Find Academic Supervisors

Abhinav SinhaView profile

Assistant Professor

• Control Theory
• Artificial Intelligence
• Autonomous Systems
• +4 more

Dr. Abhinav Sinha is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati, leading the GALACxIS Lab. His research focuses on control theory, artificial intelligence, and dynamical systems to enhance safety, robustness, and autonomy in aerospace and robotic missions. Prior roles include postdoctoral positions at The University of Texas at San Antonio and Indian Institute of Technology Bombay. He completed his Ph.D. in record time (2 years) at IIT Bombay, earning the prestigious Naik and Rastogi Award. He is a Senior Member of IEEE, AIAA, and IFAC, and serves on the IEEE TC on Manufacturing, Automation, and Robotic Control. Education: Ph.D. in Aerospace Engineering, Indian Institute of Technology Bombay, 2021 M.Tech, Indian Institute of Technology Bombay Research Interests: Multicriteria Intercept Guidance and Control Multiagent Cooperative Pursuit-Evasion Resilience in Networked Systems Learning and Control in Autonomous Systems Multivehicle Motion Planning High-Fidelity Aerial Mobility Grants & Advising: PI: Air Force Research Lab grants on Collaborative Combat Aircraft (CCA) and Information-Driven Response Systems Advising focuses on guidance, autonomy, and control strategies Labs & Teams: GALACxIS Lab develops advanced control strategies for autonomous systems Collaborators include experts like Yongcan Cao and Shashi Ranjan Kumar

• Artificial Intelligence
• Reinforcement Learning
• Game Theory
• +5 more

Michael Bowling is a Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. His research focuses on artificial intelligence, reinforcement learning, and game theory, with applications in robotics and computer games. He leads the Computer Poker Research Group and developed the Arcade Learning Environment. Educational background: B.S. Math and Computer Science, Carnegie Mellon University (1996) M.Sc. Computer Science, Carnegie Mellon University (1999) Ph.D. Computer Science, Carnegie Mellon University (2003) Research areas: Multiagent learning and planning in imperfect information games Reinforcement learning for general competency Applications in commercial games and sports analytics Recent publications focus on continual learning, Markov decision processes, and exploration strategies in reinforcement learning. Teaches game design principles and computer game development. Research featured in Smithsonian exhibitions and media including New York Times and Scientific American.

Quanyan ZhuView profile

Associate Professor

• Game Theory
• Control Theory
• Cyber-physical Systems
• +7 more

Quanyan Zhu is an Associate Professor in the Department of Electrical and Computer Engineering at the NYU Tandon School of Engineering, affiliated with the NYU Center for Cybersecurity (CCS). His research focuses on game theory, control theory, cyber-physical systems, risk management, smart grids, and wireless networks. He leads the Laboratory for Agile and Resilient Complex Systems, exploring game-theoretic tools for system design. He is also part of the Center for Advanced Technology in Telecommunications (CATT) and the USDOT Tier 1 University Transportation Center C2SMARTER. Contact: quanyan.zhu@nyu.edu , Room 1004, 370 Jay Street, Brooklyn, NY. Research Interests: Game Theory & Strategic Decision-Making Cyber-Physical Systems Security Resilient Control Systems Smart Grid & Energy Networks Wireless Network Optimization AI/ML for Cybersecurity Labs & Collaborations: Laboratory for Agile and Resilient Complex Systems NYU Center for Cybersecurity (CCS) Center for Advanced Technology in Telecommunications (CATT) C2SMARTER (USDOT Tier 1 UTC) Key Contributions: Develops game-theoretic frameworks for cybersecurity, resilient control systems, and strategic AI applications. His work bridges theory and practice, addressing challenges in autonomous systems, cyber-physical security, and smart infrastructure.

• Artificial Intelligence
• Formal Methods
• Reinforcement Learning
• +6 more

Florent Delgrange is a postdoctoral researcher at Vrije Universiteit Brussel (VUB), working across multiple research units including R&D centraal and Federated labs AI and Robotics. He holds an unpaid teaching assignment in the School of Informatics and Applied Informatics. His research focuses on the intersection of artificial intelligence, formal methods, and reinforcement learning. Key areas include: Formal verification of deep reinforcement learning policies Controller synthesis from learned policies Multi-agent systems with formal guarantees Model checking for AI systems Generative modeling in reinforcement learning frameworks Dr. Delgrange's recent publications demonstrate a strong trend toward unifying reinforcement learning with formal methods to provide safety and reliability guarantees for AI agents. His work often combines deep learning approaches with formal verification techniques to ensure that learned policies satisfy specific temporal logic constraints. Dr. Delgrange is actively involved in the research community: Peer reviewer for AAAI Conference on Artificial Intelligence (2023) Member of programme committee for European Workshop on Reinforcement Learning (2023) Peer reviewer for International Conference on Autonomous Agents and Multiagent Systems (2023) Participant in multiple reinforcement learning workshops (2022-2023) He currently leads the research project "Reliable and Adaptive Learning Agents: Unifying Reinforcement Learning and Reactive Synthesis" (OZR4417) from 2025-2026, which aims to bridge the gap between reinforcement learning and formal synthesis techniques.

Michael BarlowView profile

Professor

• Serious Games & Games Technology
• AI & Multi-Agent Systems
• Simulation & Virtual Environments
• +4 more

Professor Michael Barlow is a distinguished academic at The University of New South Wales , specifically affiliated with UNSW Canberra and the School of Systems & Computing . His work spans multiple disciplines, including Artificial Intelligence , Multi-Agent Systems , Simulation & Virtual Environments , and Trusted Autonomy . Barlow’s research delves into Machine Learning and Human-Computer Interaction , with a particular focus on Speech Science and Technology in earlier stages of his career. University: The University of New South Wales School: School of Systems & Computing Role: Professor and Head of School His research interests include Serious Games , AI & Multi-Agent Systems , Trusted Autonomy , and Human-Computer Interaction . He has pioneered work in Swarm Intelligence and Simulation Technologies , applying these to domains such as military training , unmanned vehicle coordination , and game design . Barlow’s publications reveal a consistent trend in interdisciplinary work, blending Artificial Intelligence with Human Factors . His recent studies focus on Human-Swarm Interaction and Behavioral Metrics in multi-agent systems, while earlier works explore evolutionary game theory , speech processing , and virtual environments . He has contributed extensively to teaching in Core Computer Science for undergraduates and postgraduates, with specialized courses in Games & Serious Games , Simulation Graphics , and Algorithms . His consulting interests align with his research, emphasizing Simulation , Virtual Environments , and AI/Agent Systems .

Elad LiebmanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Multiagent Systems
• +5 more

Elad Liebman is an Assistant Professor of Instruction at the Department of Computer Science , University of Texas at Austin , with prior roles as a PhD student and researcher at UT Austin and Tel Aviv University. His research spans machine learning , artificial intelligence , multiagent systems , and computational creativity , often intersecting with music and human decision-making. His academic contributions include peer-reviewed publications on topics such as Distributed Multi-Agent Reinforcement Learning (AAAI-23) Music-Driven Decision Bias (Cognition and Emotion, 2017) Adaptive Playlist Generation (MIS Quarterly, 2019) He has received accolades like the Dean’s Excellence Award and Blavatnik School of Computer Science Excellence Prize . Elad has served as a Senior Data Scientist at SparkCognition (2018-2024) Applied Scientist at Amazon (2024) His technical expertise includes Python, Java, and C++, with fluency in Hebrew and English.

• Decision Neuroscience
• Behavioral Economics
• Cognitive Neuroscience
• +3 more

George Christopoulos is an Associate Professor at the Nanyang Business School (NBS), Nanyang Technological University (NTU), Singapore, with a PhD in Decision Neuroscience from the University of Cambridge. He is affiliated with the Decision Neuroscience Lab and active in interdisciplinary research. Research focuses on decision-making processes, behavioral economics, cognitive neuroscience, and affective science, integrating machine learning and environmental psychology. Recent work explores digital biomarkers for mental health, thermal control systems, energy-saving preferences, and cognitive fatigue through physiological and computational models. He serves as an Associate Editor for Cultural Psychology (Frontiers in Psychology) and a Review Editor for Decision Neuroscience (Frontiers in Neuroscience).

• Artificial Intelligence
• Machine Learning
• Autonomous Agents and Multiagent Systems
• +7 more

Professor Mirco Musolesi is a leading academic in Computer Science at both University College London (UCL) and the University of Bologna . He directs the Machine Intelligence Lab at UCL, which is affiliated with the UCL Centre for Artificial Intelligence and the Intelligent Systems Group . His research spans multiple disciplines including AI, cybersecurity, and computational modeling of human behavior. PhD from University College London (2007) Laurea from University of Bologna (2002) Research Interests : Mechanisms for autonomous decision-making in single/multi-agent systems AI ethics, societal impacts of LLMs, and moral alignment Machine learning for mobile/cyber-physical systems Intersections of AI with cybersecurity, privacy, and computational social science Recent Article Trends : His work focuses on reinforcement learning applications, causal discovery through graph-based methods, and ethical frameworks for AI systems. Emerging themes include creativity assessment in LLMs, cost-aware urban resource allocation, and conflict modeling through game-theoretic approaches. Teaching Contributions : Autonomous and Adaptive Systems (U. Bologna, Spring 2023-25) Engineering Challenges (UCL, Winter 2020-25) Research Methods (UCL, 2021-24) External Examiner roles at King's College London and U. Sussex

Matthijs SpaanView profile

Professor

• Safe and Robust Reinforcement Learning
• Planning Under Uncertainty
• Sequential Decision Making
• +5 more

Matthijs Spaan is a Professor of Reliable AI Algorithms at Delft University of Technology , where he co-directs the Sequential Decision Making group in the Department of Intelligent Systems (EEMCS faculty). Previously, he served as an Associate Professor in the Algorithmics group at TU Delft and as a Senior Research Scientist at the Institute for Systems and Robotics, Instituto Superior Técnico (Lisbon). He holds a PhD in Computer Science (2006) and an MSc in Artificial Intelligence (2002) from the University of Amsterdam. His research focuses on safe and robust reinforcement learning , planning under uncertainty using POMDPs/Dec-POMDPs, and sequential decision-making algorithms . He has contributed to embedding epistemic uncertainty in deep reinforcement learning, safe policy improvement, and constrained decision-making frameworks for smart energy systems, robotics, and traffic optimization. His work includes 15+ key publications in top venues like AAAI, IJCAI, ICAPS, and AAMAS, spanning 2024 to 2011. He has advised four PhD students to completion and co-organized major workshops such as the Lorentz workshop on Rigorous Automated Planning (2022) and AAAI 2022 program. His professional service includes editorial roles at AI Magazine and Journal of Artificial Intelligence Research , area chair positions, and leadership in IJCAI, ICAPS, and IFAAMAS.

Rasoul EtesamiView profile

Associate Professor

• Game Theory
• Networked Control Systems
• Multiagent Decision-Making
• +7 more

Rasoul Etesami is an Associate Professor in the Department of Industrial and Systems Engineering and holds affiliate appointments in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign . He earned his Ph.D. and M.Sc. degrees in Electrical and Computer Engineering from UIUC in 2015 and was a Postdoctoral Research Fellow at Princeton University (2016-2017). Education Ph.D. Electrical and Computer Engineering, University of Illinois Urbana-Champaign (2015) M.Sc. Applied Mathematics, University of Illinois Urbana-Champaign (2015) M.Sc. Industrial and Systems Engineering, University of Illinois Urbana-Champaign (2012) Research Interests Dr. Etesami's work focuses on multiagent decision-making systems using tools from game theory, control theory, and optimization. His research spans networked control systems , dynamic games , distributed optimization , and cyber-physical security . He investigates social network dynamics and smart grid applications , with recent emphasis on federated learning and adversarial robustness in machine learning systems. Recent Publication Trends His 2024-2025 publications demonstrate expertise in: Dynamic games for social network influence and resource allocation Federated learning algorithms and poisoning attacks Optimization techniques for over-parameterized models and caching Control theory applications to smart grids and opinion dynamics Submodular optimization for social welfare maximization Bandit learning in constrained Colonel Blotto games Honors & Editorships NSF CAREER Award (2020) AFOSR Young Investigator Award (2023) James Franklin Sharp Teaching Award (2023) Associate Editor, IET Smart Grid (2018-2024) Conference chair/co-chair for AAAI, Allerton, and CDC events Academic Service Dr. Etesami actively participates in departmental governance as a member of: Faculty Hiring Committee Qualification Exam Committee Graduate Committee Research and Scholarly Enhancement Committee Seminar Chair Organizer

Mohammad Reza MousaviView profile

Professor

• Software Testing
• Autonomous Systems
• Cyber-Physical Systems
• +7 more

Mohammad Reza Mousavi is a Professor of Software Engineering at the Department of Informatics , Faculty of Natural, Mathematical & Engineering Sciences , King's College London. His research focuses on Software Testing , Autonomous Systems , Cyber-Physical Systems , Software Product Lines , and Formal Semantics and Verification . Key projects include ITEA GENIUS (2025–2028), GreenCode (2024–2027), and QAssure (2024–2026), addressing topics like quantum software verification and ethical autonomous systems . Recent work explores carbon footprint analysis in code development, doping detection in concurrent systems, and trust calibration in AI-enabled systems. He contributes to IEEE TRANSACTIONS ON SOFTWARE ENGINEERING , ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY , and workshops like ResponsibleSE 2025 and CONCUR 2025 . Collaborations span institutions including Reykjavik University , Eindhoven University of Technology , and Chalmers University of Technology .

• Intelligent Agents
• Probabilistic Graphical Models
• Deep Learning
• +3 more

Prof. Yifeng Zeng is a Professor and Head of Research & Knowledge Exchange&UoA 11 Lead in Northumbria University's Department of Computer and Information Sciences. He leads the Autonomous Systems research group, focusing on intelligent agents, deep learning, computational intelligence, and digital education. With a PhD in Engineering (2005), he has secured the EPSRC New Investigator Award (2019) and led Innovate UK projects. His work spans multiagent systems, healthcare AI applications, and data-driven decision models. Affiliations: Northumbria University, Autonomous Systems Group Research Interests: His primary areas include probabilistic graphical models, reinforcement learning in multiagent systems, and AI-driven healthcare solutions like optical coherence tomography (OCT) for cancer diagnosis. He explores ethical AI through policy legibility and interpretable decision-making frameworks. Publications: Recent work emphasizes multiagent learning strategies, spiking neural networks, and deep learning applications in medical imaging. His 2025 articles highlight advancements in legible agent policies and cooperative exploration methods. Awards: EPSRC New Investigator Award (2019) Grants & Supervision: Co-Investigator on the Turing Network Development Award (2022). Supervises 2 researchers, focusing on autonomous systems and AI ethics. Leads projects integrating machine learning with healthcare diagnostics and educational technology. Labs & Teams: Directs the Autonomous Systems group, collaborating on typhoon response systems and medical imaging AI. Active in interdisciplinary projects blending robotics, computer vision, and decision theory.

• Autonomy
• Reinforcement Learning
• Artificial Intelligence and Machine Learning
• +4 more

Huy Trong Tran is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), Department of Aerospace Engineering. He holds academic positions including a concurrent role at the Applied Research Institute (0%) and a full-time (100%) position at UIUC. His research focuses on autonomy, reinforcement learning, robotics, and resilience engineering in critical infrastructure systems. Education: Ph.D. and M.S. in Aerospace Engineering from Georgia Tech (2015/2014), M.S. in Mechanical Engineering from University of Wisconsin-Madison (2010), B.S. in Mechanical Engineering from North Carolina State University (2008, Magna Cum Laude). Research Interests: Tran's work spans autonomy in robotics, multi-agent systems, and resilience analysis of transportation and power networks. He develops AI-driven solutions for decision-making under uncertainty, with applications in aviation, field robotics, and infrastructure resilience. His methods include reinforcement learning, complex network frameworks, and predictive modeling using graph neural networks. Teaching: He has taught courses such as AE 498 CSE/CSO (Computational Systems Engineering), AE 443/442 (Aerospace Systems Design), and AE 370 (Aerospace Numerical Methods). His instruction emphasizes computational methods and systems engineering principles. Awards: Received the Best Paper Award (Theoretical Track) at the 2016 International Symposium on Resilient Control Systems for work on resilient systems frameworks. Labs/Teams: Active in research groups focused on autonomous systems, resilience engineering, and AI-driven decision-making. Collaborates with institutions like MITRE Corporation and the Air Force Institute of Technology.

• Artificial Intelligence
• Social AI
• Human-Computer Interaction
• +5 more

Bernhard Hilpert is a Visiting Fellow at the Faculty of Science, Vrije Universiteit Amsterdam, specializing in Artificial Intelligence and Social AI. His research bridges machine learning and healthcare applications. Research Interests: Focuses on Reinforcement Learning agents' transparency Virtual therapeutic agents for mental health Human-AI interaction models Biofeedback and facial expression analysis Patient-centered healthcare technology Recent Work Trends: Combines AI with clinical applications, particularly in outpatient depression care and multiagent system modeling. Collaborations: Works with healthcare professionals and AI researchers on deploying virtual agents in therapeutic contexts.

Peter StoneView profile

Professor

• Reinforcement Learning
• Multiagent Systems
• Robotics
• +7 more

Peter Stone is a Professor of Computer Science at The University of Texas at Austin, holding the Truchard Foundation Chair. He leads the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory and serves as Director of Texas Robotics and Associate Department Chair. His research focuses on reinforcement learning, multiagent systems, and robotics, emphasizing adaptive and interactive intelligent agents. Stone earned his Ph.D. from Carnegie Mellon University and has held roles at Sony AI as Chief Scientist and Cogitai, Inc. as co-founder. Education: Ph.D. in Computer Science (Carnegie Mellon University, 1998), B.S. in Mathematics with Computer Science (University of Chicago, 1993). Research Interests: Reinforcement Learning, Multiagent Systems, Robotics, AI Planning, and Ethical AI. His work addresses challenges in autonomous agents, including robot soccer, autonomous vehicles, and Gran Turismo racing, often leveraging real-world applications. Recent Articles: Focus on reinforcement learning advancements, multiagent coordination, and real-world robotics deployments. Notable works include champion-level autonomous driving agents and proto successor measure theory. Awards: ACM/SIGAI Industry Award, multiple Sony Technology Awards, ACM and IEEE Fellowships, and teaching accolades like the Minnie Stevens Piper Professorship. Advising: Supervised over 50 Ph.D., master’s, and undergraduate students, contributing widely to AI and robotics research. Current projects include the UT Austin Villa robot soccer team and Sony AI collaborations. Labs/Teams: LARG, Texas Robotics, and Sony AI. Active in RoboCup competitions and AI policy initiatives like the One Hundred Year Study on Artificial Intelligence.