جست‌وجوی استاد و پژوهشگر

Philip Jordanمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Multi-agent Systems
• Optimization
• +۲ مورد دیگر

Philip Jordan is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering, Institute of Mechanical Engineering (IGM), working within the SYCAMORE laboratory. His research focuses on multi-agent systems and optimization problems. He received both his Bachelor's and Master's degrees in Computer Science from ETH Zurich, with a semester abroad at Princeton University during his undergraduate studies. His Master's thesis investigated independent learning in Markov potential games. His primary research interests include multi-agent reinforcement learning, optimization algorithms, game theory, and their applications in distributed systems. His work often addresses challenges in constrained environments and decentralized decision-making processes where multiple agents interact with shared constraints. Philip has published in top-tier conferences including AISTATS and AAMAS in 2024, with research focusing on independent learning algorithms for constrained Markov potential games and decentralized federated policy gradient methods with Byzantine fault tolerance. His publications demonstrate strong theoretical foundations coupled with practical implementations. He currently serves as a Doctoral Assistant in the SYCAMORE laboratory at EPFL, where he conducts research in systems control and multiagent optimization. His work combines theoretical analysis with practical implementation, as evidenced by the publicly available code repository for his AISTATS 2024 publication. Prior to his doctoral studies, Philip co-founded a start-up in the field of smart contracts, demonstrating practical application of his theoretical knowledge in blockchain technology.

Tuomas Sandholmمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Algorithms and Complexity
• +۱۲ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University, where he also holds appointments in the Machine Learning Department, the Ph.D. Program in Algorithms, Combinatorics, and Optimization, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He serves as Co-Director of CMU AI and Director of the Electronic Marketplaces Laboratory. Dr. Sandholm's research spans artificial intelligence, economics, and operations research, with particular focus on algorithms and complexity, game theory, machine learning, and electronic commerce. His work involves developing incentive-compatible market mechanisms and efficient algorithms for executing those mechanisms, as well as designing software agents that act optimally in electronic marketplaces. His research has led to significant commercial applications, including systems fielded by CombineNet, Inc. that handled over $60 billion in spend. Sandholm's recent publications reveal a strong emphasis on solving large-scale games, equilibrium computation, and applying these techniques to real-world problems. His work shows a progression from theoretical foundations to practical implementations, with applications ranging from poker AI (Libratus and Pluribus) to kidney exchange systems. His research group has pioneered techniques in game abstraction, regret minimization, and equilibrium computation that have pushed the boundaries of what's possible in imperfect-information games. Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) IJCAI John McCarthy Award (2021) Robert S. Engelmore Award (2021) Minsky Medal (2019) Science Breakthrough of the Year Runner-Up (2019) As an advisor, Sandholm has mentored numerous PhD students who have gone on to prestigious positions at institutions like MIT, Stanford, and CMU. His lab has secured substantial research funding for projects including solving large-scale games and heart transplantation policy optimization. The Electronic Marketplaces Laboratory he directs has been instrumental in developing algorithms that run the national kidney exchange for UNOS, resulting in approximately 10,000 life-saving transplants. Sandholm is also the founder and CEO of multiple companies including Strategy Robot, Inc., Strategic Machine, Inc., and Optimized Markets, Inc., which apply his research to defense, intelligence, business strategy, and advertising markets.

• Artificial Intelligence
• Optimization
• Game Theory
• +۷ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University (CMU), holding appointments in the Computer Science Department, Machine Learning Department, Algorithms, Combinatorics, and Optimization (ACO) Ph.D. Program, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He co-directs CMU AI and leads the Electronic Marketplaces Laboratory. His research focuses on artificial intelligence, economics, and operations research, with notable contributions to game theory, optimization, and kidney exchange systems. Sandholm has pioneered algorithms for electronic marketplaces, including the first superhuman AI in poker (Libratus and Pluribus) and kidney exchange optimization. He has founded multiple companies, including CombineNet, Strategic Machine, and Strategy Robot, and has over 500 peer-reviewed publications and 29 patents. His awards include the Vannevar Bush Faculty Fellowship, AAAI Award for AI for Humanity, and honorary doctorate from the University of Zurich. Education: Ph.D. and M.S. in Computer Science Dipl. Eng. (M.S. with B.S. included) in Industrial Engineering and Management Science Research Interests: Artificial Intelligence, Optimization, Game Theory, Kidney Exchange, Electronic Commerce, Multiagent Systems, Market Design, and Healthcare Applications. His work bridges theoretical advancements with real-world applications, particularly in strategic reasoning, organ transplantation, and defense/intelligence systems. Recent Articles: Focus on solving large-scale games, kidney exchange optimization, and AI-driven decision-making. Key areas include imperfect-information game solving (e.g., poker AIs), algorithmic fairness in healthcare markets, and combinatorial auction design. Awards & Honors: Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) Fellowships: ACM, AAAI, INFORMS, AAAS Edelman Laureate (2005) Over 100+ awards, including honorary doctorates and industry recognitions Advising & Grants: Advised PhD students now in top positions at MIT, Stanford, and CMU. Secured grants for AI game-solving and healthcare policy optimization. Current openings for PhD students in game-solving and kidney exchange policy. Labs & Teams: Electronic Marketplaces Laboratory (EML) and CMU AI Initiative. Collaborates with industry partners (e.g., Baidu, Google) and government agencies on strategic AI applications.

• Data Science
• Artificial Intelligence
• Multiagent Systems
• +۷ مورد دیگر

Prof. Stefan Selle is a Professor of Business Informatics at Saarland University of Applied Sciences since 2007. His research focuses on Data Science, Artificial Intelligence (AI), Multiagent Systems, and Deep Learning. He teaches in various programs under the Faculty of Economics. Before academia, he worked for 7 years in an IT company as a consultant, project manager, and developer, specializing in digitalization projects across insurance, tourism, publishing, and automotive sectors. Education: Studied Physics and Economics at Heidelberg University, earned a PhD in Chemistry (Reactive Flows) at Heidelberg's Interdisciplinary Center for Scientific Computing under Prof. Jürgen Warnatz. His early work on Distributed Computing (MoneyBee/BienenStock), derived from his economics diploma thesis, was nationally recognized. Research interests include AI applications, reinforcement learning, and neural networks. He has led projects like Gaia (lecture planning platform), Keto (e-examination system), and the award-winning 'AI in the Mirror of Communication and Culture' (2019). Awards: 2021 Teaching Award, 1999 Federal Technology Prize Projects: Digital platforms for academic administration, AI ethics in culture, and Industry 4.0 applications Advising: Supervised over 100 theses in AI, data science, and business informatics He is Head of the Business Informatics Focus Area (Bachelor in Business Administration) and serves on university committees including the Franco-German Higher Education Institute (DFHI).

• Applied Mathematics
• Differential Equations
• Impulsive Systems
• +۷ مورد دیگر

Jianhua Shen is a Professor at Nanjing University of Posts and Telecommunications (NJUPT), School of Computer Science and Engineering, with a distinguished research career spanning over 30 years. His academic journey shows a natural progression from theoretical mathematics to applied computer science and educational technology. His research interests encompass Applied Mathematics , Differential Equations , Impulsive Systems , Stability Analysis , Computer Networks , Wireless Communication , Educational Technology , and Machine Learning . Shen's work demonstrates remarkable adaptability while maintaining mathematical rigor, evolving from foundational theoretical work to practical applications in modern computing and education. Shen's recent publications (2020-2025) reveal a strong focus on educational technology and federated learning , with significant contributions to virtual simulation experiments, MOOC platforms, and personalized learning systems. His work bridges theoretical computer science with practical educational applications, reflecting NJUPT's emphasis on technology-enhanced education. As an advisor, Shen has mentored numerous students including Meijuan Chen, Cong Yu, and Xiaoling Liu, who frequently appear as co-authors on his publications. His collaborative network extends across multiple departments at NJUPT and includes researchers from various Chinese institutions.

Chun Wangمشاهده پروفایل

استاد

• Sustainable Mobility Systems
• Eco-friendly Transportation Networks
• Computational Social Simulation
• +۴ مورد دیگر

Dr. Chun Wang is a Professor and Director of the Concordia Institute for Information Systems Engineering at Concordia University. His research focuses on Sustainable, Smart, and Socially-Oriented Mobility Systems, emphasizing eco-friendly solutions that enhance community well-being and reduce environmental impact. His work integrates computational social simulation, game theory, and multiagent systems to address challenges in electrification, decarbonization, and supply chain optimization. Key research areas include mobility systems design, scheduling algorithms, two-sided matching mechanisms, and e-supply chain management. His interdisciplinary team develops frameworks for ride-hailing systems, crowd-sourced delivery, and electric vehicle charging networks, with an emphasis on privacy preservation and uncertainty management. Recent publications (2021–2023) highlight advancements in auction-based scheduling, data-driven optimization for car-sharing, and privacy-preserving matching mechanisms. The lab’s work bridges theoretical models with practical applications in smart cities and sustainable infrastructure. Dr. Wang leads the Sustainable, Smart, and Socially-Oriented Mobility Systems Lab, fostering collaborations across engineering, computer science, and urban planning disciplines. His contributions span transportation networks, energy systems, and healthcare operations, with a focus on scalable and socially responsible solutions.

• Federated Learning
• Blockchain Technology
• Internet of Vehicles
• +۷ مورد دیگر

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.

Nelson Rodriguesمشاهده پروفایل

پژوهشگر

• Intelligent Manufacturing
• Reconfigurable Manufacturing Control Systems
• Artificial Intelligence
• +۵ مورد دیگر

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.

Adam Eckمشاهده پروفایل

دانشیار

• Multiagent Systems
• Machine Learning
• Computational Social Science
• +۳ مورد دیگر

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 Lamمشاهده پروفایل

پژوهشگر ارشد

• Combinatorial Optimization
• Neuro-Symbolic AI
• Operations Research
• +۳ مورد دیگر

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).

• Game Theory
• Reinforcement Learning
• Multiagent Systems
• +۳ مورد دیگر

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.

Kate Larsonمشاهده پروفایل

استاد

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +۳ مورد دیگر

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.

• Artificial Intelligence
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Zahra Babaiee is a PreDoc Researcher at the Department of Cyber-Physical Systems at Vienna University of Technology, actively contributing to interdisciplinary research at the intersection of artificial intelligence, neuroscience, and medical imaging. She is involved in projects such as MATTO-GBM (2024–2027), funded by the Austrian Science Fund (FWF), and Efficient Visual Intelligence for Autonomous Driving and Mobility (2025–2026), supported by the Austrian Research Promotion Agency (FFG). Research Focus : Her work spans Artificial intelligence and machine learning Neuroscience-inspired neural network architectures Medical imaging and radiation treatment optimization Autonomous driving and mobility analytics Depthwise convolutional filters and biological receptive fields Robust image classification techniques Collaborative Projects : Key collaborations include publications in leading conferences like AAMAS, ICLR, and WACV, with applications in tourism flow prediction, prostate segmentation, and autonomous systems. Her research demonstrates a strong emphasis on scalable AI solutions and multimodal data analysis. Laboratory Affiliation : She works within the Cyber-Physical Systems research group at Vienna University of Technology, focusing on practical implementations of AI in real-world scenarios.

Michael Richardsonمشاهده پروفایل

استاد

• Cognitive Science
• Human Factors
• Complex Systems
• +۸ مورد دیگر

Michael Richardson is a Professor in the School of Psychological Sciences at Macquarie University and Director of the Centre for Elite Performance Expertise and Training (CEPET). His research focuses on modeling human perception, action, and cognition for developing human-machine systems, with expertise in cognitive science, AI, and dynamical systems analysis. Education & Roles: Former Associate Professor at University of Cincinnati (2009-2017) Former Assistant Professor at Colby College (2006-2009) Research Interests: His work integrates cognitive science, complex systems theory, and AI to model social interaction, motor control, and multiagent coordination. Key areas include: Human-machine interaction dynamics Perception-action coupling Embodied cognition Nonlinear time-series analysis Awards: 4x Department of Psychology Graduate Instructor/Advisor of the Year (2012-2015) Psi Chi Undergraduate Instructor of the Year (2012) Advising & Grants: Supervised 12 PhD and 14 MSc students in last 5 years. Current projects include: Funding for human-machine coordination research (2022-2024) VR-based social interaction studies Labs & Collaborations: Directs CEPET and collaborates with Frontier AI Research Centre. Active in developing training programs for multiagent systems and social robotics.

• Sustainable System Engineering
• Environmental Engineering
• Sustainability Science
• +۱۶ مورد دیگر

Ni-Bin Chang is a Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida, College of Engineering and Computer Science. He has been a leading figure in sustainable systems engineering since 2002, with extensive contributions to environmental engineering, remote sensing, and the urban food-energy-water nexus. At UCF, he directs the Stormwater Management Academy and leads interdisciplinary research with significant societal and industrial impact. His research interests lie at the intersection of sustainability science and engineering, focusing on: Urban food-energy-water-waste nexus Water quality and nutrient removal Green sorption media and sustainable materials Remote sensing and spatial informatics Machine learning in environmental systems Resilient urban infrastructure The recent publications highlight a strong trend toward integrating artificial intelligence, remote sensing, and systems modeling to tackle urban sustainability challenges. His work increasingly combines machine learning with agent-based modeling and tensor decomposition to optimize environmental solutions, particularly in stormwater and air quality management. There is a clear emphasis on circular economy principles, resilience, and transdisciplinary innovation. His scientific honors include: Blaise Pascal Medal from the European Academy of Sciences Fulbright Canada Research Chair Award Outstanding Achievement Award from ASCE Induction as Fellow of the National Academy of Inventors Fellowships in ASCE, IEEE, SPIE, AAAS, RSC, and others Chang has secured over $18.66 million in research funding and has been instrumental in translating research into practice—his 15 patented green sorption media technologies have been licensed to industry, attracting $20 million in investment. He serves as Editor-in-Chief of the Journal of Applied Remote Sensing and the Journal of Hydroinformatics, shaping scholarly discourse in his fields. He mentors students and leads a dynamic research team at the Stormwater Management Academy, where over 70 field applications have been developed for nutrient removal and recovery. He also leads a research laboratory focused on sustainable infrastructure, with test beds for green filtration systems, urban metabolism modeling, and AI-driven decision support tools. His team integrates field experiments with computational modeling to develop scalable, cost-effective, and environmentally benign solutions for cities worldwide.