جست‌وجوی استاد و پژوهشگر

Joel Dyerمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Agent-based Modelling
• +۳ مورد دیگر

Joel Dyer is a Senior Research Fellow at the Oxford Institute for New Economic Thinking and a Senior Research Associate at the University of Oxford's Department of Computer Science. He holds a DPhil in computational statistics and machine learning from the University of Oxford’s Mathematical Institute. His research focuses on agent-based simulation models, likelihood-free parameter inference, and simulation-based planning under uncertainty. Affiliations: University of Oxford (Department of Computer Science), Oxford Institute for New Economic Thinking Education: DPhil in Computational Statistics and Machine Learning, Mathematical Institute, University of Oxford Research Interests: Agent-based modelling Bayesian inference and likelihood-free methods Monte Carlo simulation Simulation-based optimization and planning Research Contributions: His work bridges computational statistics and economic modeling, emphasizing scalable inference techniques for complex systems. Recent efforts include surrogate modeling for simulation optimization and path signatures for time-series analysis. Labs/Teams: Part of the Complexity Economics Programme at Oxford INET and collaborates with institutions like the Alan Turing Institute and Improbable.

Haibo Heمشاهده پروفایل

استاد

• Computational Intelligence
• Neural Networks
• Reinforcement Learning
• +۵ مورد دیگر

Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.

Michael Winikoffمشاهده پروفایل

استاد

• Autonomous Agents and Multiagent Systems
• Explainable AI (XAI)
• Agent-Oriented Software Engineering
• +۵ مورد دیگر

Professor Michael Winikoff is a prominent academic at Victoria University of Wellington's School of Information Management, where he joined in June 2019 and served as Head of School from early 2021 to early 2025. Previously, he was Head of Department at University of Otago's Department of Information Science (February 2011 to December 2016) and Associate Professor at RMIT University's School of Computer Science and IT. His career spans over two decades of research in autonomous systems and agent-oriented software engineering. His educational background includes a PhD from the University of Melbourne (1994-1997). His research focuses on improving software creation methodologies, particularly for intelligent agents that exhibit robust and flexible behavior. He is best known for developing the Prometheus methodology for agent-based systems design. More recently, his work has expanded to address societal consequences of autonomous systems and trust issues, with significant contributions to explainable AI (XAI). His publication record demonstrates consistent contributions to autonomous systems research, with recent work focusing on explainability frameworks, trust calibration, and certification of reliable autonomous systems. His 15 most recent publications (2018-2025) show a clear evolution from technical agent design toward broader societal implications, particularly the intersection of technical design and human trust in autonomous systems. The publications span journals like Artificial Intelligence and IEEE Internet Computing, as well as major conferences including AAMAS. Past President of International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) Co-Editor-in-Chief of Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS) Chair of ICORE rankings management committee Editor-in-Chief for International Journal of Agent-Oriented Software Engineering (IJAOSE) Vice President of Computing Research and Education Association of Australasia (CORE) Professor Winikoff has led significant research projects funded by the Australian Research Council, including work on adaptive personae for interactive toys, service-oriented negotiation in multi-agent systems, and advanced software engineering for intelligent agent systems. His current research continues this trajectory, focusing on making autonomous systems more explainable and trustworthy through engineering approaches that consider both technical and human factors. He maintains active collaborations across institutions, with recent work involving researchers from RMIT University and other international partners.

Nirav Ajmeriمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Multiagent Systems
• Intelligent Autonomous Systems
• +۷ مورد دیگر

Dr. Nirav Ajmeri is a Senior Lecturer in Artificial Intelligence at the University of Bristol's School of Computer Science. He holds a PhD and MS from North Carolina State University and a BE from Sardar Vallabhbhai Patel Institute of Technology. His work focuses on socially intelligent multiagent systems, ethics in AI, privacy-preserving technologies, and socio-technical systems design. Education: PhD and MS in Computer Science, North Carolina State University B.E. in Computer Engineering, Sardar Vallabhbhai Patel Institute of Technology Research Interests: Dr. Ajmeri explores ethical AI frameworks, normative multiagent systems, privacy in socio-technical environments, and human-agent collaboration. His work bridges technical innovation with societal impacts, focusing on fairness, accountability, and transparency in autonomous systems. Recent Trends in Publications: Recent work emphasizes ethical governance in AI (Rawlsian fairness, macro ethics), graph-based social network modeling, and multiagent simulations of polarization. He also investigates practical applications like misuse audits in mobile apps and cybersecurity hygiene promotion through normative systems. Awards: Best Blue Sky Paper Award at AAMAS 2020 Most Influential Paper Award (2024) for 2013 JSS publication on agile requirements Advising & Grants: Supervises PhD students in AI ethics, interactive AI, and cybersecurity. Co-developed the UKRI AI for Collective Intelligence Hub and contributed to national AI strategy frameworks. Engaged in tool development (e.g., Coco ASP implementation for norm reasoning). Labs & Teams: Active in Bristol's Interactive AI research group, leading projects on ethical AI design and multiagent system dynamics. Collaborates with interdisciplinary teams across computer science, social sciences, and policy domains.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

Qi Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +۴ مورد دیگر

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.

Alan Tsangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Multi-agent Systems
• Game Theory
• +۷ مورد دیگر

Alan Tsang is an Assistant Professor at Carleton University's School of Computer Science. He specializes in Multi-agent Systems and Computational Social Choice, focusing on strategic interactions in social networks. Previously, he was a Post-Doctoral Researcher at the National University of Singapore and earned his PhD from the Cheriton School of Computer Science at the University of Waterloo under Dr. Kate Larson. His research integrates game theory and agent-based simulations to address fairness, voting mechanisms, and social network dynamics. He is affiliated with Carleton's Institute for Data Science and the Department of Human-Computer Interaction (HCI), and serves as Information Officer of ACM SIGAI. His educational background includes a Master of Mathematics in Graph Theory and a Bachelor of Mathematics in Bioinformatics, both from the University of Waterloo. His work explores interdisciplinary applications, such as modeling pandemic impacts and ethical computing challenges. He teaches courses like Multiagent Systems and Computing, Society, and Ethics, and actively mentors students in graduate and undergraduate research programs. Key research areas include algorithmic fairness, social choice mechanisms, and agent-based modeling of societal systems. He leads the Games, Agents, and Incentives Workshop and collaborates on projects analyzing voting behavior in homophilic networks and fair allocation strategies.

• Computational Social Science
• Social Computing
• Information Systems
• +۱۲ مورد دیگر

Bjorn Ross is a Lecturer in Computational Social Science at the School of Informatics, University of Edinburgh. He is affiliated with the Institute for Language, Cognition and Computation and actively contributes to the Computational Social Science research area. He serves as Director of the SMASH research group and is part of the management team for the Centre for Doctoral Training in Natural Language Processing (CDT in NLP). Education: PhD, 2019, University of Duisburg-Essen MSc in Computer Science, 2016, University of Münster Exchange year, 2014–2015, University of Strasbourg BSc in Information Systems, 2013, University of Münster His research focuses on computational methods for analyzing social media, particularly using natural language processing, social network analysis, and agent-based modeling. Key interests include misinformation, hate speech, social bots, and the ethical implications of automated systems. He emphasizes fairness, bias, and data ethics in AI applications. His recent publications span topics such as queerphobic bias in sentiment analysis, explainable hate speech detection, temporal generalizability in misinformation models, and cross-lingual bias amplification. These works appear in leading venues including ACL, ICWSM, Social Science Computer Review, and Big Data & Society, reflecting a strong interdisciplinary focus bridging computer science, social science, and ethics. Scientific Awards: Best Paper Award, Electronic Government track, HICSS 2018 Ross supervises multiple PhD students and welcomes new applicants. He has also contributed to software tools and web-based platforms for argument analysis and counterspeech. He is an Associate Editor for Business & Information Systems Engineering and a member of AIS, ACL, and ACM. His teaching includes courses on Text Technologies for Data Science and digital persuasion. He leads the SMASH group, which focuses on social media analytics and societal challenges, and collaborates extensively within the CDT in NLP and the Institute for Language, Cognition and Computation.

Prasad Tadepalliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

• Logic in Computer Science
• Coalgebras
• Modal Logics
• +۷ مورد دیگر

Corina Cirstea is a Professor in the School of Electronics and Computer Science at the University of Southampton, where she has been a faculty member since 2003. She holds a DPhil in Computation from the University of Oxford and previously served as a Junior Research Fellow at St. John's College, Oxford. She is the Programme Leader for the BEng/MEng in Software Engineering and teaches core modules including Algorithmics, Theory of Computing, and Automated Software Verification. Education: DPhil in Computation, University of Oxford (2000) Junior Research Fellowship, St. John's College, Oxford (1999–2003) Her research focuses on logic and models of computation, particularly coalgebras and their applications in automated verification and synthesis. She explores coalgebraic temporal logics, trace semantics, and formal modeling of real-time and resource-aware systems, primarily using Event-B. Her work bridges theoretical foundations with practical system verification, especially in safety-critical domains. The analysis of her recent publications reveals a strong trend in formal methods, with consistent contributions to real-time system modeling, refinement techniques, and coalgebraic semantics. Her work integrates theoretical depth with engineering applications, particularly in automated verification and system design. Scientific Service and Recognition: Member of the editorial board, Compositionality journal Member of IFIP Working Group 1.3 Member of CALCO Steering Committee Member of CMCS Steering Committee Program Committee member for SEFM 2021, MFPS 2021, HIGHLIGHTS 2021, ICALP 2021, MFCS 2021, ACT 2020, ICE 2020, CMCS 2020, FOSSACS 2020 She advises PhD students including Chenyang Zhu and Eman Alkhammash, and has been involved in multiple research grants focused on formal verification, real-time systems, and software engineering. She collaborates extensively with researchers such as Michael Butler and Ichiro Hasuo. Her work contributes to both foundational theory and practical tooling in formal methods. She is associated with research groups and initiatives in formal methods and theoretical computer science at the University of Southampton, particularly within the broader context of software engineering and system verification.

Maria Birna van Riemsdijkمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Agent Systems
• Human-AI Interaction
• +۷ مورد دیگر

Maria Birna van Riemsdijk is an Associate Professor in Intimate Computing at the University of Twente, Netherlands. Her research focuses on developing technologies that address human vulnerability in daily life through intimate computing , blending ethics with AI and robotics. She leads the Human-Media Interaction group and contributes to the Digital Society Institute. Awards: Vidi personal grant Dutch Prize for Research in ICT 2014 2020 IEEE Computer best paper award Leadership Roles: Editorial board member, Journal of Autonomous Agents and Multiagent Systems Board member, 4TU.NIRICT (ICT research consortium) Former board member, IFAAMAS (2012-2018) Her research bridges Artificial Intelligence , Human-AI Interaction , and Healthcare , emphasizing responsible design. Recent work explores conversational agents for behavior support, ethical frameworks, and AI in healthcare contexts. Key article themes include responsible computing , conversational strategies , and adaptable AI frameworks . She has contributed to 26 total research outputs and datasets in human-agent alignment and social situation modeling.

Michael Wooldridgeمشاهده پروفایل

استاد

• Artificial Intelligence
• Multiagent Systems
• Foundations of AI
• +۲ مورد دیگر

Michael Wooldridge holds the Ashall Professorship in the Foundations of Artificial Intelligence at the University of Oxford, where he is recognized as a world leader and founding figure in multiagent systems research. His scholarly focus centers on the theoretical underpinnings of artificial intelligence, particularly interactions between autonomous AI systems. This work has established critical frameworks for distributed intelligence and agent-based computing, influencing both academic research and industrial applications in AI coordination and negotiation protocols. Professor Wooldridge's contributions have been honored with: Fellowship of the Royal Academy of Engineering (FREng, 2025) Beyond academia, he actively shapes AI policy through governmental advisory roles, including specialist consultation for the UK House of Lords inquiry on large language models and the Scientific Advisory Group for Emergencies. His public engagement culminated in the 2023 Royal Institution Christmas Lectures series titled “The Truth about AI”, making complex AI concepts accessible to broad audiences.

Jae Hee Leeمشاهده پروفایل

پژوهشگر ارشد

• Multimodal Language Models
• Explainable AI
• Neuro-Symbolic AI
• +۴ مورد دیگر

Dr. Jae Hee Lee is a postdoctoral researcher in the Knowledge Technology Group at the University of Hamburg, holding a PhD in Computer Science from the University of Bremen. His research focuses on multimodal language models, explainable AI, and neuro-symbolic integration to enhance model robustness and generalization. Previously, he specialized in spatio-temporal reasoning and multiagent systems, supported by grants like the Feodor Lynen Fellowship (2016–2017). He is an associate editor of AI Communications and co-organizes the International Workshop on Spatio-Temporal Reasoning and Learning. His recent work includes projects like LUMO (DFG-funded, 2025–2029), exploring lifelong multimodal learning through compositional knowledge. Lee advises multiple students, including Björn Plüster, developer of the LeoLM German LLM. Key research interests span explainable vision-language models, causal reinforcement learning, and concept-based explanations. He frequently serves on program committees for AI/NLP conferences (e.g., IJCAI, COLING) and organizes reading groups on LLM/XAI topics.

Jörg Rotheمشاهده پروفایل

استاد

• Computational Complexity
• Computational Social Choice
• Algorithmic Game Theory
• +۵ مورد دیگر

Prof. Jörg Rothe is a faculty member at the Heinrich-Heine-Universität Düsseldorf, affiliated with the Institut für Informatik (Department of Computer Science) under the Mathematisch-Naturwissenschaftliche Fakultät (Faculty of Mathematics and Natural Sciences). His research focuses on computational complexity, computational social choice, algorithmic game theory, fair division, and cryptology. He has authored or co-authored numerous books and publications in top-tier journals and conferences. Education details not explicitly mentioned in provided texts. Key research areas include voting theory, bribery complexity, coalition formation, and argumentation systems. Rothe's work explores topics like the complexity of control in elections, fair allocation of resources, and stability in hedonic games. His recent publications address challenges in multiagent systems and computational social choice, such as fair division mechanisms and algorithmic solutions for strategic behavior. Despite extensive research contributions, no scientific awards or grants were explicitly listed in the provided materials. His group's work often intersects theoretical computer science with practical applications in AI and decision-making systems.

Bingzhe Liمشاهده پروفایل

استادیار

• DNA Storage Systems
• Machine Learning Systems
• Low-Power Computing
• +۳ مورد دیگر

Dr. Bingzhe Li is an Assistant Professor in Computer Science at UT Dallas' Erik Jonsson School of Engineering. His research at the Lab for Intelligent Storage and Computing (Lab4ISC) focuses on DNA storage systems, machine learning infrastructure, and energy-efficient computing architectures. Awarded the NSF CAREER Award (2025) and recognized for Best Paper nominations at leading conferences. Research spans DNA storage capacity optimization, reinforcement learning for hybrid SSDs, Kubernetes storage optimization, and stochastic computing architectures. Recent publications demonstrate innovations in out-of-core graph processing and blockchain storage systems. Leads multiple NSF/NASA-funded projects on DNA storage and convertible SSDs. Teaches Digital Logic and Computer Architecture courses. Supervises 8 PhD students and 3 master's candidates in storage systems and low-power computing research.