جست‌وجوی استاد و پژوهشگر

Pete Barbrook-Johnsonمشاهده پروفایل

مدرس

• Environmental Economics
• Complexity Science
• Systems Thinking
• +۳ مورد دیگر

Pete Barbrook-Johnson is a Senior Research Associate and Departmental Research Lecturer in the Economics of Environmental Change at the University of Oxford. He holds appointments at both the Environmental Change Institute (ECI) and the Smith School for Enterprise and the Environment (SSEE), within the School of Geography and the Environment. Barbrook-Johnson serves as Deputy Director of the NERC-funded Agile Initiative and Technical Modelling Lead for the UK government EEIST project on new economic modeling of energy innovation and transition. His research interests focus on applying complexity science and systems thinking to environmental, energy, and public health challenges. He regularly employs agent-based modeling and systems mapping methodologies in his work. His research spans climate policy analysis, energy transition modeling, sustainable development, and the application of complexity economics to real-world policy challenges. His work often involves international collaboration, particularly with researchers from Brazil, China, and India. Barbrook-Johnson's recent publications demonstrate a strong trend toward integrating systems thinking with economic modeling for environmental policy. His work bridges the gap between complex systems science and practical policy application, with a particular emphasis on making economic models more responsive to the needs of policymakers. The publications span multiple disciplines including environmental economics, energy systems, climate policy, and complexity science. His notable scientific contributions include: His book on systems mapping, co-authored with Dr. Alexandra Penn Development of a library of new economic modeling case studies Leading production of a major international report on new economic modeling approaches for energy transition Contributing to the development of sensitive intervention points as a strategic approach to climate action Barbrook-Johnson has received recognition through the ESRC Innovation Fellowship and leads significant research initiatives including the Agile Initiative. His work demonstrates strong engagement with policy communities and international research collaborations. He also teaches undergraduate and masters courses focused on the economics of environmental change and the application of complexity sciences to environmental issues. He maintains affiliations with multiple research centers including the Institute for New Economic Thinking at Oxford, St Catherine's College, the Centre for the Evaluation of Complexity Across the Nexus (CECAN), and as a Visiting Fellow at the Centre for Research in Social Simulation (CRESS) at the University of Surrey.

Xiaolin Huمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems Science
• Agent-Based Systems
• +۳ مورد دیگر

Xiaolin Hu is a Professor in the Department of Computer Science at Georgia State University within the College of Arts & Sciences. His research focuses on modeling and simulation theory with applications in complex systems science, agent-based systems, and advanced computing environments. Ph.D. in Electrical and Computer Engineering, University of Arizona (2004) NSF CAREER Award recipient Senior member of IEEE and Society for Modeling and Simulation International (SCS) Associate editor for ACM Transactions on Modeling and Computer Simulation (TOMACS) and other journals SIMULATION: Transactions journal and International Journal of Modeling, Simulation, and Scientific Computing (IJMSSC) Dr. Hu's research spans three major areas: Dynamic Data-Driven Simulation (DDDS): Development of simulation frameworks that assimilate real-time data, particularly applied to wildfire spread prediction and autonomous systems. Multi-Agent Systems: Study of adaptive behaviors and coordination mechanisms for unmanned aircraft and social/public health systems. Advanced Computing: Implementation of parallel and cloud computing solutions for large-scale simulations. His recent publications (2025-2023) demonstrate a strong focus on integrating real-time data from UAVs and satellite systems into wildfire simulations, utilizing Bayesian methods for sequential data assimilation, and developing cloud-based simulation services. These works bridge computational modeling with environmental science applications. Scientific recognitions include: National Science Foundation (NSF) CAREER Award Dr. Hu has chaired multiple international conferences in modeling and simulation and leads the SIMS Lab at Georgia State University, which specializes in simulation technologies and their applications in diverse domains.

Haitao Heمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Transport Engineering
• Urban Planning
• +۳ مورد دیگر

Haitao He is a Reader in Transport and AI at Loughborough University, UK, and leads the Transport AI Innovation Centre (TRAICE). He holds a BSc (First-Class Honours), MPhil, and PhD, and is a Fellow of the Higher Education Academy (FHEA). His research focuses on AI-driven solutions for smart cities and sustainable urban mobility, including traffic simulation, machine learning, and digital twinning. He develops methodologies in traffic flow theory, agent-based modeling, and deep learning to address challenges in automation, electrification, and shared mobility. Research Interests: AI in transportation, micro-mobility, sustainability, and data-driven urban planning. Projects: UKRI Future Leaders Fellowship on net-zero urban transport, TraffEase (Manchester Prize), and BusMONITOR (KTP with Vectare). His expertise spans predictive traffic analytics, micro-mobility patterns, and infrastructure optimization. He has been awarded the UKRI Future Leaders Fellowship and has contributed to projects like SignBus (Swiss National Science Foundation) and WEAVE (highway weaving section design). Key Achievements: Developed TRAICE to advance transport AI, published over 30 articles on topics like traffic flow modeling and pandemic mobility impacts. Haitao advises on PhD and postdoctoral research, focusing on AI and transport intersections. His work integrates academic and industry collaborations, driving innovation in smart mobility and policy-making.

Dimitrios Nikolopoulosمشاهده پروفایل

استاد

• High-Performance Computing
• Systems Runtime Systems
• Memory Management
• +۶ مورد دیگر

Dimitrios Nikolopoulos is the John W. Hancock Professor of Engineering at Virginia Tech's College of Engineering, within the Department of Computer Science. His research focuses on high-performance computing, systems runtime systems, memory management, and edge computing. He holds a Chartered Engineer (CEng) certification. Education: M.Eng., University of Patras, Greece (1996) M.Sc., University of Patras, Greece (1997) Ph.D., University of Patras, Greece (2000) Research Interests: His work spans transprecision computing, parallel programming paradigms, efficient inference frameworks for large language models (LLMs), and energy-efficient server ecosystems. Recent efforts emphasize optimizing edge computing systems, GPU resource sharing, and adaptive memory management in cloud-edge environments. Recent Trends in Publications: Recent studies highlight advancements in edge-serving frameworks (e.g., SLED), multi-agent systems for HPC code optimization (MARCO), and novel approaches to GPU and memory resource utilization in constrained settings. Themes include reducing latency, improving scalability, and integrating AI-driven techniques into HPC workflows. Grants & Advising: Details on current grants and advisees are not explicitly listed in the provided materials. Labs/Teams: His research is conducted through collaborative groups within Virginia Tech's Department of Computer Science, focusing on systems, parallel computing, and AI/ML infrastructure.

Stephanie Tulk Jessoمشاهده پروفایل

استادیار

• Human Factors in Healthcare
• Human-AI Interaction
• Human-Robot Interaction
• +۵ مورد دیگر

Stephanie Tulk Jesso is an Assistant Professor at the School of Systems Science and Industrial Engineering at Binghamton University. Her research focuses on human-centered design principles applied to healthcare technology, human-AI interaction, and social robotics. She holds a joint appointment in systems engineering and has collaborated extensively with clinical partners to address real-world healthcare challenges. Her work emphasizes translational research, aiming to bridge gaps between technological innovation and practical healthcare applications. Key research themes include improving patient-clinician interactions through AI tools like VizTrust, reducing hospital fall risks via human-centered robotics, and evaluating clinician involvement in AI development. She has pioneered studies on trust dynamics in human-AI/robot interactions using experimental methods in virtual environments and healthcare settings. Notable projects include co-designing AI chatbots with hospitals and exploring ethical implications of autonomous medical robotics. Her multidisciplinary approach integrates insights from social cognition, behavioral economics, and engineering design. Recent work examines how perceived competence and context influence human-AI collaboration, with implications for healthcare system design. She actively participates in translational research initiatives to ensure technologies are developed with end-user needs central to the process. Dr. Jesso's research trends show strong focus on: (1) Healthcare technology adoption challenges, (2) Ethical frameworks for AI/robotics in medicine, (3) Quantitative evaluation of human-technology collaboration, and (4) Co-design methodologies involving clinicians and patients. Her work frequently appears in top journals addressing human factors, medical informatics, and robotics applications. In advising, she mentors students working at the intersection of technology and healthcare ethics. Her lab collaborates with regional hospitals and tech companies to develop practical solutions for clinical environments. Current projects include AI-driven patient monitoring systems and designing intuitive interfaces for collaborative surgical robots.

Fangzheng Lyuمشاهده پروفایل

استادیار

• GIS Computational and Data Science
• Urban Informatics
• CyberGIS & Geospatial Computing
• +۷ مورد دیگر

Fangzheng Lyu is an Assistant Professor in the Department of Geography at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Natural Resources and Environment. His research focuses on advancing GIScience, geospatial data science, and computational methods to address complex urban and environmental challenges using big data. He holds a Ph.D. from the University of Illinois Urbana-Champaign (2024), an M.S. from the same institution (2021), and a B.E. from the University of Hong Kong (2018). Dr. Lyu's expertise includes geospatial data science, cyberGIS, urban informatics, and geospatial AI. His work spans three core areas: (1) urban dynamics modeling using machine learning and cyberGIS, (2) scalable spatial algorithms for remote sensing and big data analysis, and (3) democratizing access to advanced geospatial cyberinfrastructure. He teaches courses such as Principles of GIS, Geospatial Data Science, and Spatial Data Science at Virginia Tech. His research has been published in leading journals like International Journal of Geographical Information Science , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , and Sustainability . Notable contributions include frameworks for urban heat island prediction, spatiotemporal image fusion models (e.g., Multi-stream STGAN), and methods for analyzing spatial accessibility during public health crises. Dr. Lyu actively contributes to interdisciplinary initiatives, including the Center for Geospatial Information Technology at Virginia Tech. His work emphasizes reproducibility and collaboration, with a focus on bridging geospatial research and education through cyberGIS platforms like CyberGIS-Jupyter. He has received funding for proposals integrating geospatial technologies with urban sensing and epidemiological modeling.

Ryan Zurakowskiمشاهده پروفایل

دانشیار

• Mathematical Modeling
• HIV Dynamics
• Biomedical Engineering
• +۵ مورد دیگر

Ryan Zurakowski is an Associate Professor and Interim Department Chair in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a Ph.D., M.S., and B.S. in Electrical and Computer Engineering from the University of California, Santa Barbara. His research focuses on developing mathematical models to understand viral disease dynamics, particularly HIV, with applications in biomedical engineering and control systems. Dr. Zurakowski’s work combines advanced modeling techniques with Bayesian inference to study cryptic HIV replication, drug efficacy, and reservoir persistence. His lab has demonstrated HIV’s ability to replicate in hidden body regions despite antiretroviral treatment, validated by collaborators at institutions like IRSI-Caixa and UCSF. He has also pioneered system identification methods for censored data, including the Tobit Kalman Filter, with applications in tracking and surveillance for the Army Research Laboratories. Research interests include information-optimal experiment design, censored-data estimation, and applying engineering principles to biomedical challenges. He collaborates with global institutions and prioritizes translating mathematical insights into actionable clinical strategies. For joining his lab, interested students and postdocs should contact him directly via ryanz@udel.edu with a CV and research interests.

Hein Duijfمشاهده پروفایل

استادیار

• Social Epistemology
• Artificial Intelligence Ethics
• Philosophy of Responsibility
• +۴ مورد دیگر

Dr. Hein Duijf is an Assistant Professor in Theoretical Philosophy at Utrecht University's Department of Philosophy and Religious Studies, within the Humanities faculty. He previously held positions at LMU Munich and VU Amsterdam. His research focuses on social epistemology, the ethics of artificial intelligence, and collective agency, addressing topics like responsibility voids, epistemic norms, and AI ethics. Education: PhD in Philosophy and Artificial Intelligence (Utrecht University, cum laude) MSc in Mathematics (Radboud University Nijmegen, cum laude) Research Interests: Hein investigates expertise dynamics, deliberation in social networks, responsible AI design, and formal models of collective responsibility. He emphasizes how institutions and technologies impact societal flourishing. Recent Activities: Invited talks on diversity in decision problems at TU Dresden, University of Luxembourg, and others Contributions to interdisciplinary robotics ethics initiatives Teaching courses on AI philosophy, social epistemology, and moral responsibility at Utrecht University Labs/Teams: Active in Utrecht's Institutions for Open Societies (IOS) research theme, exploring human-centered AI applications and ethical frameworks.

• Artificial Intelligence
• Computer Science
• Game Design
• +۳ مورد دیگر

Cristiana Pacheco is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. Her research focuses on artificial intelligence applications in video game design, particularly in modeling believable agent behavior. She holds an affiliation with the university's Peter Landin research facility and is reachable at c.pacheco@qmul.ac.uk. Her work bridges computer science and game design, emphasizing predictive models, Monte Carlo methods, and time-continuous believability assessment. Recent research explores agent behavior pipelines and co-evolution strategies in multi-player game environments. Key contributions include frameworks for evaluating agent realism and optimizing gameplay dynamics. Though no awards or grants are explicitly listed, her publications indicate sustained engagement with AI-driven gaming challenges. She has not listed formal advisees but contributes to the school's academic and research activities through visiting collaborations.

• Supply Chain Management
• Modern Slavery
• Human Trafficking Prevention
• +۲ مورد دیگر

Alexander Trautrims is Professor of Supply Chain Management at Nottingham University Business School, University of Nottingham, and Associate Director of the Rights Lab's Business and Economies Programme. His pioneering research on modern slavery in supply chains informs global policy and business practice. Research examines forced labor in medical glove production (2024), food supply chains (2023), and pandemic procurement (2021). Develops intervention frameworks through agent-based modeling (2020), policy analysis, and multi-stakeholder collaborations. Co-chairs British Standards Committee BS25700 on organizational responses to modern slavery. Advises OSCE and Council of Europe on anti-trafficking measures while creating sector-specific guidelines for construction, healthcare, and agriculture. Authored first academic article on modern slavery in supply chain management journals (2015).

• human-centered ai
• psychology of technology
• machine learning
• +۵ مورد دیگر

Chao Zhang is an Assistant Professor in Human-Centered AI at the Human-Technology Interaction group, Eindhoven University of Technology. His research bridges psychology and technology through data-driven methods and psychological theories, focusing on behavior change, AI-human dynamics, and trustworthy systems. MSc in Human-Technology Interaction (TU/e) PhD in Intelligent Systems (TU/e, 2019) Postdoc at Utrecht University Research interests span Human-Centered AI , Explainable AI , and Behavioral Data Science . Key contributions include: Integrating psychological principles with machine learning Modeling longitudinal behavioral data Developing interpretable AI systems for lifestyle interventions Investigating social characteristics in AI-driven environments Current work includes the ENFIELD project on Trustworthy Green AI. Scientific contributions appear in CHI Conference , arXiv , and International Journal of Social Robotics . Recognized with a TU/e PhD Thesis Award nomination in 2021.

Sander Kooleمشاهده پروفایل

استاد

• Emotion Regulation
• Adaptivity in Social Interactions
• Self-Regulation
• +۶ مورد دیگر

Prof. Sander Koole is a Full Professor at the Faculty of Behavioural and Movement Sciences and APH - Mental Health at Vrije Universiteit Amsterdam. He specializes in Clinical Psychology and serves as Chief Editor at Taylor & Francis since 2018. His research focuses on Emotion Regulation , Interpersonal Synchrony , and Adaptive Behavioral Systems , with over 195 publications and 15 supervised PhD theses. His work bridges clinical practice with computational models, addressing topics like mental health in pandemics and human-robot interaction. Research Interests: Emotion Regulation Dynamics Multimodal Interpersonal Synchrony Computational Models of Coregulation Psychotherapy and Mental Health Cultural and Cognitive Foundations of Behavior Awards & Grants: Best Paper Awards (2015, 2000, 1995) Consolidator Grant (2011) Academic Excellence in Psychology (1991) Advising & Grants: Supervisor of 15 PhD theses. Research funded by grants addressing emotion regulation and mental health. Active in media commentary on topics like stress, self-esteem, and societal well-being. Labs/Teams: Collaborates on projects involving computational neuroscience, clinical psychology, and interdisciplinary teams at VU Amsterdam.

Marina De Vosمشاهده پروفایل

مدرس ارشد

• Answer Set Programming
• Normative Multi-Agent Systems
• Knowledge Representation
• +۳ مورد دیگر

Dr. Marina De Vos is a Senior Lecturer in the Department of Computer Science at the University of Bath. She leads research in knowledge representation and normative systems, with affiliations to multiple research centers including the Centre for Mathematical Biology and UKRI CDT in Accountable AI. Education: Postgraduate Certificate in Learning and Teaching in Higher Education, University of Bath (2010) Doctor of Science in Computer Science, Vrije Universiteit Brussel (2002) Master of Computing, Vrije Universiteit Brussel (1998) Research focuses on declarative programming paradigms, particularly Answer Set Programming (ASP) and normative multi-agent systems. Her work enables intuitive problem description in knowledge representation languages, with applications spanning structural engineering, music composition, legal reasoning, and policy modeling. Additional research interests include inductive machine learning, explainable AI, hybrid AI systems, and game theory. Recent publications demonstrate strong focus on norm synthesis in multi-agent systems, contextual reasoning, and governance frameworks for autonomous systems. This reflects a consistent pattern of applying formal computational methods to social and ethical dimensions of AI. Professional activities include keynote presentations at international conferences, workshop chair roles for major AI events, and external PhD examination responsibilities across European institutions.

Hadi Khorshidiمشاهده پروفایل

پژوهشگر ارشد

• Medical Data Mining
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dr. Hadi Khorshidi is a Senior Research Fellow at the Cancer Health Services Research Unit within the Melbourne School of Population and Global Health at the University of Melbourne. He joined the CHSR Group in September 2021. Previously, he held roles as a Senior Data Analyst at the Institute for Safety, Compensation and Recovery Research (ISCRR) and as a Research Fellow in the School of Computing and Information Systems. His research focuses on medical data mining, optimization, machine learning, and uncertainty quantification applied to healthcare challenges, particularly in cancer research and injury outcomes. Dr. Khorshidi earned his PhD in Applied and Computational Mathematics from Monash University in 2016, with a thesis titled “System Reliability Optimisation via Uncertainty Quantification.” He holds prior academic and professional qualifications in related fields, including a Master’s and Bachelor’s degrees (specific details not provided in the text). His research interests span interdisciplinary domains, including medical data mining , optimization techniques , machine learning applications , and uncertainty quantification , with a focus on healthcare systems and cancer research. He has contributed to projects involving agent-based modeling for disease diagnosis, system dynamics modeling for genomic sequencing implementation, and improving decision-making through AI and collaborative human-machine approaches. Dr. Khorshidi has secured notable grants and awards, including the O&G Innovation Grant and a joint research grant through the Manchester-Melbourne Research Fund. These support his work on integrating advanced technologies into healthcare decision-making and cancer management strategies. In addition to his research, he serves as a Chief Investigator in the Manchester-Melbourne Research Fund project. His collaborative efforts include editorial roles in journals such as the International Journal of System Assurance Engineering and Management , and contributions to program committees for IEEE conferences. He is affiliated with the Cancer Health Services Research Unit and collaborates with the CHSR Group, contributing to interdisciplinary teams focused on advancing healthcare solutions through data-driven methodologies.

Sam Nallaperuma-Herzbergمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Optical Communication Systems
• +۵ مورد دیگر

Dr. Sam Nallaperuma-Herzberg is an Assistant Research Professor at the University of Cambridge's Department of Computer Science and Technology. He holds prestigious fellowships including the Borysiewicz Interdisciplinary Fellowship, Bye Fellowship at Murray Edwards College, and the Schmidt/Accelerate Fellowship in AI for Science and Society. His research focuses on machine learning applications in optical communication systems, neural networks for medical diagnostics, and reinforcement learning for network optimization. Affiliations: Department of Computer Science and Technology, Murray Edwards College Key Research Themes: Machine Learning, AI for Healthcare, Optical Fiber Networks, Reinforcement Learning Algorithms His work bridges computational methods with real-world challenges in telecommunications and healthcare. Notable achievements include developing graph neural networks for insomnia modeling and optimizing ultra-wideband fiber-optic systems using deep learning. He has been recognized with multiple interdisciplinary research fellowships, reflecting his cross-cutting contributions. Research outputs span over 30 peer-reviewed articles since 2017, with recent emphasis on AI-driven solutions for optical network optimization and circRNA-RBP interaction prediction. His projects include the BrainTwin initiative applying AI to sleep disorder analysis. Awards: Borysiewicz Interdisciplinary Fellow Bye Fellow, Murray Edwards College Schmidt/Accelerate Fellow in AI for Science and Society Current research explores hybrid neural architectures for medical diagnostics and Gaussian process modeling for optical signal optimization. He collaborates extensively on EU-funded initiatives and industry partnerships in AI and telecommunications.