جست‌وجوی استاد و پژوهشگر

• Digital Technology Innovation Management
• Artificial Intelligence
• Blockchain
• +۴ مورد دیگر

Dr. Senmao Xia is a Senior Lecturer in Digital Economy, Entrepreneurship and Innovation at the University of Surrey, where he serves as Doctoral Supervisor, Associate Director of the Surrey Academy for Blockchain and Metaverse Applications, and Programme Director for Blockchain and Metaverse Business Models. He holds adjunct roles as Visiting Associate Professor at Peking University HSBC Business School and Adjunct Research Fellow at Central University of Finance and Economics. His PhD and SFHEA status underscore his academic credentials. His research bridges digital innovation (AI, blockchain), entrepreneurship, and sustainability, with a focus on: Technological impacts on supply chains and operations Consumer behavior in green economies Human-AI collaboration in service recovery Recent publications emphasize empirical and theoretical advances in digitalization, circular economies, and innovation strategy, reflecting interdisciplinary engagement across management, economics, and environmental studies. Awards & Leadership: Senior Fellow of Higher Education Academy (SFHEA) Vice President, Chinese Economic Association (Europe/UK) Editorial roles: Journal of Innovation and Knowledge (Associate Editor), Technovation (Guest Editor), and others Research Supervision & Industry Engagement: Actively supervises PhD students (current: 2 candidates, 2 visitors; completed: 3). Advises firms on blockchain/AI applications (e.g., collaboration with Fotor for digital photo creation) and hosts international conferences (e.g., Sino-UK Forum).

• Statistical Modelling
• Epidemiology
• Bayesian Inference
• +۴ مورد دیگر

Dr Ben Swallow is a Lecturer in Statistics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. His research focuses on statistical methodology applied to epidemiological and ecological problems, with a particular emphasis on Bayesian inference, agent-based models, and public health surveillance. He has contributed to high-impact studies on pandemic response, vaccine efficacy analysis, and wildlife population dynamics. Dr Swallow has supervised PhD students Xiahui Li and Rebecca Supple. His work bridges theoretical statistics with practical applications, including modeling virus transmission dynamics, analyzing ecological data from citizen science projects, and developing computational tools for epidemiological research. Key contributions include advancements in agent-based model calibration, statistical methods for evaluating vaccine safety, and Bayesian approaches to infectious disease modeling. His research outputs span journals such as Journal of Theoretical Biology , BMC Biology , and Journal of the Royal Statistical Society . He actively participates in academic discussions, including contributing to the 'Discussion Meeting on Analysis of citizen science data'.

• Asylum Policies
• Border Studies
• Forced Migration
• +۳ مورد دیگر

Martin Lemberg-Pedersen is an Honorary Associate Professor at the University of Warwick's School of Politics and International Studies (PAIS), joining in 2021. He currently serves as Head of Policy and Society for Amnesty International Denmark. Previously, he held roles as Associate Professor at the University of Copenhagen's Centre for Advanced Migration Studies and Assistant Professor at Aalborg University. His research focuses on Western asylum, border, and deportation practices, with emphases on EU externalization policies in Mediterranean contexts and postcolonial trajectories in forced migration studies. Lemberg-Pedersen’s academic career includes a PhD and Post Doc from the University of Copenhagen, followed by teaching roles across institutions. His research has been funded by grants like the Carlsberg Foundation’s 'Between Asylum and Security' project (2013-2016) and IRC Denmark’s postcolonial displacement project (2017-2020). He has published widely in journals like Global Affairs , Citizenship Studies , and Forced Migration Review . His work critiques EU border externalization, linking it to historical colonial legacies and examining its humanitarian consequences. Notable projects include the Advancing Alternative Migration Governance (AdMiGov) initiative under Horizon 2020. He has also explored Denmark’s externalization plans to Rwanda and Africa, emphasizing ethical and geopolitical dimensions. Lemberg-Pedersen supervises PhD students and has coordinated courses on International Political Economy, Refugee Studies, and Normative Philosophy. Beyond academia, he guides tours in Copenhagen contextualizing the city’s colonial history. His interdisciplinary approach integrates political science, postcolonial theory, and migration studies to challenge exclusionary policies.

• Computer Vision
• Cognitive Robotics
• Perception
• +۶ مورد دیگر

Fiora Pirri is a Full Professor at Sapienza University of Rome, leading the ALCOR Laboratory since 1998. She holds a PhD from Université Pierre et Marie Curie Paris VI, a DEA in Artificial Intelligence (ENPC), and dual Master's degrees in Statistics and Architecture from Sapienza. Her research focuses on Computer Vision, Cognitive Robotics, Perception, and Machine Learning, with applications in 3D reconstruction, Green AI (e.g., vertical farming), multimedia forensics, and edge vision. Current projects include Horizon2020 initiatives like Second HAND and FP7/TRADR in disaster response robotics. **Education:** PhD: Université Pierre et Marie Curie Paris VI DEA IARFAG: ENPC (Ecole Nationale des Ponts et Chaussées) Master's in Statistics: Sapienza University of Rome Master's in Architecture: Sapienza University of Rome **Research Interests:** Her work bridges visual perception and cognitive systems, emphasizing robust solutions for unstructured environments. Key areas include semantic segmentation, robotic action anticipation, SAR-optical data fusion, and plant phenotyping in controlled agriculture. ALCOR Lab's projects address challenges in urban search and rescue, environmental monitoring, and human-robot collaboration. **Grants & Projects:** Leads EU-funded initiatives such as Second HAND (2024), TRADR (FP7), and NIFTI (Cognitive Systems & Robotics). Collaborates on SARFIRE (ASI Contract) for fire susceptibility modeling. Recent publications highlight advancements in unsupervised segmentation, spatiotemporal fusion, and low-resolution action recognition. **Lab & Infrastructure:** ALCOR Lab (Rooms S012/S011, Via Ariosto 25) focuses on perception-driven robotics, with active research in XAI, adversarial learning, and edge computing. The lab integrates theoretical and applied research to advance autonomous systems in dynamic environments.

Irene Ameriniمشاهده پروفایل

دانشیار

• Multimedia Forensics
• Deepfake Analysis
• Machine Learning
• +۵ مورد دیگر

Irene Amerini is an Associate Professor at the Department of Computer, Control and Management Engineering A. Ruberti, Sapienza University of Rome. She leads the Computer Vision and Multimedia Forensics Research Team at ALCORLab. Her research focuses on multimedia forensics, deepfake analysis, machine learning, adversarial machine learning, and computer vision. She has organized workshops like WiCV at CVPR 2019 and ECCV 2020, and serves on committees including IEEE Forensics and Security Technical Committee. Education: Ph.D. in Computer Engineering, Multimedia and Telecommunication (2011), University of Florence Postdoctoral Researcher at Image Forensics and Security Lab, University of Florence (2019–2022) Visiting Research Fellowship at Charles Sturt University (2018) Research Interests: Deepfake detection through RGB-depth integration and neural network fusion Adversarial robustness in AI models Monocular depth estimation for real-time applications Forensic analysis of synthetic media Awards: Endeavour Research Fellowship (2018) Best Paper Award at eForensics 2009 Highly Cited Paper (2017–2019) for copy-move attack detection work Best Paper Award SUMAC '23 (2023) Advising & Grants: She advises on AI ethics in media forensics and collaborates on projects involving secure AI systems. Her grants include Australian Government funding through the Endeavour program and EU initiatives. Labs & Teams: Leads the ALCORLab's Computer Vision and Multimedia Forensics Research Team, focusing on AI-driven solutions for digital forensics and security.

Birgit Kleinschmitمشاهده پروفایل

استاد

• Remote Sensing
• Climate Change
• Environmental Science
• +۴ مورد دیگر

Prof. Birgit Kleinschmit is a Professor and Head of the Department of Geoinformation in Environmental Planning at Technische Universität Berlin. Her research focuses on remote sensing applications for analyzing human-environment systems, particularly in the context of climate and land-use changes. She leads projects on spatio-temporal modeling of landscapes, drought impacts, and wildfire dynamics using AI and satellite data. Member of the Scientific Advisory Board for Forest Policy (Bundesministerium) Co-Speaker of the DFG Urban Water Interfaces Research Group Recipient of the 2024 '100 Most Influential Minds in Berlin Science' award Her work integrates multisensor data (e.g., Sentinel-2, SAR) with machine learning to assess forest health, wildfire susceptibility, and urban evapotranspiration. Recent studies include drought responses in Central Europe, wildfire driver analysis, and sustainable land-use strategies. Publications emphasize environmental monitoring innovations, with over 20 peer-reviewed articles since 2022. Active in policy advising through Geo.X Research Network and federal committees.

Alby Duarte Rochaمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Machine Learning
• Environmental Planning
• +۳ مورد دیگر

Dr. Alby Duarte Rocha is a Researcher at the Technical University of Berlin's Department of Geoinformation Processing for Landscape and Environmental Planning. His work focuses on remote sensing applications, machine learning, and environmental modeling. He holds a Doctorate in Remote Sensing from the University of Twente (2019), a Master in Geodesy from Federal University of Paraná (2007), and a Bachelor in Statistics (1998). Research interests include predicting plant traits using hyperspectral data, spatiotemporal models for vegetation parameters, and urban evapotranspiration modeling. His studies address climate change impacts, urban heat mitigation, and environmental justice through interdisciplinary approaches integrating AI and remote sensing. Key publications (2021–2025) highlight contributions to soil moisture modeling, wildfire analysis, and urban cooling disparities using satellite and machine learning techniques. His work supports sustainable urban planning and ecosystem monitoring.

• Environmental statistics
• Bayesian statistics
• Spatial statistics
• +۳ مورد دیگر

Andrew O. Finley is a Professor at Michigan State University with joint appointments in Forestry and Statistics & Probability. He leads the Geospatial Lab, focusing on environmental statistics, Bayesian modeling, and spatial analysis. His research integrates hierarchical models to enhance ecological and forestry data analysis, with applications in forest inventory, carbon monitoring, and biodiversity assessment. Education includes a PhD from the University of Minnesota (2007), MS in Statistics (2007) and Forestry (2003), and a BS from Penn State (2000). He has held NSF, NASA, and USDA grants totaling over $10M, advancing carbon monitoring and statistical methodologies. Research interests span Bayesian statistics, spatial-temporal modeling, and computational tools for large datasets. Notable contributions include the spBayes and spNNGP R packages, widely used for spatial analysis. His work has been recognized with ASA awards and fellowships. Recent projects include assessing western US tree species decline, developing scalable carbon monitoring frameworks, and integrating remote sensing with field data. He advises over 20 graduate students and collaborates globally on ecological and environmental challenges.

Shanshan Zhaoمشاهده پروفایل

دانشیار مدعو

• Biostatistics
• Environmental Health
• Cancer Research
• +۷ مورد دیگر

Dr. Shanshan Zhao is an Adjunct Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill and a Principal Investigator at the National Institute of Environmental Health Sciences (NIEHS). Her research focuses on developing statistical methods to understand environmental and social influences on health, particularly in cancer epidemiology and risk prediction. She holds a secondary appointment in the NIEHS Epidemiology Branch and leads a group integrating statistical methodology with population-based studies. Her work emphasizes disease risk assessment using survival analysis, longitudinal data, and biomarker evaluation. Key research areas include multivariate failure time data, environmental mixture analysis, and epigenetic biomarkers. She has contributed to studies on breast, prostate, and colorectal cancers, emphasizing spatial-temporal modeling and public health applications. Zhao’s awards include the 2014 ASA David P. Byar Travel Award and recognition for her 2023 NIEHS publication on environmental breast cancer risk factors. Her team develops software tools like mhazard for multivariate survival analysis and STAFT for spatio-temporal modeling. Her lab collaborates across disciplines to advance environmental health sciences, with a focus on translating statistical innovations into actionable insights for clinicians and policymakers.

Manuel Güntherمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Open-Set Recognition
• +۴ مورد دیگر

Prof. Dr. Manuel Günther is an Assistant Professor of Artificial Intelligence and Machine Learning at the Department of Informatics, University of Zurich (since 2020). His research focuses on open-set recognition, adversarial machine learning, reproducible research, and biometric systems. He holds a diploma (2004) and doctoral degree (2012) from the Technical University of Ilmenau, Germany, with a thesis on statistical extensions of Gabor graph-based face analysis. Educational Background: PhD: Technical University of Ilmenau (2004–2012) Diploma in Computer Science: Technical University of Ilmenau (2004) Research Interests: Günther’s work addresses challenges in open-world learning, such as handling unknown classes in face recognition, adversarial robustness of deep networks, and reproducible research practices. He leads development of the Bob machine learning library and pioneered the Open-Set Face Recognition Challenge . Labs & Affiliations: Previously at the University of Colorado Colorado Springs (2015–2018) and Idiap Research Institute (2012–2015). Current research integrates interpretability in medical imaging (e.g., tuberculosis detection) and environmental applications (forest drought prediction).

Elisabeth Addinkمشاهده پروفایل

دانشیار

• Remote Sensing
• Hydrology
• Geo-computation
• +۳ مورد دیگر

Dr. Elisabeth Addink is an Associate Professor in Physical Geography at Utrecht University's Faculty of Geosciences. Her research focuses on remote sensing applications for vegetation and ecosystem monitoring, particularly in dynamic environments like tidal flats, rivers, and agricultural landscapes. She integrates techniques such as object-based image analysis (GEOBIA), deep learning, and UAV-based sensing to study landscape functioning, geo-computation, and hydrological processes. Dr. Addink's work addresses challenges in land degradation, environmental sustainability, and climate resilience, with projects spanning Europe, Africa, and Asia. Her research interests include the use of geospatial technologies to map ecological variables, assess irrigation water consumption, and model channel networks in estuaries and braided rivers. She has contributed to initiatives like the 'Future Delta' program and collaborates on projects analyzing subsidence in the Mekong Delta and plague host distribution in Kazakhstan. Dr. Addink is actively involved in academic activities, including editorial roles for remote sensing journals and organizing international conferences like GEOBIA. Dr. Addink's publications reflect a focus on advancing methodologies for environmental monitoring, with recent work emphasizing deep learning-enhanced remote sensing and the application of UAV technology for vegetation and floodplain analysis. She teaches courses in earth sciences and geospatial techniques, and her lab is part of Utrecht's Pathways to Sustainability research theme.

• Machine Learning
• Computational Biology
• BioImage Informatics
• +۳ مورد دیگر

Gisele Barboni Miranda is an Assistant Professor in Machine Learning for Computational Biology at KTH Royal Institute of Technology and a SciLifeLab Fellow. She holds a joint Ph.D. in Computer Science and Applied Mathematics from the University of São Paulo (Brazil) and Applied Biological Sciences from Ghent University (Belgium). Her research focuses on integrating multi-modal data (e.g., microscopy images, omics data) to model cellular behavior and improve disease diagnosis/treatment. She leads projects in the BioImage Informatics Facility and the Imaging Program of the Chan-Zuckerberg Initiative. Research interests include generative AI, deep learning, graph machine learning, and multi-modal profiling for biological systems. She teaches courses such as Statistical Methods in Applied Computer Science and supervises degree projects in Computer Science and Engineering. Scientific contributions span epidemiological modeling (e.g., influenza, SARS-CoV-2), environmental engineering applications of ML, and computational linguistics. Awards include the SciLifeLab Fellowship. Active in collaborative initiatives like the Chan-Zuckerberg Imaging Program.

Alan Woodleyمشاهده پروفایل

مدرس ارشد

• Data Science
• Environmental Management
• Computational Modeling
• +۳ مورد دیگر

Dr. Alan Woodley is a Senior Lecturer in the School of Computer Science at Queensland University of Technology (QUT) and leads the Spatial Informatics Laboratory. He holds a PhD in Computer Science from QUT and a Certificate in New Ventures Leadership from MIT. His roles include Associate Investigator at the Centre for Agriculture and the Bioeconomy, Australian Data Science Centre, and ARC Centre of Excellence for Mathematical & Statistical Frontiers. Education: PhD (2008), B.IT (Hons) (2003), B.IT (2003) from QUT; MIT Innovation Bootcamp (2018). Research focuses on applying big data solutions to environmental and mining domains, emphasizing data analytics, computational modeling, and visualization. Key areas include industrial sustainability, social-technical integration, and flood monitoring using remote sensing and social media. His work has attracted over $4M in funding and produced over 70 publications. Recent articles highlight innovations like flood mapping via spectral indices, social media-based disaster detection, and parallel clustering algorithms. Notable grants include projects with the US Department of Energy, Food Agility CRC, and CRC for Spatial Information. Awards: Vice Chancellor's Research Fellowship (2018), Bonneville Power Administration grants for salmon monitoring and fish density tools, and Food Agility CRC funding for circular economy frameworks. Teaching: Courses include Programming Principles, Web Development, and Large Scale Data Mining (encompassing 100–600 students annually). Collaborations: Partnerships with Lendlease, Bonneville Power Association, Queensland Government, and mining industry representatives to develop tools like community compost apps and land change systems. His Spatial Informatics Lab drives interdisciplinary projects in environmental informatics and geospatial data science.

Stan Hurnمشاهده پروفایل

استاد

• Time-Series Econometrics
• Econometrics
• Finance
• +۳ مورد دیگر

Stan Hurn is a Professor of Econometrics at the School of Economics and Finance, QUT Business School, Queensland University of Technology. He holds a PhD from the University of Oxford and has been at QUT since 1998. His research focuses on time-series econometrics, finance, and energy economics. Notable contributions include work on Granger causality, electricity demand forecasting, and stochastic volatility models. He has published extensively in top-tier journals such as the Journal of Econometrics and Energy Economics. Education: PhD in Economics, University of Oxford (1992) D.Phil. in Economics, University of Oxford (1992) Research Interests: Time-series econometrics, financial econometrics, energy economics, and stochastic processes. His research investigates topics such as the dynamics of industrial production, causal relationships in economic systems, and the application of econometric models to energy markets. He has also contributed to methodological advancements in likelihood approximations for diffusions and volatility modeling. Scientific awards and fellowships are not explicitly mentioned in the provided texts. His work has been widely cited, particularly in areas related to econometric modeling and energy market analysis. Professor Hurn has supervised doctoral students but specific names are not listed here. His grants and collaborations involve projects on financial econometrics and energy systems.

Xiaowei Jiaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the School of Computing and Information. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal challenges in environmental science, hydrology, and climate modeling. Key research interests include physics-guided machine learning, spatio-temporal data mining, and fairness-aware models. He has developed frameworks like Physics-Guided Neural Networks (PGNN) and Spatial-Net for heterogeneous data, addressing issues in stream temperature prediction, crop yield modeling, and climate change projections. His work has been recognized with awards including the Best Dissertation Award (Minnesota) and multiple Best Paper awards in top conferences. Dr. Jia teaches advanced machine learning courses, emphasizing knowledge integration into AI systems. His lab collaborates extensively with domain scientists to advance applications in agriculture, hydrology, and environmental monitoring. Ongoing projects include high-resolution methane emission datasets (X-MethaneWet) and fairness-aware learning for resource distribution.