جست‌وجوی استاد و پژوهشگر

Piotr Krystaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithmic Mechanism Design
• Algorithmic Game Theory
• +۸ مورد دیگر

Piotr Krysta is a Professor in the School of Computer and Cyber Sciences at Augusta University, GA, USA, and a part-time Professor in the Department of Computer Science at the University of Liverpool, UK. He is affiliated with the Materials Innovation Factory and the Doctoral Training Centre in Distributed Algorithms. His research focuses on theoretical computer science, algorithmic mechanism design, algorithmic game theory, optimization, and their applications in computational economics, blockchains, computational chemistry, AI, and machine learning. Education: Ph.D. from the University of Saarland (2001), Habilitation from the University of Dortmund (2006). Prior roles include postdoctoral positions at Max Planck Institute for Informatics and University of Waterloo. Research interests include approximation algorithms, distributed algorithms, stochastic optimization, and adversarial equilibria. Notable works include crystal structure prediction algorithms and MEV blockchain mechanisms. Recognitions include the Emmy Noether Fellowship (2004–2008), ICALP 2012 Best Paper Award, and AAMAS 2013 Best Paper Award. Advising: Supervised 7 Ph.D. students, including Antonia Tsili and Emmanouil Bakopoulos. Secured £1.2 million in research grants, notably on mechanism design and algorithmic game theory. Labs/Teams: Active in the Materials Innovation Factory and collaborations with chemists and computer scientists for computational chemistry projects.

Giovanni Paoliniمشاهده پروفایل

دانشیار

• Combinatorics
• Topology
• Group Theory
• +۳ مورد دیگر

Giovanni Paolini is an Associate Professor of Mathematics at the University of Bologna (Italy) since 2023. Previously, he worked as an Applied Scientist/Senior Applied Scientist at Amazon Web Services and Caltech (2019–2023), and held a postdoctoral position at the University of Fribourg (2019). He earned his PhD in Mathematics from Scuola Normale Superiore (Pisa, Italy) in 2019, under Prof. Mario Salvetti, with a thesis on topology and combinatorics of affine reflection arrangements. His research spans combinatorics, topology, group theory , and machine learning (deep learning and NLP). Notable contributions include resolving the K(π,1) conjecture for affine Artin groups and developing AI for strategic games like 7 Wonders Duel. He organizes events such as MATH-MIND: Mathematics-Industry Networking Days (2025) and has participated in workshops like Artin Groups and Arrangements at MSRI (2024). Teaching roles include courses on geometry, combinatorial topology, and machine learning at the University of Bologna. His work bridges pure mathematics and applied AI, with publications in journals like Inventiones Mathematicae and conferences like ICLR and ICML. Collaborations include projects with Stefano Soatto (UCLA), Mario Salvetti, and interdisciplinary teams at Amazon.

Shiri Dori-Hacohenمشاهده پروفایل

استادیار

• Information Ecosystem Threats
• AI Safety
• Sociotechnical AI Alignment
• +۵ مورد دیگر

Shiri Dori-Hacohen is an Assistant Professor of Computer Science & Engineering at the University of Connecticut, directing the Reducing Information Ecosystem Threats (RIET) Lab. Her work focuses on AI safety, sociotechnical AI alignment, and mitigating information ecosystem threats through transdisciplinary collaboration. She has secured $7.7M in NSF funding and received awards including the 2023 D-30 Disability Impact List for groundbreaking fairness research. Education: B.Sc. and M.Sc. (cum laude) in Computer Science, University of Haifa, Israel M.S. and Ph.D. in Computer Science, University of Massachusetts Amherst Research Interests: Combating misinformation, controversy detection, AI ethics, and health equity. Her work bridges computer science with medicine, public health, and social sciences. Current projects address disinformation, botnet detection, and fairness in medical AI systems. Awards and Recognition: 2023 D-30 Disability Impact List 2022 AI Risk Analysis Award (NeurIPS) 2016 UMass Amherst Innovation Challenge First Place 2011 Google Lime Scholarship Leadership & Advocacy: Co-founder of Graduate Women in STEM @ UMass, Women in Information Retrieval, and Disabled In Computing. Active media commentator featured in Reuters, The Guardian, Forbes, and Galei Tzahal radio. Labs & Initiatives: PI of RIET Lab which develops computational tools to detect threats in information ecosystems. Research incorporates petabyte-scale social network data and graph-theoretic approaches. Current focus on medical fairness and misinformation mitigation in marginalized communities.

Juan Zhangمشاهده پروفایل

مدرس

• Mobile Edge Computing
• Intelligent Transportation Systems
• Game Theory
• +۷ مورد دیگر

Dr. Juan Zhang is a Lecturer at the Department of Computer and Information Sciences, Northumbria University, UK. She holds a PhD in Computer Science from the University of Exeter and has held postdoctoral positions at Helmut-Schmidt University, Germany, and visiting roles at Georg-August-University of Göttingen. Her research focuses on mobile edge computing, intelligent transportation systems, decision-making strategies, and autonomous vehicles. She has published extensively in top journals like IEEE Transactions and IEEE Conference Proceedings, with over 225 citations. Dr. Zhang teaches modules including Computer Networks, Cyber Security, and supervises numerous PhD/MSc students in edge computing and vehicular networks. Education: PhD in Computer Science, University of Exeter (UK) M.Sc. in Control Science & Engineering B.Eng in Mechanical Engineering, Beihang University (China) Research Interests: Her work integrates AI with transportation systems, emphasizing eco-driving controllers, vehicular platooning, and multi-agent systems for HD mapping. She explores game-theoretic approaches for edge computing optimizations and reinforcement learning applications in autonomous systems. Teaching: Current modules: KF7023, KV6017, KF7029, etc. Supervised over 20+ student projects on edge computing, federated learning, and vehicular networks. Labs/Teams: Active in Northumbria's Computer Networks Group and collaborates internationally on vehicular edge computing and smart logistics systems.

Yezekael HAYELمشاهده پروفایل

پژوهشگر

• Complex Systems Optimization
• Network Science
• Applied Mathematics
• +۶ مورد دیگر

Yezekael HAYEL is a Researcher at the Laboratoire d'Informatique Avignon (LIA) and Director of the Agrosciences & Sciences Doctoral School at Avignon Université. His research focuses on optimizing complex systems with network structures, bridging computer science, applied mathematics, and social sciences. Key areas include opinion dynamics in social networks, cybersecurity, and infrastructure optimization for electric vehicles. He co-authored several influential articles on crowdfunding dynamics, epidemic control, and smart charging algorithms. As Doctoral School Director, he oversees interdisciplinary programs spanning biology, STAPS, computer science, and agricultural sciences. His leadership in the IMPLANTEUS university research school highlights his commitment to multidisciplinary collaboration. Recent work includes a CNRS-published article on opinion evolution models and a collaboration with the Nancy Automation Research Center. Education: Multidisciplinary background in mathematics and computer science Key Projects: ANR-funded IMPLANTEUS research school Optimization of EV charging ecosystems Cybersecurity strategies against network epidemics His pedagogical contributions include teaching at New York University and advocating for international academic exchange. He emphasizes perseverance and creativity in research, advising students to embrace curiosity amid challenges. Awards: None explicitly mentioned, but his leadership roles reflect institutional recognition. Labs/Teams: Active in Avignon Université's Metaboscope Platform and DARI department, collaborating with industry partners like the Nancy Automation Research Center.

• Stochastic Optimization
• Chance-Constrained Optimization
• Distributionally Robust Optimization
• +۶ مورد دیگر

Abdel Lisser is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on stochastic optimization, distributionally robust optimization, and game theory with applications in control systems, networks, and autonomous systems. He has made significant contributions to chance-constrained programming, Markov decision processes, and neurodynamic optimization. His work bridges mathematical foundations with practical applications in energy management, telecommunications, and machine learning. Notable recent research includes the development of physics-informed neural networks for solving nonlinear optimization problems and stochastic trajectory planning for autonomous vehicles. Lisser collaborates internationally, with publications in top journals such as *Journal of Optimization Theory and Applications* and *IEEE Transactions on Neural Networks and Learning Systems*. He advises on projects involving robust control and decision-making under uncertainty. His laboratory, L2S, is a leading center for systems analysis and optimization at Paris-Saclay.

• Artificial Intelligence
• Procedural Content Generation
• Game Design
• +۵ مورد دیگر

Amy Hoover is an Assistant Professor in the Informatics department at New Jersey Institute of Technology (NJIT). Her research focuses on artificial intelligence, procedural content generation, and the intersection of AI with creative domains such as music composition and video game design. She explores topics like evolutionary algorithms, open-ended learning systems, and ethical considerations in AI applications. Her work spans contributions to procedural content generation (PCG) in games, including automated deckbuilding for Hearthstone and generative level design. She also investigates peer learning dynamics in AI systems and the application of large language models (LLMs) for creative tasks. Hoover’s research often bridges computational methods with human-centric design, such as curriculum development in AI education and fostering creativity through interactive tools. Her publications reflect collaborations in AI-driven game design, music composition, and educational technology. Media coverage highlights her work on ethical risks in mixed-reality gaming and AI’s role in mental health support through gaming communities. Notable contributions include frameworks like Watts for open-ended learning, ensemble learning methodologies inspired by human collaboration, and studies on transfer dynamics in evolutionary curricula. Her work emphasizes practical applications of AI in both technical and creative fields.

Bernd Finkbeinerمشاهده پروفایل

استاد

• Formal Verification
• Reactive Synthesis
• Hyperproperties
• +۴ مورد دیگر

Prof. Bernd Finkbeiner is a faculty member at CISPA Helmholtz Center for Information Security and holds a Professorship in Computer Science at Saarland University. He earned his Ph.D. in 2003 from Stanford University. Leading the Reactive Systems Group since 2003, now part of CISPA, his research focuses on ensuring safety and security in computer systems through formal methods like specification, program synthesis, and verification. Key projects include output-sensitive reactive synthesis (OSARES), hyperproperty logics (HYPER), and real-time monitoring (RTLOLA). Education : Ph.D. in Computer Science, Stanford University, 2003 His research interests span hyperproperties, formal verification, runtime monitoring of cyber-physical systems, and distributed synthesis. He has pioneered tools like StreamLAB and AutoHyper for hyperproperty analysis. His work on temporal causality and information-flow guided synthesis addresses challenges in distributed and secure systems. Key Achievements : Recipient of ERC Advanced Grant 2022–2027 for Project HYPER Best Paper Awards at ICALP 2009, FSEN 2007, and VMCAI 2012 Leader of the Reactive Systems Group at CISPA Grants & Funding : ERC Advanced Grant supporting research on hyperproperties His lab develops cutting-edge tools for formal methods, including BoSy for bounded synthesis and RTLola for runtime verification. Current research explores compositional synthesis, explainable reactive systems, and robust monitoring for medical and autonomous systems.

Stefanos Leonardosمشاهده پروفایل

مدرس

• Blockchain Technology
• Cryptocurrency Economics
• Game Theory
• +۳ مورد دیگر

Stefanos Leonardos is a Lecturer in Machine Learning at the Informatics department. His research focuses on blockchain technology, cryptocurrency economics, game theory, and distributed systems. He has contributed to understanding mechanisms in Ethereum fee markets, strategic bidding behaviors in blockchain auctions, and the dynamics of coordination games. Leonardos has been awarded multiple Best Paper Awards (2019, 2020) and an Outstanding Paper Award (2021), highlighting his impactful work in interdisciplinary fields. His collaborations span academic and industry partners, exploring applications such as decentralized oracle networks (CountChain) and blockchain-based advertising platforms (AdChain). Key research directions include analyzing MEV (Maximal Extractable Value) mechanisms, modeling market equilibria in decentralized systems, and advancing multi-agent learning frameworks. Leonardos' work often bridges theoretical insights with practical applications, addressing challenges in blockchain governance, transactional data analysis, and incentive alignment.

Ekram Hossainمشاهده پروفایل

استاد

• Wireless Communications
• 6G and Beyond Cellular Networks
• AI/Machine Learning
• +۵ مورد دیگر

Dr. Ekram Hossain is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba, serving as Associate Chair (Graduate Studies). He is a Fellow of the Canadian Academy of Engineering and the Engineering Institute of Canada, and an IEEE Fellow (2015). His research focuses on wireless communication networks, AI-driven 6G systems, and resource management. He leads the WiCoNS research group and holds editorial roles in top journals like IEEE Transactions on Mobile Computing. Education: Ph.D. in Electrical Engineering (University of Victoria, 2001) Professional Roles: IEEE Distinguished Lecturer (2012-2020), Board Member of IEEE Communications Society Research Interests span 6G networks, reconfigurable intelligent surfaces, non-orthogonal multiple access, and green radio. Notable contributions include over 20 books and numerous award-winning papers. Awards include the 2024 IEEE Education Award and Clarivate Highly Cited Researcher recognition (2017-2024). Awards & Recognition: Multiple IEEE awards, Royal Society of Canada membership, and leadership in professional organizations like IEEE Canada. Advising & Grants: Supervised over 30 graduate students. Secured funding from NSERC and industry partners for projects on heterogeneous networks and 5G/6G technologies. Active in global initiatives like the Next G Alliance Research Council. Labs/Teams: WiCoNS group focuses on AI-driven wireless systems, edge computing, and network virtualization.

• Multi-agent systems
• Reinforcement learning
• Trust management
• +۵ مورد دیگر

Athirai Aravazhi Irissappane is an Assistant Professor at Nanyang Technological University's College of Computing and Data Science, School of Computer Science and Engineering. With a PhD in Trust oriented decision making via POMDPs completed in 2016, they have established themselves as a prominent researcher in multi-agent systems and reinforcement learning. Their academic journey shows a clear progression from foundational work in trust management to cutting-edge research in deep reinforcement learning frameworks. Dr. Irissappane's research interests focus on multi-agent systems , reinforcement learning , trust management , and recommender systems . They have made significant contributions to the field of multi-objective reinforcement learning, developing frameworks that address complex policy distributions and cooperative behavior in multi-agent environments. Their work bridges theoretical advances with practical applications, particularly evident in their contributions to the RecSys Challenge 2023. Analysis of their recent publications (2020-2024) reveals a strong emphasis on Advanced reinforcement learning architectures for multi-agent cooperation Privacy-preserving recommendation systems Automated data quality improvement for recommendation tasks Scalable frameworks for reinforcement learning research These works demonstrate increasing sophistication in handling complex decision-making scenarios while addressing practical constraints like computational efficiency and privacy concerns. Dr. Irissappane has been actively involved in major academic initiatives including the RecSys Challenge 2023 and has contributed to comprehensive guides on multi-objective reinforcement learning that have become valuable resources for the research community. Their collaborative work spans multiple institutions and has resulted in publications in top-tier venues including IEEE Transactions, Autonomous Agents and Multi-Agent Systems journal, and conference proceedings of AAAI, IJCAI, and AAMAS.

• Quantum Computing
• High-Performance Computing (HPC)
• Optimization Algorithms
• +۳ مورد دیگر

Katarzyna Rycerz is an academic researcher specializing in quantum computing, high-performance computing (HPC), and optimization algorithms. Her work spans hybrid quantum-classical systems, complex network analysis, and multiscale simulations. Collaborating with institutions such as AGH University of Science and Technology (inferred via co-authors like Marian Bubak), her research focuses on advancing quantum algorithms for optimization problems, quantum annealing applications, and software frameworks for distributed computing environments. Her notable contributions include developing libraries like QHyper for hybrid quantum-classical optimization, analyzing quantum walk-based image segmentation, and exploring the application of quantum computing to classical problems like the Traveling Salesman Problem. She has also contributed to foundational studies in quantum game theory and functional programming paradigms in HPC. Rycerz's publications reflect a sustained focus on interdisciplinary research, bridging quantum mechanics, computer science, and applied mathematics. Her work often addresses practical challenges in computational efficiency, algorithm design, and scalable simulation frameworks for complex systems.

• Human-Robot Interaction
• Affective Computing
• E-commerce Search Algorithms
• +۴ مورد دیگر

Mingming Li is an active professor at the Chinese Academy of Sciences with extensive research contributions across multiple domains including human-robot interaction, e-commerce systems, and wireless communications. With over 110 publications spanning from 2007 to 2025, Dr. Li demonstrates consistent scholarly productivity and leadership in interdisciplinary research. Dr. Li's research interests span Human-Robot Interaction , Affective Computing , E-commerce Search Algorithms , Machine Learning , and Wireless Communications . Recent work focuses on understanding how humanoid robots' multimodal characteristics (appearance, voice, touch) affect users' emotional responses and cognitive processing, employing physiological measures like ECG and fNIRS. Simultaneously, Dr. Li leads research in e-commerce search optimization, developing novel deep learning architectures for improved recommendation systems. The publication record reveals significant trends toward increasingly sophisticated multimodal analysis in HRI and more efficient neural architectures for practical applications. Dr. Li's work bridges theoretical advancements in machine learning with practical implementations in human-centered systems. Dr. Li has advised numerous graduate students including Jiahao Chen, Aung Naing Win, and Songlin Wang, with whom collaborative publications demonstrate strong mentorship. Research has been supported by substantial funding reflected in the volume and quality of publications across top venues. Current research activities include leading projects on digital twin-based thermal error compensation, multimodal affective robot design, and advanced search algorithms for e-commerce platforms, indicating an active laboratory environment with multiple ongoing research threads.

Vinzenz Thomaمشاهده پروفایل

پژوهشگر ارشد

• Multi-Agent Reinforcement Learning
• Mechanism Design
• Algorithmic Game Theory
• +۳ مورد دیگر

Vinzenz Thoma is a Doctoral Fellow at the ETH AI Center, ETH Zurich, affiliated with the Department of Computer Science (D-INFK). His research focuses on the intersection of multi-agent reinforcement learning, mechanism design, and algorithmic game theory. He is supervised by Professors Niao He and Sven Seuken. His work addresses challenges in dynamic auction environments, incentive alignment through reinforcement learning, and automated mechanism design. Recent publications explore topics like equilibrium computation in sequential auctions and contextual bilevel optimization for multi-agent coordination. Thoma's research bridges theoretical foundations with practical applications in game theory and AI-driven decision-making systems. He contributes to advancing methodologies for strategic interactions in complex, dynamic systems.

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +۷ مورد دیگر

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.