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Simone D’AmicoView profile

Associate Professor

• Autonomous Spacecraft
• Distributed Space Systems
• Space Technology Policy
• +3 more

Simone D’Amico is a Science Fellow at the Hoover Institution and an Associate Professor of Aeronautics and Astronautics (with a courtesy appointment in Geophysics) at Stanford University. He serves as the W. M. Keck Faculty Scholar in the School of Engineering and directs the Stanford Space Rendezvous Laboratory , co-directs the Center for Aerospace Autonomy Research (CAESAR) , and leads the undergraduate aerospace engineering program at Stanford. Expertise: Autonomous spacecraft, distributed space systems, space technology policy Funding: NASA (Starling), National Science Foundation (VISORS, SWARM-EX) Advisory Roles: Four space startups focusing on SAR remote sensing, orbital lifetime prolongation, and space-based solar power Prior Affiliation: German Aerospace Center (research scientist and team leader) Research Interests center on autonomous satellite swarms, multiagent architectures, and responsible space policy. His work bridges aerospace engineering and geophysics, emphasizing sustainable space exploration and commercial-military space governance. Scientific Contributions include developing guidance, navigation, and control systems for distributed space missions. His recent publications address: 2025: Export control reforms and commercial space innovation 2024: Ethical frameworks for space preservation Awards highlight his technical and policy impact, including NASA honors, IEEE and AIAA best paper awards, and the Leonardo 500 Award.

Saurabh Dilip VaishampayanView profile

Researcher

• Systems Control
• Multiagent Optimization
• Control Systems
• +4 more

Saurabh Dilip Vaishampayan is a Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the Department of Mechanical Engineering. He works at the SYCAMORE research laboratory, focusing on systems control and multiagent optimization research. Additionally, he is a doctoral student in the Doctoral Program in Electrical Engineering at EPFL. Education: Doctoral Program in Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL) Research Interests: Systems Control Multiagent Optimization Control Systems Optimization Electrical Engineering Robotics Artificial Intelligence Contact: Email: saurabh.vaishampayan@epfl.ch Phone: +41 21 695 81 48 Office: ME C1 409

• Artificial Intelligence
• Machine Learning
• Technoculture
• +4 more

Ramon Sangüesa Sole is a Professor at Universitat Politècnica de Catalunya (UPC) within the School of Computer Science and Department of Computer Science . He is a member of the IDEAI-UPC (Intelligent Data Science and Artificial Intelligence Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). Fields of Interest : Artificial Intelligence, Machine Learning, Technoculture, Science Communication, Social Innovation Education : Doctor in Computer Science (UPC) His research focuses on integrating AI with social and environmental contexts, developing algorithmic transparency frameworks, and creating sociotechnical systems. Key projects include democratizing AI through citizen participation and applying machine learning to urban planning and educational innovation. His work emphasizes ethical considerations and interdisciplinary collaboration. Recent publications analyze topics like algorithmic transparency, urban AI applications, and student engagement technologies. Awards include the DISTINCIÓ JAUME VICENS VIVES for research excellence and the 8è Premi a la Qualitat en la Docència Universitària for educational innovation. Sangüesa leads projects in AI-driven design, environmental decision support systems, and personalized learning frameworks.

Hassan ZargarzadehView profile

Associate Professor

• Robotics
• Intelligent Control Systems
• Power Electronics
• +7 more

Dr. Hassan Zargarzadeh is an Associate Professor in the Phillip M. Drayer Department of Electrical and Computer Engineering at Lamar University, specializing in Robotics and Intelligent Control Systems. His research spans multiagent systems, underwater robotics, wireless vehicle charging, and adaptive control algorithms. Ph.D. in Electrical Engineering, Missouri University of Science and Technology, 2012 M.S. in Electrical Engineering, Iran University of Science and Technology, 2009 B.S. in Electrical Engineering, Amir Kabir University, 2000 Dr. Zargarzadeh leads the Robotics and Intelligent Control Systems (RICS) Lab, focusing on: Advanced control methods for uncertain nonlinear systems Mechatronics and robotics design Power electronics and vehicle charging systems Swarm robotics and game-theoretic path planning His recent research trends include applying neural networks to autonomous systems, developing vision-based navigation for underwater robots, and optimizing wireless power transfer for electric vehicles. Collaborative projects span environmental monitoring, energy systems, and multiagent control. Scientific contributions include: Best presenter runner-up at ISC-supported research poster presentation, 2010 Top 3% M.S. student in Iran, 2008 National exam rankings in 1995 (top 100) and 2006 (top 800) Advising and grants: Advised 10+ graduate students in robotics and control $35,000 CAPM grant (2017) for bunker fuel optimization $25,000 CAWAQ grant (2016) for invasive species detection

Sanmay DasView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computational Social Science
• +2 more

Sanmay Das is a Professor of Computer Science at Virginia Tech's College of Engineering and Associate Director of AI for Social Impact at the Sanghani Center for Artificial Intelligence and Data Analytics. He chairs the ACM Special Interest Group on Artificial Intelligence and contributes to editorial leadership as Associate Editor for multiple journals. Ph.D. in Computer Science (MIT) M.S. in Electrical Engineering and Computer Science (MIT) A.B. in Computer Science (Harvard University) His research focuses on AI for public services/resources (homeless services, K-12 education, organ transplantation) and AI-mediated human systems analyzing algorithmic impacts on societal inclusion and information exposure. At Virginia Tech, he expands initiatives in responsible AI through partnerships with nonprofits, government, and industry. Das has received prestigious recognition including NSF CAREER Award, ACM Distinguished Member status, and teaching/service awards from Washington University and George Mason University. He leads the 2025 International Conference on Autonomous Agents and Multiagent Systems and participates in DARPA's Information Science and Technology Study Group.

Ulle EndrissView profile

Professor

• Artificial Intelligence
• Collective Decision Making
• Computational Social Choice
• +5 more

Ulle Endriss is a Professor of Artificial Intelligence and Collective Decision Making at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam. He leads the COMSOC Group and is a member of both the Theoretical Computer Science research unit of the ILLC and the Centre for Explainable, Responsible and Theory-driven AI (CERTAIN). Endriss has held academic positions since 2005, progressing from Assistant Professor (2005-2011) to Associate Professor (2011-2019) and finally Full Professor since 2019. Endriss's research focuses on formal methods in AI, particularly in multiagent systems and knowledge representation, with special interest in computational social choice—the intersection of AI with economics and political science. His work spans voting theory, fair division, participatory budgeting, and game theory. He is actively involved in the ADDI project on digital democracy and regularly contributes to major AI conferences including AAMAS, IJCAI, and AAAI. His recent publications demonstrate a strong focus on computational social choice, with particular emphasis on voting systems, participatory budgeting mechanisms, and the theoretical foundations of collective decision making. His research combines theoretical rigor with practical applications in democratic processes and resource allocation problems. Runner-up for the Best Paper Award (2023) Best Paper Award (2016) Best Paper Award (2012) Best Paper Award (2005) Finalist for the Best Paper Award (2014) Finalist for the Best Paper Award (2008) IJCAI-JAIR Best Paper Prize 2016 Endriss regularly teaches courses on Computational Social Choice and Game Theory for MSc Logic and MSc AI programmes. He makes all his teaching materials openly available, consistent with his commitment to open access in both research and education. He has supervised numerous Master's theses that have led to published research. His work extends to outreach through platforms like Game-Academy.org and Pabuviz.org, which help explain complex voting concepts to broader audiences.

• Autonomous Navigation for Robots
• Deep Learning
• Artificial Intelligence
• +7 more

Jesús Tordesillas Torres is an Assistant Professor in the Department of Electronics, Automation, and Communications at the School of Engineering, Comillas Pontifical University. He joined the institution in June 2024, bringing extensive experience from postdoctoral research at MIT and ETH Zurich, and prior academic training from MIT and the Polytechnic University of Madrid. Education: PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 2022 MS in Aeronautics and Astronautics, MIT, 2019 MS in Industrial Engineering, Polytechnic University of Madrid, 2019 BS in Industrial Engineering, Polytechnic University of Madrid, 2016 His research focuses on robotics, particularly autonomous navigation, trajectory planning, and optimization under uncertainty. He integrates deep learning and control theory to develop systems capable of safe, fast, and perception-aware navigation in dynamic and unknown environments. His work spans aerial and ground robots, multiagent systems, and challenging terrains, with strong emphasis on real-world deployment and robustness. The recent publications highlight a consistent trend in trajectory optimization, perception-aware planning, and multiagent coordination. His work bridges theoretical advances in optimization and learning with practical robotic applications, especially in safety-critical and communication-constrained scenarios. Scientific Awards: Best Paper Award, IEEE ICRA 2023 1st Place, Urban Circuit, DARPA Subterranean Challenge (2020) 2nd Place, Tunnel Circuit, DARPA Subterranean Challenge (2021) Finalist, Best Paper, IEEE IROS 2019 Jesús Tordesillas Torres has been actively involved in research grants and projects, including the ADS FERRARI WP-2 Project funded by Airbus Defence and Space (2025–2025). He mentors students and collaborates with leading institutions such as MIT, ETH Zurich, and the University of Pennsylvania. He also serves as a reviewer for top-tier journals including IEEE Transactions on Robotics , International Journal of Robotics Research , and IEEE Robotics and Automation Letters , as well as major conferences like ICRA and IROS. He leads and participates in research teams focused on autonomous systems, with affiliations to the Institute for Research in Technology (IIT) at Comillas. His invited talks at ETH Robotics Summer School and University of Pennsylvania reflect his growing influence in the robotics community.

Dennis GrinwaldView profile

Researcher

• Probabilistic Machine Learning
• Distributed Deep Learning
• Explainable AI
• +3 more

Dennis Grinwald is a doctoral researcher at the Technical University of Berlin affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Master of Science in Computer Science from TU Berlin (2022) and focuses on machine learning applications in collaborative multiagent systems, distributed deep learning, and explainable AI. His technical work includes open-source projects like Hanabi game-solving with reinforcement learning, DQN-based Lunar Lander automation, and speech command prediction using LSTMs. M.Sc. in Computer Science (2022, TU Berlin) Current focus areas: Probabilistic ML, distributed learning architectures, and XAI Active GitHub contributor (22 repositories, 8 followers) Notable projects: Hanabi multiagent reinforcement learning ML applications in natural sciences DQN for autonomous lunar lander environments LSTM-based speech command recognition

Marco Perez HernandezView profile

Senior Lecturer

• Multiagent Systems
• Distributed Artificial Intelligence
• Internet of Things
• +5 more

Marco Perez Hernandez is a Senior Lecturer in the School of Computing and Creative Technologies at the University of the West of England Bristol. His research focuses on distributed intelligent systems, multiagent systems, and their applications in IoT environments and digital infrastructures. Specializes in agent-based control systems and network intent control Recipient of UWE’s Vice Chancellor Early Career Research Award 2022 His work explores the dynamics of complex networks of agents, designing algorithms for collective learning and self-organization. The research emphasizes reducing platform dependencies in MAS through container-based virtualization, enabling flexible and cost-effective deployment. Scientific awards include: UWE’s Vice Chancellor Early Career Research Award 2022 Projects: GLOW-Energy (EPSRC, Co-I) LLM Agents in coding (UWE CATE, PI) NGCDI (EPSRC & BT, Researcher)

Philip BrownView profile

Associate Professor

• Game Theory
• Multi-Agent Systems
• Cyber-Physical Systems
• +9 more

Philip Brown is an Associate Professor in the Department of Computer Science at the University of Colorado at Colorado Springs. He received his PhD in Electrical and Computer Engineering from the University of California, Santa Barbara in 2018 under the supervision of Jason R. Marden. His research focuses on the intersection of technology and society, utilizing game theory, optimization, and multiagent systems to study incentive mechanisms, cybersecurity, and smart infrastructure. Current Projects: CAREER: Endogenous Information Design for smart transportation networks AFOSR YIP: Robust multiagent coordination with randomized algorithms Deriving satellite maneuver intent via game theory Past Projects: Socially Networked Autonomy: Autonomous vehicle routing COVID-19 Policy Optimization: Localized decision frameworks Value-Based Access Control: Path security mechanisms Dynamic Aviation Routing: Weather-aware path selection Research Areas: Dr. Brown's work spans game theory applications in cyber-social systems, robust network games, and strategic security modeling. He explores how financial/informational incentives shape crowd behavior in smart cities, cybersecurity vulnerabilities from mis-modeled attackers, and the paradoxes of altruism in transportation networks. Publication Trends: His recent articles focus on endogenous Bayesian games for information design, altruism dynamics in congestion games, security modeling for autonomous systems, and pandemic policy analysis. Key subfields include network robustness, incentive mechanisms, and human-machine interaction in infrastructure systems. Scientific Awards: NSF CAREER award (2025) UCCS Outstanding Teacher Award (2024) AFOSR Young Investigator Award (2022) CCDC Best PhD Thesis Award (2018) Best Paper awards at EAI GameNets, IEEE conferences Advising: Dr. Brown has advised 2 PhD graduates (Joshua Seaton & Brandon Collins) and multiple Master's/undergraduate researchers. His lab (DeSCon) investigates decision-making mathematics in infrastructure, epidemiology, and multiagent systems.

• Game Theory
• Evolutionary Game Theory
• Learning in Games
• +3 more

Alex Possajennikov serves as an Associate Professor at the School of Economics, University of Nottingham, where he conducts research and teaching in mathematical modeling of economic decision-making processes. His primary research focuses on Game Theory, specifically investigating dynamic processes of learning and evolution in multiagent decision situations. He examines how strategic interactions evolve over time through learning mechanisms and evolutionary dynamics, with direct applications to economic systems and behaviors. This work bridges theoretical game-theoretic models with practical economic phenomena, emphasizing the mathematical foundations of interactive decision-making. While specific advising relationships and grant funding details are not disclosed in the available text, his research program centers on advancing the understanding of equilibrium selection, adaptive learning algorithms in games, and evolutionary stability in economic contexts.

• Software Engineering
• Software Performance Engineering
• Geographic Information Systems (GIS)
• +4 more

Dr. Eng. Tomasz Babczyński is a researcher at Wrocław University of Science and Technology, affiliated with the Faculty of Computer Science and Telecommunications and the Department of Technical Computer Science. Software Engineering (specializing in Software Performance Engineering) Geographic Information Systems (GIS) software Image processing and pattern recognition (focusing on handwritten document analysis) His research spans interdisciplinary applications of computer science, including: Railway systems dependability analysis (ERTMS/ETCS protocols) Historical landscape modeling using GIS Multiagent system performance evaluation for power systems Advanced techniques for line segmentation in handwritten documents Recent publications (2020-2024) demonstrate expertise in tensor voting algorithms, data heterogeneity challenges, and safety-critical system analysis. His work combines theoretical computer science with practical applications in transportation, energy, and historical information systems. Contact: tomasz.babczynski@pwr.edu.pl

Ilai BistritzView profile

Assistant Professor

• Game Theory
• Distributed Control
• Multiagent Learning
• +10 more

Dr. Ilai Bistritz is an Assistant Professor at the School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering , Tel Aviv University , with a PhD in Electrical Engineering (2023) from Stanford University under Nicholas Bambos . His research bridges Game Theory , Distributed Control , and Multiagent Learning , focusing on decentralized decision-making in networked systems like autonomous vehicles , smart grids , and epidemic modeling . His work addresses challenges in Distributed Optimization where agents operate with limited communication and feedback, such as in multiplayer bandits and delayed adversarial environments . He has developed algorithms for max-min fairness , non-myopic informational cascades , and delay-robust regret minimization , achieving theoretical breakthroughs in Networked Artificial Intelligence . Scientific awards include the Best Student Paper Award at IEEE WCNC 2018 and Best Student Paper Finalist at WODES 2020 . His research emphasizes privacy-preserving protocols, scalable architectures, and applications in health monitoring , energy systems , and wireless networks .

• Computer Science
• Mathematics
• Social Sciences
• +6 more

J. M. Meylahn is an Assistant Professor at the University of Twente, affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) and the Applied Mathematics Department's Stochastic Operations Research group (EEMCS-AM-MOR). Their research focuses on multiagent learning and interdisciplinary intersections of computer science, mathematics, and social sciences. Expertise: Reinforcement Learning, Social Dilemma, Learning Algorithm, Interaction, Organisations Education: Theoretical Physics (Stellenbosch University), Mathematics PhD (Leiden University, 2019) Research interests: Multiagent systems, stochastic operations research, learning algorithms in social contexts

• Time delayed systems
• Cooperative control
• Infinite delayed systems
• +4 more

Xiang Xu is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in December 2022 as an Associate Professor in the Department of Electronic and Electrical Engineering, following a postdoctoral fellowship at City University of Hong Kong from 2018 to 2021. His educational background includes: Ph.D. in Mechanical and Biomedical Engineering from City University of Hong Kong (2014-2018) Bachelor of Engineering in Automation from Nanjing University of Science and Technology (2010-2014) Xu's research focuses on advanced control theory, particularly addressing complex challenges in time-delayed systems. His work has significantly contributed to the understanding and control of systems with infinite delays, which are notoriously difficult to analyze and stabilize. He has developed novel Lyapunov-based methods and predictor feedback techniques that have extended the theoretical foundations of control theory for systems with unbounded delays. His research bridges theoretical advancements with practical applications in multi-agent systems, traffic control, and reinforcement learning. Xu has been recognized with several prestigious awards, including: National Science Fund for Excellent Young Scholars (Overseas) in 2022 Shenzhen Peacock Plan Class C in 2022 2023 All-Wheel Drive System Theory and Application Conference Outstanding Young Paper Award City University of Hong Kong Outstanding Doctoral Dissertation Award in 2018 Shortlisted for the 2018 Guan Zhaozhi Award Xu has mentored four Ph.D. students during his postdoctoral period, all of whom have published papers in top control journals. Two of his mentees have secured assistant professor positions at other universities. His research has attracted significant funding, including two RGC grants totaling over HK$1.7 million that his supervisor successfully applied for based on Xu's work. He is actively recruiting PhD students, postdoctoral fellows, and research assistants to continue his research in time-delay systems and multi-agent control. Xu leads a research group focused on control theory at SUSTech, with ongoing projects in multi-agent systems, time-delay systems, and partial differential equations. His team collaborates with leading researchers worldwide, including IEEE Fellow Miroslav Krstic from the University of California, San Diego. The research group provides comprehensive training for students and postdocs, with strong support for publishing in top-tier journals and attending international conferences.