جست‌وجوی استاد و پژوهشگر

Imran Ahmadمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Imran Ahmad is a Professor in the School of Computer Science at the University of Windsor. He holds a Ph.D. in Computer Science from Wayne State University (1997), with prior degrees in Applied Physics (Electronics) from Karachi and an M.S. in Computer Science from Michigan. His research focuses on computer vision, machine learning, and image processing, including applications in traffic analysis, generative AI, and medical imaging. He has authored over 50 publications since 1995, with recent work emphasizing deep learning for vehicle detection, human activity recognition, and generative models. Education: B.Sc., M.Sc. in Applied Physics (Electronics) – Karachi M.S. in Computer Science – University of Michigan Ph.D. in Computer Science – Wayne State University, Michigan, USA (1997) Research Interests: Dr. Ahmad’s work bridges theoretical and applied computer science. Key areas include: Advanced computer vision techniques for real-world systems (e.g., traffic monitoring, medical diagnostics) Generative AI for image and video synthesis Deep learning optimization and transfer learning Human activity recognition using stereo trajectories and motion analysis Agent-based simulations for crowd behavior modeling Key Publications Trends (2020–2025): Recent work emphasizes: Generative models for text-to-image/video synthesis Transfer learning for cross-domain image classification 3D trajectory analysis for activity detection Vehicle detection/classification systems Accessibility tools for color vision deficiency

Jamie Matthewsمشاهده پروفایل

مدرس ارشد

• Interactive Storytelling
• Narrative Generation
• Mise-en-scène
• +۳ مورد دیگر

Dr. Jamie Matthews is a Senior Lecturer in Games Development at the Department of Computing & Games, Teesside University, affiliated with the Centre for Digital Innovation. His research focuses on interactive storytelling, narrative generation, and virtual student behavior modeling. He holds a Doctoral Thesis in Crowd Simulation for Interactive Entertainment Applications (2021). Education: PhD in Computer Science (2021) from Teesside University, supervised by Prof. John Porteous and Dr. Christopher Jayne. Research interests include Mise-en-scène techniques for narrative actions, benchmarking frameworks for virtual agents, and semantic representation in storyworlds. His work bridges computer science and digital arts, emphasizing procedural storytelling and multiagent systems. Publications span conference contributions on virtual student behavior benchmarks and narrative staging systems. His MISER framework has been highlighted in multiagent systems research. Awards: No specific honors listed. Active in grant-funded projects related to interactive entertainment applications. Labs/Teams: Involved with the Centre for Digital Innovation, focusing on digital innovation in games and interactive technologies.

• Applied Mathematics
• Computational Neuroscience
• Mathematical Biology
• +۴ مورد دیگر

Thoa Thieu is an Assistant Professor at the School of Mathematical and Statistical Sciences and College of Health Professions at the University of Texas Rio Grande Valley. She holds email addresses at thoa.thieu@utrgv.edu and thoathieu28@gmail.com . Dr. Thieu's education includes a Double Ph.D. in Mathematics from Gran Sasso Science Institute (Italy) and Karlstad University (Sweden), supervised by Prof. Adrian Muntean and Dr. Matteo Colangeli. She earned a Master's in Applied Mathematics from the University of Orleans and Tours (France), and a B.S. in Mathematics and Statistics from the University of Science in Vietnam. Her research focuses on stochastic systems modeling with applications in: Neuroscience (neuronal dynamics, synaptic plasticity) Crowd behavior analysis (pedestrian flows, active-passive interactions) Biological systems (insulin transport in beta cells, cellular cytoskeleton dynamics) Interdisciplinary computational methods (coupled Skorokhod SDEs, lattice gas frameworks) Her recent work emphasizes multi-fidelity modeling and well-posedness analysis in complex systems. She has contributed to neuromorphic computing applications and disaster-evacuation simulations through her mathematical frameworks. Professional experience includes Postdoctoral Fellowships at Indiana University (2023-2024) under Dr. William Holmes, and at Wilfrid Laurier University (2021-2022) under Prof. Roderick Melnik. Her research outputs bridge pure mathematics (stochastic PDEs, uniqueness proofs) with applied domains in health sciences and social dynamics.

Narjes Sadeghiamirshahidiمشاهده پروفایل

استادیار مهمان

• Supply Chain Management
• Food Logistics
• Simulation Modeling
• +۷ مورد دیگر

Narjes Sadeghiamirshahidi is a Visiting Assistant Professor in the Department of Mechanical and Industrial Engineering at the Tagliatela College of Engineering, University of New Haven. She also serves as the Coordinator for the M.S. Engineering and Operations Management program. Her work bridges academic research with industry applications, focusing on enhancing supply chain efficiency across diverse sectors. Education: B.Sc. Industrial Engineering – Industrial Production, Yazd University M.Sc. Industrial Engineering, Universiti Teknologi Malaysia Ph.D. Industrial Engineering, University of Texas at Arlington Research Interests: Dr. Sadeghiamirshahidi specializes in Supply Chain Management , Food Logistics , and Simulation Modeling . Her primary research addresses regional food transportation challenges for Texas farmers and ranchers, funded by the USDA through the Sustainable Agriculture Research and Education (SARE) project. Key objectives include developing collaborative transportation networks, analyzing logistics systems with tools like agent-based modeling and system dynamics, and improving sustainability in agricultural and healthcare supply chains. Publications & Trends: Her articles highlight innovative solutions for transportation collaboration in agriculture and manufacturing, leveraging system dynamics, agent-based modeling, and data-driven analysis. Recent work emphasizes resilience in food supply chains during crises and operational efficiency in SMEs using crowd-shipping platforms. Grants & Professional Experience: Prior to academia, she held roles as a Quality Management System Consultant for automotive and industrial firms, including SQS Co., RKP Co., and Satrap Jahan Co., focusing on ISO compliance and process optimization. Her USDA-funded SARE project underscores her commitment to applied research in sustainable agriculture and regional logistics. Labs & Collaborations: While no formal lab affiliations are listed, her research involves interdisciplinary teams designing transportation management tools for farmers and collaborating with federal agencies (e.g., USDA) to advance regional food systems.

Ilias Sakellariouمشاهده پروفایل

استادیار

• Constraint Logic Programming
• Multi-Agent Systems
• Artificial Intelligence
• +۳ مورد دیگر

Ilias Sakellariou is an Assistant Professor in the Department of Applied Informatics at the University of Macedonia. He holds a BSc in Physics from Aristotle University of Thessaloniki, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a PhD in Distributed Constraint Logic Programming from Aristotle University of Thessaloniki. Education: BSc in Physics, Aristotle University of Thessaloniki MSc in Knowledge-Based Systems, University of Edinburgh PhD in Distributed Constraint Logic Programming, Aristotle University of Thessaloniki Research Interests: Constraint Logic Programming, Multi-Agent Systems (BDI, Agent-Based Simulation), AI applications in 5G networks, cloud/network slicing, and distributed systems. His work includes projects like NECOS (cloud slicing) and UNIC (5G network infrastructure). Publications: His research spans agent-based modeling, network slicing, and AI-driven systems. Recent works focus on emotional agents in simulations and 5G infrastructure optimization. Projects & Grants: Participated in EU-funded projects such as NECOS (H2020) and UNIC (FED4FIRE+), contributing to cloud slicing and network architecture design. His work includes software tools like TXStates (NetLogo extensions for state machines) and eX-Machines for evacuation simulations. Labs/Teams: Develops simulation tools for agent-based systems, emphasizing NetLogo extensions for complex agent modeling and validation.

Klaus Bogenbergerمشاهده پروفایل

استاد

• Traffic Engineering
• Intelligent Transportation Systems
• Urban Mobility Planning
• +۷ مورد دیگر

Prof. Klaus Bogenberger is a full professor at the Technical University of Munich (TUM), holding the Traffic Engineering and Control professorship within the TUM School of Engineering and Design. His career includes roles at BMW Group, TRANSVER GmbH, and the University of the Bundeswehr Munich. He holds a doctorate from TUM on adaptive fuzzy systems for traffic control. His research focuses on urban traffic flow theory, mathematical modeling using novel data sources (vehicle/drone data), and emerging technologies like autonomous vehicles. Key areas include traffic simulation, traffic control methods prioritizing pedestrians/cyclists, quality analysis of traffic information, and innovative mobility systems (on-demand transit, robot taxis, shared mobility). Notable achievements include the HEUREKA Prize (2001). Recent work explores tradable mobility credit schemes (MobilityCoins), digital twin benchmarking (TUM2TWIN), and infrastructure optimization for electric vehicles (CDRpy framework). His interdisciplinary projects integrate machine learning, agent-based modeling, and sensor networks. Current initiatives emphasize equitable mobility solutions and policy analysis using large-scale data from tracking panels like the Mobilität.Leben study.

• Computer Vision
• Human Mobility Modeling
• Infrastructure Monitoring
• +۴ مورد دیگر

Takehiro Kashiyama is an Assistant Professor at the Institute of Industrial Science, University of Tokyo, specializing in civil engineering with a focus on infrastructure monitoring and urban mobility systems. His work bridges computer vision, machine learning, and civil infrastructure applications. Dr. Kashiyama's research interests center around Computer Vision applications for infrastructure monitoring , Human Mobility Modeling , and Disaster Response Systems . His work develops innovative approaches for road damage detection using deep learning techniques, traffic flow reconstruction from moving camera systems, and human mobility modeling for urban planning and disaster scenarios. His research integrates cutting-edge computer vision techniques with practical civil engineering applications. His publication record shows a consistent focus on applying AI techniques to civil infrastructure problems, with a particular emphasis on road damage detection systems and human mobility modeling. His work often involves collaboration with Yoshihide Sekimoto and other researchers at the University of Tokyo. Dr. Kashiyama has made significant contributions to road damage detection challenges, human mobility modeling using agent-based approaches, and traffic flow analysis systems. His work has been published in journals such as Computer-Aided Civil and Infrastructure Engineering, Sensors, and IEEE conferences including Big Data and ITSC.

David Cazierمشاهده پروفایل

استاد

• Virtual Reality
• Computer Graphics
• Computational Geometry
• +۴ مورد دیگر

David Cazier is a **Professor at the University of Strasbourg** and a **Researcher at the ICube Laboratory**, specializing in geometric and graphical computing. He also teaches computer science at the **IUT of Haguenau**. His research focuses on three main axes: multiresolution geometric models, real-time interaction in virtual reality, and tools for adapting such models to simulations and visualization. His work bridges theoretical advancements in geometric modeling with practical applications in medical imaging, crowd simulation, and augmented reality. **Research Interests**: Cazier’s expertise includes virtual reality (VR) immersion, real-time interaction techniques, and the development of efficient geometric models for complex simulations. His projects often involve hierarchical meshes, topological changes during deformations, and applications in surgical AR. Key areas include multiresolution representations, collision detection in dynamic environments, and non-manifold geometry processing. **Publications**: His recent work emphasizes medical applications (e.g., surgical AR with topological changes) and foundational contributions to geometric data structures (e.g., CGoGN, CPH). He explores user interaction challenges in the digital divide era and develops tools for crowd simulation using deformable agents. **Collaborations and Projects**: Active in collaborations with the ICube Lab’s IGG team, Cazier contributes to platforms like CGoGN and InVirtuo. His research spans national and European projects, including IHU, ANR, and CNRS initiatives. He supervises internships on geometric models for VR applications and interdisciplinary projects combining geometry with medical and industrial challenges.

Amarnath Banerjeeمشاهده پروفایل

استاد

• Health Care Systems
• Information Systems
• Manufacturing Systems
• +۳ مورد دیگر

Amarnath Banerjee is a Professor and Associate Department Head for Undergraduate Affairs in the Department of Industrial & Systems Engineering at Texas A&M University. He holds a Ph.D. in Industrial Engineering and Operations Research from the University of Illinois at Chicago (1999). His research focuses on modeling, simulation, and visualization in healthcare systems, information systems, manufacturing systems, and Industry 4.0. Notable contributions include work on disaster evacuation networks, smart manufacturing systems, and ethical engineering education. Dr. Banerjee has received prestigious awards such as the Corrie and Jim Furber Faculty Fellow (2016–2017) and the Charles W. Crawford Service Award (2012–2013). His research spans healthcare operations optimization, agent-based simulations for disease spread, and applications of reinforcement learning in energy systems. His recent publications highlight advancements in digital twin technology for Industry 4.0, ethics education in engineering curricula, and predictive analytics for energy demand. He has also contributed to interdisciplinary collaborations, including projects on medical device design and sustainable energy integration.

Ioanna Lykourentzouمشاهده پروفایل

دانشیار

• Collaborative Systems
• Crowd Systems
• Machine Learning
• +۵ مورد دیگر

Ioanna Lykourentzou is an Associate Professor at Utrecht University, leading the Collaborative Technologies Lab and coordinating Computing Science Master's and Information Sciences Honors Bachelor's programs. She holds a PhD and ECE degree from the National Technical University of Athens. Her research focuses on collaborative systems, combining computational science (machine learning, optimization) with social sciences (team dynamics, personality assessment). Education background includes a PhD and ECE degree from National Technical University of Athens. She has held postdoctoral and visiting roles at Carnegie Mellon University, INRIA Nancy-Grand Est, and Henri Tudor. Research interests span computational team formation, Human-AI collaboration, and cultural heritage accessibility. Recent work includes projects on GenAI in education (NWO Take-off grant), algorithmic team formation for game development (EU MSCA funding), and ethical AI frameworks. Active in open science initiatives as an Open Software Fellow and Ethics Review Board member. Key collaborations include MIT-Netherlands Seed Fund projects on Human-AI design, and interdisciplinary work in digital humanities through Utrecht's AI in Cultural Inquiry SIG. Her lab hosts researchers like Pedro Zambon (MSCA fellow) and Heleen Kerstholt (AI-enabled teamwork).

Sean Grimesمشاهده پروفایل

عضو هیئت علمی

• Swarm Intelligence
• Biologically Inspired AI
• Multi-Agent Systems
• +۷ مورد دیگر

Sean Grimes is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. He has been actively involved in teaching and research since 2015, earning his PhD in 2023 and transitioning into a full-time faculty role in 2022. Education: PhD in Computer Science, Drexel University, College of Computing & Informatics (2016–2023) MS in Computer Science, Drexel University, College of Computing & Informatics (2015–2018) BS in Neuroscience, Ursinus College (2008–2012) His research focuses on biologically inspired artificial intelligence, particularly swarm intelligence, multi-agent systems, and prediction mechanisms grounded in wisdom-of-crowds theory. He integrates principles from neuroscience and AI to develop modular, distributed systems capable of collective decision-making. His work emphasizes transparency, adaptability, and privacy in machine learning models. The recent publications highlight a consistent trend in developing agent-based systems for classification and prediction, with applications ranging from sports betting to medical diagnostics. These systems leverage swarm intelligence and biological metaphors to improve accuracy, reduce training time, and allow for incremental feature integration without retraining. The research demonstrates strong interdisciplinary potential, bridging computer science, biology, and cognitive science. While no formal scientific awards are listed, his peer-reviewed publications in journals such as Future Internet , Applied Sciences , and conference proceedings like IEEE BIBM reflect scholarly recognition and impact. Grimes has been deeply involved in mentoring and teaching, serving as a Teaching Assistant, Senior Teaching Assistant, and now Assistant Teaching Professor. He has developed course materials, led lectures, and supported student learning across multiple computer science courses. His entrepreneurial experience includes co-founding Ecodren Research and Irregex, where he applied AI to real-world problems in feedback systems and data analysis. He has not received external grant funding explicitly mentioned in the text. His research was conducted within the Geometric Biomedical Computing Group at Drexel University, where he collaborated on biologically inspired AI projects. His work on WoC-Bots and Ortus represents ongoing efforts to build intelligent systems modeled on biological principles.

• Cross-Cultural Psychology
• Organizational Behavior
• Intercultural Management
• +۲ مورد دیگر

Professor Mikhail Minkov is a distinguished faculty member at Varna University of Management, Bulgaria, with a concurrent affiliation at International University College in Sofia. Holding the rank of Professor, he specializes in cross-cultural research within organizational behavior and management contexts. His academic credentials include: PhD in Social Anthropology, Sofia University (2010) Master's in Linguistics, Sofia University (1988) Professor Minkov's research centers on cultural differences in society and management, with pioneering contributions to Hofstede's cultural dimensions framework. He investigates how national culture shapes organizational behavior, societal values, and economic outcomes through large-scale comparative studies across 50+ countries. His work uniquely bridges anthropology, psychology, and management science, examining intersections between genetic factors, cultural evolution, and social phenomena like happiness and accident proneness. Recent publications (2015-2018) reveal a methodological shift toward integrating biological and sociological perspectives, with increasing focus on sociogenetics and computational modeling. Key trends include revising cultural dimension metrics, analyzing parental socialization of values, and developing unobtrusive measures of national traits. His work consistently appears in top cross-cultural journals, demonstrating rigorous multi-country validation approaches. Professor Minkov maintains an active international research profile through collaborations with scholars across six continents. His projects typically involve complex dataset synthesis from diverse national samples, suggesting substantial research funding support though specific grants aren't documented. While student mentoring details are limited, his publication co-authorship patterns indicate supervision of junior researchers in cross-cultural methodology. Though not leading a named laboratory, Professor Minkov's research ecosystem operates through dynamic international partnerships, producing influential frameworks for understanding cultural variation in organizational contexts.

J.W.M. Gerritsمشاهده پروفایل

استاد

• Innovation Management
• Information Technology in Business
• Internet Business Models
• +۳ مورد دیگر

Prof. J.W.M. Gerrits is a Full Professor at Vrije Universiteit Amsterdam’s School of Business and Economics, Department of Knowledge, Information and Innovation, and affiliated with the Amsterdam Business Research Institute and KIN Center for Digital Innovation. His work bridges organizational innovation and technology, with a focus on IT-driven business process optimization, internet applications, and technology’s role in enhancing organizational capabilities. He holds ancillary roles as an advisor at Woxsen University and director at Newtech Solutions. Research interests include innovation management, e-business models, entrepreneurship, and agent-based simulations of social and behavioral phenomena. Notable areas of exploration involve emotion recognition in virtual environments, legal/ethical AI implications, and crowd behavior analysis. His teaching includes the Digital Entrepreneurship Project course. Recent work emphasizes AI ethics, social robotics in healthcare, and computational models of emotion contagion. His publications span interdisciplinary topics like panic amplification in crowds, gamified mental health interventions, and domain-adaptive sentiment analysis tools like SentiMap. Labs/teams: Active contributor to the KIN Center for Digital Innovation and Amsterdam Business Research Institute.

Andrzej Kasprzakمشاهده پروفایل

استاد

• Machine Learning
• Computer Science
• Network Optimization
• +۳ مورد دیگر

Professor Andrzej Kasprzak is a faculty member at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, serving as Head of the Department of Systems and Computer Networks. His research spans machine learning, network optimization, and data stream processing, with applications in telecommunications, healthcare, and industrial systems. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Systems and Computer Networks Email: andrzej.kasprzak@pwr.edu.pl Kasprzak’s research focuses on integrating machine learning with network optimization, particularly in elastic optical networks and data stream environments. His work addresses challenges in multi-agent pathfinding, fake news detection, and 3D printing automation, emphasizing practical experimentation systems. Recent publications highlight trends in ensemble learning for real-time data streams, domain adaptation in AI systems, and innovative optimization algorithms for network traffic and scheduling problems. These contributions bridge theoretical advancements with industrial applications. As Head of Department, Kasprzak leads initiatives in systems and computer networks, fostering interdisciplinary collaboration in artificial intelligence and telecommunications. His career reflects a commitment to advancing computational methodologies in complex environments.

• Artificial Intelligence
• Game Theory
• Multi-Agent Systems
• +۳ مورد دیگر

Agnieszka Mensfelt is a Postdoctoral Research Assistant at the Department of Computer Science, Royal Holloway University of London. Her work focuses on advancing strategic AI systems through intersections of game theory, multi-agent systems, and large language model (LLM) applications. She contributes to research areas aligned with UN Sustainable Development Goals, particularly in technological innovation and computational problem-solving. Research Themes: Strategic reasoning, quality-diversity optimization, and teleo-reactive behavior modeling Methodologies: LLM-enhanced recursive reasoning, domain-specific dissimilarity metrics, and agent-based simulation Collaborations: Active researcher network across Europe with recent presentations at international conferences Recent publications highlight her expertise in formalizing complex game structures, improving AI behavioral transparency, and developing novel optimization frameworks. Despite no explicit awards mentioned, her frequent conference contributions indicate significant community engagement. Her work spans theoretical and applied aspects of computational intelligence, balancing technical innovation with ethical considerations in AI deployment. Current activities include oral presentations at major AI workshops and collaborations with interdisciplinary research teams.