Find Academic Supervisors

Xu JinView profile

Assistant Professor

• Adaptive Control
• Iterative Learning Control
• Stochastic Control
• +8 more

Xu Jin is an Assistant Professor in the Department of Mechanical Engineering at the University of Kentucky, affiliated with the Stanley and Karen Pigman College of Engineering. His research focuses on advanced control systems, including adaptive control, iterative learning control, and fault-tolerant mechanisms for multiagent systems, robotics, and autonomous vehicles. He holds a Ph.D. in Aerospace Engineering from Georgia Institute of Technology, along with degrees in Mathematics, Electrical and Computer Engineering, and related fields from prestigious institutions. Education: Ph.D. in Aerospace Engineering, Georgia Institute of Technology (Atlanta, GA) M.S. in Mathematics, Georgia Institute of Technology M.A.Sc. in Electrical and Computer Engineering, University of Toronto (Toronto, ON, Canada) B.Eng. in Electrical and Computer Engineering, National University of Singapore Research Interests: Dr. Jin's work emphasizes Adaptive Control , Iterative Learning Control , and Cooperative Control for cyber-physical systems, with applications in robotics, autonomous vehicles, and multiagent coordination. He investigates constraints such as dynamic path dependencies, safety margins, and resilience against cyberattacks. His methodologies often integrate stochastic control, nonlinear systems theory, and consensus protocols to ensure robust performance under uncertainty. Publications: His recent work highlights contributions to constrained formation control of quadrotor teams, attacker-resilient adaptive systems, and fixed-time control strategies for autonomous vehicles. These studies address real-world challenges like obstacle avoidance, load transportation, and environment-aware path following. Advising & Grants: No student advisees or grants are explicitly listed in the provided text. His research is likely supported through institutional and external funding mechanisms aligned with his technical focus areas. Labs/Teams: While not explicitly named, his affiliation with the Stanley and Karen Pigman College of Engineering suggests involvement in robotics, autonomous systems, and control engineering research groups.

• Federated Learning
• Blockchain Technology
• Internet of Vehicles
• +7 more

Hao Chen is a Professor at the University of Chinese Academy of Sciences, School of Artificial Intelligence, with significant contributions across multiple research domains. His work spans computer science, networking, and artificial intelligence with applications in transportation, healthcare, and industrial systems. His primary research interests include Federated Learning , Blockchain Technology , Internet of Vehicles , Satellite Networks , Resource Allocation , and UAV Systems . His research program focuses on developing novel algorithms and frameworks for distributed systems, edge computing, and intelligent networking solutions. Recent work demonstrates particular strength in privacy-preserving techniques, multi-agent systems, and real-world applications of AI in transportation and healthcare domains. Analysis of his recent publications (2024-2025) reveals a strong emphasis on practical applications of theoretical concepts, with particular attention to vehicular networks, satellite communications, and medical imaging. His work frequently combines deep learning with traditional optimization techniques to address complex real-world problems. The interdisciplinary nature of his research connects computer science with civil engineering, medical diagnostics, and transportation systems. His leadership in federated learning and blockchain applications for Internet of Vehicles has established him as a significant contributor to these emerging fields. Recent publications show increasing collaboration with both academic and industry partners across multiple continents.

• Artificial Intelligence
• Multiagent Systems
• Social Computing
• +5 more

Munindar P. Singh is a Professor affiliated with North Carolina State University. His research focuses on multiagent systems, normative systems, cybersecurity, and ethical AI. He has contributed extensively to the development of protocols for decentralized systems, computational governance, and the application of AI in socially responsible contexts. Key Roles: Leading research in multiagent systems, norms, and sociotechnical systems. Affiliations: North Carolina State University. His work spans theoretical frameworks for agent collaboration, practical applications like microtransit optimization, and ethical considerations in AI. Recent studies include analyzing social media dynamics, deception strategies in cybersecurity, and moral reasoning in narratives. Publications reflect interdisciplinary impact across AI, computer science, and social computing. Notable areas include norm emergence, cyber deception via hypergame theory, and ethical frameworks for autonomous systems.

Jamie MatthewsView profile

Senior Lecturer

• Interactive Storytelling
• Narrative Generation
• Mise-en-scène
• +3 more

Dr. Jamie Matthews is a Senior Lecturer in Games Development at the Department of Computing & Games, Teesside University, affiliated with the Centre for Digital Innovation. His research focuses on interactive storytelling, narrative generation, and virtual student behavior modeling. He holds a Doctoral Thesis in Crowd Simulation for Interactive Entertainment Applications (2021). Education: PhD in Computer Science (2021) from Teesside University, supervised by Prof. John Porteous and Dr. Christopher Jayne. Research interests include Mise-en-scène techniques for narrative actions, benchmarking frameworks for virtual agents, and semantic representation in storyworlds. His work bridges computer science and digital arts, emphasizing procedural storytelling and multiagent systems. Publications span conference contributions on virtual student behavior benchmarks and narrative staging systems. His MISER framework has been highlighted in multiagent systems research. Awards: No specific honors listed. Active in grant-funded projects related to interactive entertainment applications. Labs/Teams: Involved with the Centre for Digital Innovation, focusing on digital innovation in games and interactive technologies.

Hashem AshrafiuonView profile

Professor

• Nonlinear Control Systems
• Autonomous Vehicles
• Underactuated Systems
• +7 more

Dr. Hashem Ashrafiuon is a Professor in the Department of Mechanical Engineering at Villanova University's College of Engineering. He directs the Heterogeneous Vehicles Lab (HAVLab) and the Unmanned Surface and Underwater Vehicles Laboratory, leading research in nonlinear control systems for autonomous vehicles and biomedical applications. Educational Background: Bachelor of Science in Mechanical Engineering Master of Science in Mechanical Engineering Doctor of Philosophy in Mechanical Engineering All degrees earned at State University of New York at Buffalo Research Focus: Dr. Ashrafiuon specializes in advanced control systems, with key areas including: nonlinear control, networked control of heterogeneous autonomous vehicles, underactuated system dynamics, and EEG-based diagnostics for neurological disorders. His work bridges engineering principles with biomedical applications, particularly in mobility assistance robotics and brain disorder analysis. Scientific Recognition: ASME Fellow IEEE Senior Member National Research Council Fellow (twice selected) Recognized as one of the Top 25 Mechanical Engineering Professors by Online Engineering Programs Senior Editor, Journal of Vibration and Control Associate Editor, Journal of Dynamic Systems, Measurement and Control Academic Contributions: Dr. Ashrafiuon has mentored multiple PhD students and led numerous research projects funded by government and private entities. His publications demonstrate expertise in spacecraft docking, autonomous navigation, and biomedical signal processing, with recent work focusing on: extremum seeking control for antenna pointing, iterative control frameworks for rockets, and cooperative localization systems for multi-vehicle networks.

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +5 more

Zhen Tang is an active researcher with a prolific publication record spanning from 2007 to 2025, primarily in computer science and engineering disciplines. Their work appears consistently in high-impact venues including IEEE Access, IEEE Transactions, and major conferences in computer vision and systems engineering. Research interests span computer vision, control theory, biomedical image analysis, machine learning, and multi-agent systems. Tang's work demonstrates a strong interdisciplinary approach, connecting theoretical control systems with practical applications in medical imaging, distributed computing, and emerging technologies like DNA computing. Recent publications show a growing interest in large language models, blockchain applications, and ethical considerations in technology design. The publication trends reveal an evolution from foundational work in image processing and pattern recognition (2010-2015) to more complex systems involving multi-agent control and deep learning (2016-2020), and most recently expanding into large language models, blockchain healthcare applications, and technology ethics. The research shows strong connections between theoretical control systems and practical applications across multiple domains. Zhen Tang has established long-term collaborations with researchers including Yanli Wan, Zhenjiang Miao, Wei Wang, and others, suggesting stable research group affiliations. The consistent publication output across 18 years indicates an established academic career with significant contributions to multiple subfields within computer science and engineering.

Nelson RodriguesView profile

Researcher

• Intelligent Manufacturing
• Reconfigurable Manufacturing Control Systems
• Artificial Intelligence
• +5 more

Nelson Rodrigues is a Researcher at the Polytechnic Institute of Bragança (IPB) and a Ph.D. candidate at the University of Porto, conducting research since 2010 with involvement in H2020 PERFoRM and GO0D MAN projects, as well as FP7 ARUM and GRACE initiatives. His academic credentials include: Master of Science in Information Systems, Polytechnic Institute of Bragança His research centers on Intelligent and Reconfigurable Manufacturing Control Systems, investigating Artificial Intelligence applications for self-adaptive manufacturing evolution. Additional expertise spans Industry 4.0, Cyber-physical systems, Multiagent systems, Simulation, and Advanced Data Analytics, with emphasis on enhancing manufacturing flexibility through cutting-edge computational methods. Recent publication analysis reveals a strategic pivot toward energy systems optimization, particularly virtual power plants, utilizing high-performance computing, quantum annealing, and evolutionary algorithms. Concurrent research explores intelligent transportation frameworks, assistive robotics, and manufacturing business analytics, demonstrating interdisciplinary technical agility. He maintains active affiliations with LIACC - Artificial Intelligence and Computer Science Laboratory and the IEEE Technical Committee on Industrial Agents, contributing to over 29 publications with an h-index of 11. Research funding derives from European Union projects including H2020 PERFoRM and GO0D MAN, and FP7 ARUM and GRACE, where he developed intelligent manufacturing control architectures and service reconfiguration systems. Within LIACC and IEEE collaborations, he advances industrial agent technologies for real-time manufacturing adaptation, focusing on dynamic reconfiguration and human-in-the-loop cyber-physical production systems.

• Multi-Agent Systems
• Human-Robot Interaction
• Game Theory
• +7 more

Katia Sycara is a Research Professor at Carnegie Mellon University's Robotics Institute, part of the School of Computer Science. She also holds the Sixth Century Chair (part-time) in Computing Science at the University of Aberdeen, UK. Her research focuses on multi-agent systems, human-robot interaction, game theory, and adversarial reasoning. She leads the Advanced Agent-Robotics Technology Lab and has developed frameworks like the RETSINA multiagent infrastructure. Education: B.S. in Applied Mathematics (Brown University), M.S. Electrical Engineering (University of Wisconsin), Ph.D. Computer Science (Georgia Tech), Honorary Doctorate (University of the Aegean, 2004). Research interests include semantic web services, agent interoperability, crisis response systems, and negotiation support. She has pioneered work on the DAML-S language for semantic web services and contributed to the OASIS UDDI standard. Notable awards include the 2002 ACM SIGART Agents Research Award and AAAI Fellowship. She has led multimillion-dollar projects funded by DARPA, NASA, AFOSR, and ONR, focusing on applications like autonomous robotics, urban search and rescue, and enterprise integration. Awards: ACM SIGART Autonomous Agents Research Award (2002) AAAI Fellow (2002) IEEE Fellow (2013) Outstanding Alumnus (University of Wisconsin, 2005) Grants and Projects: Command and Control of Multi-Robot Teams (ONR) Games on Graphs (MURI/ONR) Urban Search and Rescue (NASA) Labs/Teams: Director of Advanced Agent-Robotics Technology Lab and contributor to the Semantic Web Science Association.

Adam EckView profile

Associate Professor

• Multiagent Systems
• Machine Learning
• Computational Social Science
• +3 more

Adam Eck is the David H. and Margaret W. Barker Associate Professor of Computer Science and Chair of the Data Science Integrative Concentration at Oberlin College’s College of Arts & Sciences. His research focuses on multiagent decision-making in complex environments, machine learning applications in computational social science and public health, and AI-driven solutions for challenges such as wildfire suppression, cybersecurity, and survey informatics. He teaches courses like Applied Machine Learning (CSCI/DATA 373) and Data Science Research (DATA 501). Key research projects include developing AI systems for open multiagent environments, using machine learning to address public health crises (e.g., opioid epidemic, COVID-19), and leveraging chatbots to enhance survey design. Eck has pioneered work on decision-theoretic planning for autonomous systems and has explored innovative sampling methods for social media platforms like Twitter. His work bridges computer science with interdisciplinary domains, emphasizing ethical and practical applications of AI. Notable contributions include collaborations on automated decision-making frameworks funded by NSF grants and the development of algorithms for community-level health analysis. Eck was recognized as a Scientific Teaching Fellow by Yale’s Summer Institute in 2018 for advancing inclusive pedagogical practices. Recent publications highlight breakthroughs in generative AI for survey design, scalable multiagent planning, and the integration of Big Data with traditional survey science. His research often addresses societal challenges through technical innovation, reflecting a commitment to both academic rigor and real-world impact.

Edward LamView profile

Research Fellow

• Combinatorial Optimization
• Neuro-Symbolic AI
• Operations Research
• +3 more

Edward Lam is a Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His research focuses on the intersection of combinatorial optimization and neuro-symbolic AI, developing algorithms for routing, scheduling, control, and analytics, primarily applied to automated decision-making in transportation and logistics. He holds a PhD in Computer Science (2019) and a BSc(Hons) in Mathematics and Statistics (2010), both from the University of Melbourne. Lam is affiliated with CSIRO Data61 and actively supervises PhD students in optimization and machine learning. **Education**: PhD in Computer Science, University of Melbourne (2019) BSc(Hons) in Mathematics and Statistics, University of Melbourne (2010) **Research Interests**: Combinatorial optimization, neuro-symbolic AI, multi-agent systems, and applications in autonomous logistics. His work addresses challenges in routing, scheduling, and decision-making under constraints. **Article Trends**: Recent publications emphasize branch-and-cut-and-price algorithms for multi-agent pathfinding, electric vehicle routing, and hybrid optimization methods. His work bridges theoretical algorithm design with real-world applications in transportation and warehousing. **Awards**: Received the Doctoral Research Award (2019) for outstanding contributions to optimization algorithms. **Advising & Grants**: Currently accepting PhD students focused on combinatorial optimization and machine learning, with available scholarships. Collaborates on projects funded by industry and governmental entities like CSIRO. **Affiliations**: Maintains an external position at CSIRO Data61 and has contributed to patents in optimization algorithms (2018).

• Cloud Computing
• Blockchain Technology
• Data Security
• +5 more

Xiaojun Zhang is a Professor actively contributing to cloud computing, blockchain technology, data security, and educational technology. His work spans cybersecurity, signal processing, and wireless systems. Key Research Areas: Privacy-preserving data aggregation, machine learning for biomedical imaging, blockchain-based integrity auditing, and educational metacognition studies. Recent Article Trends (2022–2025): Focus on secure federated learning, data denoising algorithms, and blockchain applications in smart grids, healthcare, and education. Collaborations include institutions in China and international researchers.

• Game Theory
• Reinforcement Learning
• Multiagent Systems
• +3 more

Bolin Gao is an Adjunct Assistant Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His research focuses on game theory, reinforcement learning, and optimization algorithms with applications in multiagent systems and control theory. He explores topics such as equilibrium analysis, convergence dynamics, and stability in complex systems through continuous-time models and second-order methods. Key research interests include: Game dynamics and equilibrium computation in noncooperative games Reinforcement learning in multiagent environments Optimization algorithms for concave games and potential games Passivity theory and stability analysis in learning systems His recent work emphasizes second-order mirror descent methods and their convergence properties beyond traditional equilibrium assumptions. Articles from 2020-2023 demonstrate advancements in discounted dynamics, higher-order learning, and exact convergence analysis. No scientific awards were explicitly mentioned in the provided text. His academic contributions focus on theoretical foundations of game-theoretic learning and optimization frameworks.

• Transportation
• Logistics
• Sustainable Infrastructure
• +3 more

Iftikhar Hussain serves as a Research Associate within the School of Social Sciences, specifically at the Edinburgh Business School of Heriot-Watt University in Edinburgh, United Kingdom. His academic profile centers on interdisciplinary research bridging engineering, social sciences, and sustainable development, with significant contributions to UN Sustainable Development Goals through transportation and infrastructure innovation. His research interests focus on sustainable urban systems, emphasizing transportation optimization, cold chain logistics, and smart city applications. Key methodologies include multiagent simulation, negotiation modeling, and machine learning techniques applied to carpooling networks, waste management, UAV communications, and cognitive radio security. This work consistently addresses real-world challenges in resource efficiency and sustainable infrastructure planning. Analysis of his 23 publications (2014-2025) reveals strong interdisciplinary trends, with recent work (2022-2025) showing increased output in sustainable infrastructure and wireless communications. His publications demonstrate consistent collaboration across engineering and social science domains, with notable impact in transportation modeling (63 citations for 2024 smart city waste management study) and emerging contributions to UAV communications and cold chain sustainability. No scientific awards are documented in available records. Regarding academic mentoring and funding, no information about student supervision or research grants appears in current profiles, though his collaborative publication record indicates active research partnerships. His ORCID (0000-0003-3360-4532) shows engagement with international research networks, particularly in sustainable infrastructure and transportation systems. No dedicated laboratory or research team leadership is specified in current documentation, though his work involves multi-institutional collaborations evidenced by co-authorship patterns across publications in transportation, wireless networks, and sustainable urban development.

Kate LarsonView profile

Professor

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +3 more

Kate Larson is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Her research explores artificial intelligence with emphasis on multiagent systems, reinforcement learning, and AI applications for sustainability and climate challenges. She employs game-theoretic approaches to address coordination and cooperation in complex systems. Education Ph.D. in Computer Science from Carnegie Mellon University (2004), M.Sc. from Washington University in St. Louis (1999), and B.Sc. from Memorial University of Newfoundland (1997). Research Focus Professor Larson's work bridges theoretical AI and practical societal impacts. Key areas include: Designing cooperative AI frameworks for multiagent environments Developing value-alignment mechanisms for ethical AI Applying reinforcement learning to climate change mitigation Creating robust AI systems through game-theoretic guarantees Publication Trends Her recent publications (2024-2025) demonstrate strong focus on AI safety, game-theoretic foundations, and multimodal learning systems. Dominant themes include: algorithmic alignment techniques, multi-agent risk management, and interpretable AI architectures, with increasing emphasis on real-world climate and sustainability applications. Awards & Recognition No major scientific awards mentioned in source materials. Research Infrastructure No specific labs or teams detailed in available information.

• Quantum Systems Modeling
• Formal Methods for Computer Security
• Access Control
• +3 more

Lorenzo Ceragioli is an Assistant Professor (RTD-A) in Computer Science at IMT Lucca within the SySMA research unit. Previously, he was a postdoctoral researcher at the University of Pisa's Department of Computer Science, where he also earned his PhD in Computer Science. His work focuses on quantum systems modeling, formal verification methods for security protocols, and access control systems. University Affiliation: IMT Lucca Research Unit: SySMA Research interests include: Quantum process verification through bisimulation techniques Analysis of SELinux security policies Logical modeling of multi-agent systems Recent work has explored firewall policy translatability and information flow control in SELinux configurations. His tools like SELinuxIFCIL and F2F address policy verification and firewall system compatibility challenges. Key contributions include: Developed IFCIL extension for SELinux policy verification Created F2F tool for firewall policy expressiveness analysis Advanced quantum process bisimulation theories