interaction, and interactive narrative systems, with significant contributions to virtual reality and brain-computer interface technologies. His research has led to award-winning interactive systems and numerous publications in top-tier conferences and journals. Education: PhD in Computer Science ("Intelligent Virtual Actors in Interactive Storytelling") Master's degree in Computer Aided Graphical Technology Applications BSc in Computer Science Professor Charles' research focuses on the intersection of AI, narrative systems, and immersive technologies. His work explores how computational intelligence can enhance virtual environments, particularly through brain-computer interfaces that enable more natural human-virtual agent interactions. Recent projects investigate social anxiety in VR settings, multimodal interaction frameworks, and personalized dialogue systems for virtual characters. His research bridges theoretical AI concepts with practical applications in healthcare, education, and entertainment domains. His most recent publications demonstrate a clear trend toward applying interactive narrative techniques to real-world problems, particularly in mental health assessment (OCD, social anxiety), responsible gambling initiatives, and educational applications. The work increasingly incorporates multimodal input analysis and neurofeedback mechanisms to create more responsive and adaptive virtual experiences. Scientific Awards: Blue Sky Award (ACM Hypertext, 2018) Best Application (International Conference on Automated Planning and Scheduling, 2013) Professor Charles has successfully supervised numerous PhD students working on topics ranging from crowd simulation to narrative generation systems. His research has been supported by substantial grants from Innovate UK, Economic and Social Research Council, AHRC/EPSRC, and the European Commission, including projects on believable agent behavior in VR, responsible online gambling, and machine understanding for interactive storytelling. He maintains active collaborations with researchers across Europe and has contributed significantly to the development of narrative medicine applications.

• Intelligent Agents
• Distributed Artificial Intelligence
• Multiagent Systems
• +8 more

Javier Vazquez Salceda , Senior Lecturer at Universitat Politècnica de Catalunya (UPC), is affiliated with the School of Computer Science and the Department of Computer Science. His research spans intelligent agents, smart cities, and socio-technical systems. Key Projects: HUB D'INNOVACIÓ PEDIÀTRICA, XARXES R+D+I in health technologies, and AI-on-demand platforms. Collaborations: Active in IDEAI-UPC (Intelligent Data Science and AI Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). Research Focus: Agent-based modeling, normative systems, and distributed AI applications in healthcare and urban planning. He explores how multiagent systems can address socio-technical challenges through projects like wastewater management simulations and organ transplant coordination tools. Academic Engagement: Published extensively in journals like Artificial Intelligence and Law and IEEE Intelligent Systems , with a focus on middleware architectures, electronic institutions, and provenance systems. His work bridges theoretical AI with real-world implementations. Contact: Email jvazquez@lsi.upc.edu , ORCID 0000-0003-1732-9446 , Scopus ID 6602325102 .

Sharon GuniView profile

Assistant Professor

• Artificial Intelligence
• Intelligent Transportation Systems
• Reinforcement Learning
• +6 more

Dr. Sharon Guni is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. She directs the PiStar laboratory, focusing on advancing artificial intelligence (AI) theory and its practical applications, particularly in transportation systems and multi-agent coordination. Her research integrates reinforcement learning, combinatorial optimization, and game theory to address real-world challenges such as traffic congestion and autonomous vehicle management. Ph.D. in Information Systems Engineering, Ben-Gurion University (2015) M.S. in Information Systems Engineering, Ben-Gurion University (2012) B.S. in Information Systems Engineering, Ben-Gurion University (2011) Dr. Guni’s research interests span AI, intelligent transportation systems, reinforcement learning, and multiagent systems. Notable contributions include socially optimal traffic tolling mechanisms, conflict-based search algorithms for multi-agent pathfinding, and agent-based models for epidemiological inference. She has received prestigious awards, including the NSF CAREER Award (2023) and the AAAI Outstanding Paper Award (2016). Her lab’s work on self-optimizing traffic signal controllers has been featured in WIRED Magazine and multiple news outlets. Recent projects include socially optimal non-discriminatory policies for continuous-action games and curriculum generation for reinforcement learning. Awards: Bergmann Memorial Research Award (2024), Wilson Memorial Lecture (2025), AIJ Prominent Paper Award (2020) Grants: NSF CAREER Award, Texas A&M grants supporting traffic optimization and AI research PiStar collaborates on interdisciplinary projects, including autonomous driving demonstrations with Houston high schools and pandemic mitigation strategies via agent-based modeling. The lab hosts a dynamic team of graduate students and researchers advancing AI’s societal impact.

• Traffic Engineering
• Automated Vehicles
• Reinforcement Learning
• +5 more

Papamichail Ioannis is a Professor at the School of Production Engineering and Management, Technical University of Crete. His research focuses on advanced traffic control systems, automated vehicle navigation, and intelligent transportation systems. He specializes in macroscopic/microscopic traffic modeling, reinforcement learning applications, and optimization-based control strategies for lane-free and conventional traffic environments. Key research areas include automated vehicle trajectory planning, variable speed limit algorithms, cooperative adaptive cruise control, and intersection control for connected vehicles. His work integrates numerical methods, partial differential equations, and multiagent decision-making frameworks to address traffic congestion, safety, and efficiency challenges. Recent investigations emphasize lane-free traffic systems, exploring optimal path planning, vehicle nudging strategies, and boundary control mechanisms through microscopic simulations. He has also developed novel controllers for highway work zones and urban networks, leveraging data fusion and real-time state estimation techniques. Ioannis collaborates on EU-funded projects and actively contributes to SUMO-based simulation tools for automated vehicle testing. His research bridges theoretical control systems with practical traffic management solutions, aiming for zero congestion/accidents in future transportation networks.

• Artificial Intelligence
• Smart Grids
• Machine Learning
• +5 more

Carlos Ramos is a researcher affiliated with the University of Porto's Faculty of Engineering and the Polytechnic Institute of Porto's GECAD Research Group. He holds a PhD in Electrical Engineering and Computers from the University of Porto (1993). His work focuses on AI applications in energy systems, ambient intelligence, and agent-based systems. Ramos has collaborated extensively with researchers like Zita A. Vale and Sabah Mohammed on projects involving smart grids, manufacturing optimization, and cyber-physical systems. Research Interests : His primary areas include artificial intelligence, smart grids, machine learning, agent-based systems, and ambient intelligence. Recent work emphasizes explainable AI models in building energy management and genetic algorithms for industrial scheduling. Publications : Ramos has over 138 publications spanning journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Intelligent Systems . Key themes include AI-driven energy optimization, IoT applications in smart cities, and multi-agent systems for market simulation. His work often bridges theoretical AI with practical industrial and healthcare applications. Labs/Teams : Active in the GECAD Research Group, focusing on intelligent engineering solutions. Collaborates with international partners on projects like the Mascem electricity market simulator and the ISEM agent-based marketplace.

John ThangarajahView profile

Professor

• Artificial Intelligence
• Autonomous Systems
• Knowledge-based Reasoning
• +3 more

John Thangarajah is a Professor in AI and Director of Research at the Center for Industrial AI Research & Innovation (CIAIRI) at RMIT University, Melbourne. He leads research directions, academic partnerships, and industry collaborations, focusing on applied research with over $4M in government/industry funding. His expertise spans Autonomous Systems, Knowledge-Based Reasoning, and Agent-Based Modelling. He holds awards including the 2020 Vice Chancellor’s Leadership Award for fostering collaboration in Computer Science and the 2017 AAMAS Best Paper Blue Sky Track. Education: Extensive background in CS/IT education, transformed first-year Computer Science curricula to hands-on experiential learning. Research: Specializes in AI-driven smart systems, human-machine teams, and multi-agent systems. Member of IFAAMAS board. Publications: Over 147 peer-reviewed works in AI, including advancements in explainable agents, reinforcement learning, and medical imaging applications. Awards: Recognized for innovation in AI (Telstra 2011), demonstration excellence (2005), and leadership in academia. Grants: Secured significant funding from DST Group and industry partners, emphasizing practical impact. His lab focuses on AI applications in security, resilience, and industrial innovation through CIAIRI and the Security & Resilience Hub.

Popirlan Claudiu IonutView profile

Lecturer

• Artificial Intelligence
• Mobile Agents
• Multiagent Systems
• +6 more

Dr. Popirlan Claudiu Ionut is a Lecturer in the Computer Science Department at the Faculty of Exact Sciences, University of Craiova, Romania. He has been actively involved in academic and research activities since 2004, progressing from Assistant Lecturer to Assistant Professor and currently serving as a Lecturer. He holds a Ph.D. in Computer Science from the University of Pitesti and has extensive teaching experience in advanced programming, databases, GIS, and software engineering. His educational background includes: Ph.D. in Computer Science, University of Pitesti (2005–2009) Master in Artificial Intelligence, University of Craiova (2003–2004) B.Sc. in Computer Science, University of Craiova (1999–2003) Secondary Education, Fratii Buzesti National College (1995–1999) Dr. Popirlan's research is centered on Artificial Intelligence, with a strong emphasis on Mobile Agents and Multiagent Systems. His work explores knowledge representation, processing, and management using agent-based architectures, with applications in robotics, contact centers, and virtual organizations. He has also contributed to web-based 3D visualization and modeling in mechanical engineering. His technical expertise spans Java technologies, databases, and software engineering. The analysis of his recent publications reveals a consistent focus on mobile agents for knowledge processing, distributed systems, and intelligent control. Themes include agent architectures, knowledge base management, pathfinding algorithms, and simulation systems, indicating a deep and sustained research trajectory in autonomous and intelligent software systems. He has received research grants such as TD CNCSIS and CNCSIS IDEI, where he served as director and team member respectively, focusing on mobile agents and knowledge management. His editorial roles include Scientific Referent for INFO-PRACTIC and Editorial Secretary for the Annals of the University of Craiova. Dr. Popirlan is an active member of the academic community, affiliated with IEEE, IEEE Computer Society, ACM, IBM Academic Initiative, Microsoft Faculty Connection, and the Romanian Mathematical Society. He is also part of the Research Center of Artificial Intelligence in Craiova.

• Operations Research
• Logistics
• Supply Chain Management
• +3 more

Remy Dupas is a Professor at the University of Bordeaux , affiliated with the IMS Bordeaux - Integration, Material to System Laboratory within the Production Engineering research group. His work focuses on Operations Research , Logistics , and Transportation Systems , developing advanced algorithms for complex routing and supply chain optimization problems. Research Highlights: Innovative Branch-Cut-and-Price algorithms for Two-Echelon Vehicle Routing Problems with drones and time windows City Logistics models for sustainable urban freight distribution in Tokyo MultiAgent Systems for supply chain coordination 3D Loading Constraints integration in pickup-and-delivery problem solving Rail-Rail Transshipment scheduling methodologies Key Publications (2024-2007) demonstrate expertise in Combinatorial Optimization , Dynamic Routing , and Interoperability Metrics for enterprise systems. His research is characterized by strong Algorithm Development and Real-Time Transportation solutions.

Munindar SinghView profile

Professor

• Artificial Intelligence
• Service-oriented computing
• Sociotechnical systems
• +6 more

Munindar Singh is an Alumni Distinguished Graduate Professor in the Department of Computer Science at North Carolina State University , where he also serves as Co-Director of the DoD-sponsored Science of Security Lablet . His research spans Artificial Intelligence , Multiagent Systems , and Service-Oriented Computing , with applications in Cybersecurity , Privacy , and Social Computing . His research interests focus on ethical and sociotechnical dimensions of AI, including moral reasoning in social contexts , resilient multiagent systems , and responsible computing for sustainability . His work integrates formal methods with machine learning to address challenges in decentralized systems , agent communication , and cyber deception . Recent publications highlight trends in AI ethics , cybersecurity , and social simulation , with subfields like moral judgment analysis , domain adaptation , and metaverse services . These works often bridge technical and societal concerns, emphasizing trustworthy AI and ethical governance . Fellow, American Association for the Advancement of Science (AAAS) (2020) ACM/SIGAI Autonomous Agents Research Award (2020) IBM Shared University Research Award (2018) NCSU Research Leadership Academy Membership (2017) IFAAMAS Influential Paper Award (2016) National Science Foundation CAREER Award (1996) Munindar has held leadership roles, including editor-in-chief of ACM Transactions on Internet Technology and IEEE Internet Computing . His research is funded by sponsors such as DARPA , NSF , and IBM . He co-directs the Science of Security Lablet , a DoD-funded initiative advancing cybersecurity research.

Christian L. VestergaardView profile

Researcher

• Statistical Physics
• Biophysics
• Computational Neuroscience
• +12 more

Christian L. Vestergaard is a CNRS researcher (Researcher rank) in the Decision and Bayesian Computation group within the Department of Neuroscience at the Institut Pasteur, Paris. He holds a PhD in theoretical biophysics from the Technical University of Denmark and has held postdoctoral positions at the Institut Pasteur, Center for Theoretical Physics (Marseille), and DTU Nanotech. He currently leads research projects on neural connectomes and numerical methods for network analysis. Education: PhD in Theoretical Biophysics, DTU Nanotech, Technical University of Denmark (2008–2012) Master’s in Physics and Biophysics, Niels Bohr Institute, University of Copenhagen (2003–2008) Exchange Semester, UPMC, Paris (2007) Second-Year Master’s in Complex Systems His research lies at the intersection of statistical physics, biophysics, and computational neuroscience. He focuses on data-driven modeling of complex dynamical systems, particularly in biological contexts such as neural networks, single-molecule dynamics, and behavioral analysis. His work emphasizes the development of statistical and computational methods to infer structure-function relationships in biological systems. The analysis of his recent publications reveals a strong trend toward methodological innovation in network science, Bayesian inference, and machine learning applied to neuroscience and biophysics. Key themes include temporal network modeling, stochastic processes, information maximization in decision-making, and high-throughput behavioral phenotyping in Drosophila. His work combines theoretical rigor with practical applications in biological data analysis. Scientific Awards and Grants: ANR JCJC grant — SiNCoBe (2021–2024) ANR Inception grant — BitesGoingViral (2022–2024) ANR PR[AI]RIE Springboard Chair (2021–2023) ACIP Inter-Institut Pasteur Concerted Actions grant — ETHOMOS (2019–2021) Pasteur-Roux-Cantarini postdoctoral fellowship (2017–2019) Copenhagen Graduate School pre-doc stipend (2008) Christian L. Vestergaard actively collaborates with principal investigators such as Jean-Baptiste Masson and François Laurent. He has been involved in advising PhD students and postdoctoral researchers within the lab, contributing to interdisciplinary projects that integrate physics, computer science, and biology. His group utilizes advanced computational tools and software such as TRamWAy and LarvaTagger for analyzing large-scale biological datasets. Laboratories and Research Teams: Decision and Bayesian Computation Lab, Institut Pasteur (current) Statistical Physics and Complex Systems team, CPT Marseille (former) Stochastic Systems and Signals group, DTU Nanotech (former)

• Mathematical Optimization
• Multiobjective Optimization
• Parametric Optimization
• +5 more

Margaret Wiecek is a Professor in the Department of Mathematical and Statistical Sciences at Clemson University, affiliated with the College of Science. She holds a PhD and MS in Systems Engineering and Electrical Engineering from AGH University of Science and Technology in Krakow, Poland. Her research focuses on mathematical optimization, particularly multiobjective optimization, distributed systems, and optimization under uncertainty. Applications span engineering design, portfolio optimization, and network routing. She has developed algorithms like the distributed computation of Pareto sets and robust multiobjective optimization frameworks. Wiecek's work emphasizes interdisciplinary collaboration, with contributions to fluid-structure interaction modeling and parametric optimization. Notable publications include studies on robust efficiency, biobjective Bayesian optimization, and decomposition methods for complex systems. Her research has been recognized through awards such as the MCDM Gold Medal (2019) and the Excellence in Discovery Faculty Award (2020). Education: PhD, Systems Engineering, AGH University of Science and Technology (Krakow, Poland) MS, Electrical Engineering, AGH University of Science and Technology (Krakow, Poland) Awards: Sofia Kovalevskaia Visiting Professorship (1995) Mercator Visiting Professorship (2002) MCDM Gold Medal (2019) Advising & Grants: Guided students achieving awards (e.g., Brian Dandurak, Garrett Dranichak) Recipient of Collaborative Award (2012) and Research Award (2014) Labs/Teams: Involved in multiobjective optimization research groups and interdisciplinary projects at Clemson University.

Jose M. VidalView profile

Professor

• Multiagent Systems
• Agent-based Modeling
• Algorithmic Game Theory
• +7 more

Jose M. Vidal is a Professor and Director of Undergraduate Studies in the Computer Science and Engineering department at the Molinaroli College of Engineering and Computing, University of South Carolina . He holds a Ph.D. in Computer Science and Engineering from the University of Michigan (1998), an M.S. in Computer Science from Rensselaer Polytechnic Institute (1991), and a B.S. in Computer Science and Engineering from MIT (1990). His research focuses on multiagent systems , spanning theoretical aspects like coordination mechanisms, algorithmic game theory, and distributed algorithms, as well as implementation in agent-based simulations, web services, and mobile applications. He has authored numerous publications in fields including healthcare process optimization, traffic engineering, and sociological theory construction. 2019: Ad Hoc Vehicle Platoon Formation (Traffic Engineering) 2019: Wikitheoria for Sociological Theory Construction 2018: Email Intent Classification using Deep Learning 2017: Intelligent Transportation Systems using Mobile Data 2016: Healthcare Workflow Simulation Models 2014: Behavioral Modeling from Observational Data He has received significant funding from the NSF, Darpa, and industry partners for projects like Wikitheoria (a collaborative theory-building platform), TargetShare (resource allocation), and mobile healthcare applications. His work bridges theoretical research with practical implementations in domains ranging from traffic systems to hospital operations.

Pier Luca LanziView profile

Professor

• Artificial Intelligence
• Machine Learning
• Multiagent Systems
• +7 more

Pier Luca Lanzi is a Full Professor at Politecnico di Milano, affiliated with the Department of Electronics, Information, and Bioengineering (DEIB). He leads the Artificial Intelligence and Robotics Lab (AIRLab), focusing on interdisciplinary research at the intersection of AI, robotics, and human-centric technologies. His work emphasizes applications in gaming, healthcare, and education, leveraging machine learning, evolutionary algorithms, and virtual reality. Education: Not explicitly stated in the provided texts. Affiliations: Director of the AIRLab; core member of DEIB. His research interests span artificial intelligence, multiagent systems, procedural content generation for games, serious games for rehabilitation, and AI-driven medical imaging. He explores how AI can enhance learning experiences (e.g., VR classrooms for physics and history) and improve therapeutic outcomes through interactive frameworks like the IGER system for stroke rehabilitation. Notable contributions include frameworks for automated level design in games (e.g., DOOM level generation via GANs), AI-assisted diagnostic imaging systems, and exergaming solutions for juvenile arthritis patients. His work often bridges computational intelligence with real-world applications, emphasizing human-centered design. Advising & Grants: While no specific grant details are provided, his prolific publication record suggests sustained research activity. Students and collaborators include prominent names in AI and robotics, though formal advisee名单 are not listed here. Labs/Teams: Directs the AIRLab, which develops cutting-edge technologies in AI, robotics, and human-technology interaction. Collaborates with industry and academic partners on projects ranging from VR educational tools to medical exergames.

Konstantinos D. BlekasView profile

Professor

• Machine Learning
• Autonomous Agents
• Computer Vision
• +1 more

Konstantinos D. Blekas is a Professor at the Department of Computer Science & Engineering , Polytechnic School, University of Ioannina. His career spans over 25 years with research focusing on Machine Learning , Autonomous Agents , Computer Vision , and Bioengineering . Education: Diploma in Electrical Engineering (1993) and PhD in Electrical & Computer Engineering (1997) from NTUA. Teaching: Offers courses like Probabilities & Statistics (MY304), Machine Learning (MYE002), Data Mining, and Bioinformatics. Research Interests include advanced machine learning techniques, reinforcement learning applications in traffic and robotics, image segmentation, and bioinformatics for medical data analysis. His work bridges theoretical innovation with practical implementations in autonomous systems and biomedical research. Recent Publications highlight trends in multiagent reinforcement learning , neural network optimization , and fMRI data modeling , with applications in robotics, air traffic management, and biomedical imaging. Collaborative projects often integrate spatial constraints and generative models. Scientific Awards Best Paper Award, DASC 2018 Best Student Paper Award, SETN 2018 2nd Winner, AIBIRDS 2014 Competition Professional Impact includes mentoring students, developing educational curricula, and cross-disciplinary research in medical AI. His work is widely cited (>1000 citations) with contributions to journals like Neural Networks , Medical Imaging , and AI Magazine .

• Applied Mathematics
• Optimization
• Autonomous Agents and Multiagent Systems
• +4 more

Thomas Evans is a Lecturer in Quantitative Methods at the Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), a position he has held since January 2019. He holds a PhD in Mathematics (2015) from UCL, supervised by Professors Steven Bishop and Frank Smith, with research focusing on agent-based modelling, game theory applications in search/patrol models, and mathematical foundations of these systems. Education: MSci Mathematics with a Year in Europe, Imperial College London (2003–2007) PGCE Postgraduate Certificate of Education, Canterbury Christ Church University (2009–2010) PhD Mathematics, UCL (2011–2015) Research Expertise: Dr. Evans specializes in applied mathematics, optimization techniques, and the development of models for complex systems. His work includes optimizing land use around Heathrow Airport using multi-objective linear programming, modeling international trade via input-output tables, and applying the sterile insect technique (SIT) to control dengue-carrying mosquitoes. He has contributed to projects such as ENFOLD-ing (EPSRC-funded), 3cixty (EU), and HARMONY (EU). Teaching and Experience: Prior to UCL, he taught mathematics in the UK, France, Kenya, and Ghana (2009–2011). His academic roles at UCL include Research Assistant (2014–2015), Research Associate (2015), and Teaching Fellow (2015–2019). Labs/Teams: Active within CASA, where he collaborates on interdisciplinary projects blending mathematical modeling with real-world urban and environmental challenges.