جست‌وجوی استاد و پژوهشگر

• Computational Complexity
• Algorithmic Game Theory
• Mechanism Design
• +۳ مورد دیگر

Argyrios Deligkas is a Senior Lecturer (equivalent to Associate Professor) at the Royal Holloway University of London. He obtained his PhD from the University of Liverpool under the supervision of Rahul Savani and held postdoctoral positions at the University of Liverpool and the Technion (Industrial Engineering and Management department). His research focuses on computational aspects of game theory, optimization, and complexity. Key interests include: Algorithmic Game Theory and Mechanism Design Computational Complexity (PPAD/PPA-hardness) Fixed-parameter algorithms and combinatorial optimization Fair division and resource allocation Equilibrium computation in multi-agent systems Recent publications (2023-2025) demonstrate a strong emphasis on computational hardness in fair division (e.g., PPA-hardness proofs), mechanism design with real-world constraints, and parameterized algorithms for pathfinding/graph problems. Collaborative work with European researchers dominates his output, with frequent appearances at AAAI, IJCAI, and AAMAS. He actively presents at conferences (e.g., ALGA2025, WINE 2024) and collaborates internationally, including visits to universities in Vienna, Prague, Glasgow, and Edinburgh.

Farhad Mohsinمشاهده پروفایل

استادیار

• Computational Social Choice
• Fairness in Machine Learning
• Preference Aggregation
• +۴ مورد دیگر

Farhad Mohsin is an Assistant Professor in the Department of Math and Computer Science at College of the Holy Cross in Worcester, MA. He earned his PhD in Computer Science from Rensselaer Polytechnic Institute (RPI) between 2018-2023, working with Professor Lirong Xia, and completed his BSc in Electrical and Electronic Engineering at Bangladesh University of Engineering and Technology (BUET) from 2010-2015. Prior to his academic career, he worked as a Telecommunications Engineer/Data Analyst at Grameenphone Ltd, Bangladesh. Dr. Mohsin's research focuses on computational social choice, particularly preference aggregation and fair decision-making. He explores ML-based techniques for designing economic mechanisms, with specific interest in fairer voting rules. His broader research interests include natural language processing, interpretable machine learning, and multi-agent reinforcement learning. His recent publications (2021-2024) examine computational complexity of voting paradoxes, election data generation using deep learning, and natural language-based preference aggregation. His work has appeared in top venues including IJCAI, AAMAS, and JAIR. Dr. Mohsin teaches courses including Data Mining, Data Structures, Analysis of Algorithms, Discrete Structures, and Advanced Algorithms. He supervises undergraduate research projects, with recent honors theses on multi-agent reinforcement learning and fairness in zoning laws.

María Belén Pérez Lanchoمشاهده پروفایل

استاد

• Multi-agent Systems
• Ambient Intelligence
• Context-aware Computing
• +۵ مورد دیگر

María Belén Pérez Lancho is a Full Professor in the Department of Computer Science and Automation at the University of Salamanca, Spain. With over 25 years of academic experience since completing her PhD in 1995, she maintains active affiliations with multiple research groups including BISITE (Bioinformatics, Intelligent Computing Systems and Educational Technology), CaUSAL (Academic Culture, Heritage and Social Memory), and Process Supervision and Control. Dr. Pérez Lancho earned her doctoral degree from the University of Salamanca with her thesis "Integrated process control system: a practical approach" supervised by Dr. Eladio Sanz García. Her academic foundation in systems engineering and automation has evolved into diverse research applications across multiple domains. Her research program demonstrates remarkable breadth while maintaining technical coherence. She has pioneered context-aware multiagent systems for home care environments, developing architectures like HoCa and HoCCAC that monitor patients and optimize task scheduling. In environmental science, she created hybrid intelligent systems for forest fire prediction using Case-Based Reasoning with topology-preserving algorithms. More recently, she has addressed cybersecurity challenges in educational technology through two-factor authentication systems. Her work consistently bridges theoretical computer science with practical implementations in real-world settings. Analysis of her publication trajectory reveals a consistent focus on adaptive intelligent systems that respond to environmental changes. Her earliest work established foundations in systems engineering, which evolved into sophisticated multi-agent applications across healthcare, environmental monitoring, and security domains. The most recent publications continue this trajectory while addressing contemporary challenges in educational technology security. Dr. Pérez Lancho maintains extensive collaborative relationships, particularly with Juan M. Corchado at the University of Salamanca, as evidenced by numerous co-authored publications. Her research has been implemented in actual home care environments and environmental monitoring systems, demonstrating the practical impact of her work. Her research infrastructure includes: BISITE group focusing on intelligent computing systems and educational technology CaUSAL group examining academic culture and heritage Process Supervision and Control group applying her foundational work to industrial contexts These affiliations support her interdisciplinary approach to solving complex problems through computational methods.

Shaheen Fatimaمشاهده پروفایل

مدرس ارشد

• Multiagent Systems
• Autonomous Agents
• Game Theory
• +۴ مورد دیگر

Shaheen Fatima serves as a Senior Lecturer in the Department of Computer Science, actively engaged in research and academic instruction. Her office is located in N.3.15 of the Haslegrave Building, with direct contact via +44 (0) 1509 222 677. Her research expertise centers on multiagent systems and autonomous agents, with specialized focus on resource allocation using market-based methodologies and agent-mediated negotiation for electronic commerce through game theory frameworks. This work addresses strategic optimization in distributed artificial intelligence, emphasizing efficiency in resource distribution and competitive decision-making protocols within complex computational environments.