جست‌وجوی استاد و پژوهشگر

Martin Jakobssonمشاهده پروفایل

استاد

• Marine Geology
• Arctic Ocean Glacial History
• Paleoceanography
• +۴ مورد دیگر

Martin Jakobsson is a Professor of Marine Geology and Geophysics at Stockholm University's Department of Geological Sciences. His career spans international research expeditions and leadership roles, including Dean of Earth Science (2021) and Head of Department (2012-2018). A key member of the Marine Geology group, he studies Arctic and Antarctic glacial history through sediment analysis and seafloor mapping. PhD from Stockholm University (2000) Academy Fellow, Royal Swedish Academy of Sciences (2004-2009) Professor II position at University Centre of Svalbard (2011-) His research focuses on marine geological archives to reconstruct glacial cycles, ice-ocean interactions, and bathymetric mapping. As GEBCO Vice-Chair (2013-2020), he led initiatives for global seabed mapping through the Seabed 2030 project. Recent publications reveal expertise in: Arctic Ocean paleocirculation Methane release from thawing permafrost Ice sheet sensitivity to oceanic warming High-resolution bathymetric analysis Scientific recognition includes: Election to Royal Swedish Academy of Sciences (2012) 1st Vice President, Royal Swedish Academy of Sciences (2016-2019) Membership in Norwegian Scientific Academy for Polar Research (2016) He leads international collaborations on polar research vessels, with over a year of cumulative ship time and multiple Co-Chief Scientist roles on Arctic expeditions.

• Intellectual Property Law
• Legal History
• Copyright Law
• +۳ مورد دیگر

Professor Isabella Alexander is a distinguished legal historian and intellectual property scholar at the University of Technology Sydney (UTS), Faculty of Law. She specializes in copyright law with a particular focus on its historical dimensions and application to cartography and visual works. As a leading researcher in her field, she holds significant research grants including ARC Discovery projects on copyright law in the 21st century and copyright in cartography. Her educational background includes: PhD from the University of Cambridge LLB (Hons) from the Australian National University BA (Hons) from the Australian National University Professor Alexander's research explores how laws inform and regulate the creation, circulation and reception of knowledge and culture from a historical perspective. Her work bridges legal history with contemporary intellectual property challenges, particularly in areas of copyright for visual works, cartography, and digital creativity. She has made significant contributions to understanding the historical development of copyright principles and their application to modern technological contexts. An analysis of her recent publications reveals a strong focus on the intersection of copyright law with visual culture, cartography, and emerging digital challenges including AI-generated content and memes. Her work demonstrates a clear trajectory toward interdisciplinary approaches that connect historical legal frameworks with contemporary technological and cultural phenomena, showing increasing engagement with how historical understanding can inform solutions to modern intellectual property challenges. Her notable scientific achievements include: 2023 LSAANZ Award for published scholarly book/monograph for "Copyright and Cartography: History, Law, and the Circulation of Geographical Knowledge" Professor Alexander has secured significant research funding as Lead Chief Investigator on the ARC Discovery grant "Hacking Copyright Law in the 21st Century: Art, Law, History and Technology" and as sole Chief Investigator on "Copyright and Cartography: Understanding the past, shaping the future." She has actively contributed to policy development through submissions to the Australian Productivity Commission's Inquiry into IP Arrangements and on addressing copyright infringement online through secondary liability and website blocking mechanisms. She currently serves as President of the Australian and New Zealand Law and History Society and is a member of the Governing Board of the International Society for the History and Theory of Intellectual Property, demonstrating her leadership and influence in both national and international academic communities.

Nikos Tzavidisمشاهده پروفایل

استاد

• Small Area Estimation
• Official Statistics
• Outlier Robust Inference
• +۷ مورد دیگر

Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.

Andrew Hudson-Smithمشاهده پروفایل

استاد

• Digital Urban Systems
• Smart Cities
• Internet of Things (IoT)
• +۷ مورد دیگر

Professor Andrew Hudson-Smith is the Director of the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London (UCL), where he holds the title of Professor of Digital Urban Systems. He leads UCL's academic and applied research in smart cities, digital twins, and urban informatics. His roles include Editor-in-Chief of the Future Internet Journal and academic lead for the Smart Queen Elizabeth Park project. He contributes to strategic initiatives such as the Greater London Authority Smart London Board and EPSRC Digital Economy Programme Advisory Board. Education: PhD (2003), MSc (1996) from UCL; BSc in Geography from the University of Plymouth (1992). Research focuses on digital urban systems, IoT applications, sustainable urban planning, and cyberphysical systems. Notable projects include urban heat island monitoring, participatory planning platforms, and metaverse-driven urban design. His work bridges technology, art, and public engagement, exemplified by initiatives like Tales of Things and the Haggle-O-Tron. Recent publications emphasize digital twins, metaverse urbanism, and IoT integration in urban environments. Awards include a Fellowship of the Royal Society of Arts. His work addresses SDGs 3 (Good Health) and 11 (Sustainable Cities), with a focus on equitable smart city development and public space innovation.

John Paisleyمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Statistics
• Data Science
• +۳ مورد دیگر

John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.

Sanjay Purushothamمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Optimization Theory
• +۵ مورد دیگر

Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.

Yang Zhouمشاهده پروفایل

دانشیار

• Big Data Algorithms
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.

Xiaoxiang Zhuمشاهده پروفایل

استاد

• Remote sensing and Earth observation
• Signal processing
• Artificial intelligence and machine learning
• +۳ مورد دیگر

Xiaoxiang Zhu is a Professor for Data Science in Earth Observation at the Technical University of Munich (TUM) and serves as the Director of the International AI Future Lab - AI4EO. She is also on the Board of Directors of the Munich Data Science Institute (MDSI) and has held various leadership positions in research institutions including as Spokesperson for Helmholtz AI Research Field "Aeronautics, Space and Transport" (MASTr). Her educational background includes a doctorate (Dr.-Ing.) and habilitation from TUM. She has held positions as Private Dozent at TUM (2013-2015), TUM Junior Fellow (2013-2015), and Research Group Leader for "SparsEO" at Munich Aerospace (2011-2016). Professor Zhu's research focuses on the intersection of remote sensing, artificial intelligence, and data science. Her work primarily addresses global urban mapping, sustainable development goals, and climate change monitoring through Earth observation technologies. She develops advanced signal processing techniques and machine learning algorithms specifically tailored for satellite imagery and geospatial data analysis. Her research has significant applications in urban planning, environmental monitoring, and disaster management. Her team has pioneered approaches that combine synthetic aperture radar (SAR) with deep learning for improved Earth observation capabilities. Professor Zhu has received numerous prestigious awards including being named an IEEE Fellow (2021), receiving the Geodesy Award of the Nico Rüpke Foundation (2020), and being awarded an ERC Proof of Concept Grant (2020, 2022). She is also a Fellow of the Academia Europaea (2024) and AAIA Fellow (2024). Her publication record includes highly cited works such as "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources" (2017). She leads a substantial research team comprising numerous PhD students and postdoctoral researchers working on various projects including Horizon Europe - ThinkingEarth, EarthCare, and AI4TWINNING. Her research group, the Chair of Data Science in Earth Observation, is actively involved in multiple large-scale European and German research initiatives focused on Earth observation and AI applications.

Katja Hoseمشاهده پروفایل

استاد

• Knowledge Graphs
• Semantic Web
• Data Management
• +۷ مورد دیگر

Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.

Clio Andrisمشاهده پروفایل

دانشیار

• Information Visualization
• Geovisualization
• GIS
• +۴ مورد دیگر

Clio Andris is an Associate Professor at Georgia Tech, jointly appointed in the School of City and Regional Planning and the School of Interactive Computing. She directs the Friendly Cities Lab, focusing on mathematical models of social networks applied to urban planning, transportation, and geography. Her work integrates spatial analysis with visualization, emphasizing interdisciplinary collaboration. Education and Career: Andris earned a PhD in Urban Information Systems from MIT (2011), where she was an NDSEG Fellow. She held postdoctoral positions at Singapore-MIT Alliance for Research and Technology and the Santa Fe Institute. Prior to Georgia Tech, she was a faculty member at Penn State’s Department of Geography, affiliated with the GeoVISTA Center. Research Focus: Her research bridges social networks, geovisualization, and urban informatics. Key areas include spatial social network analysis, GIS applications for urban policy, and the impact of digital tools on civic engagement. She has developed innovative visual analytics tools like SNoMaN and ROBIN to democratize spatial data exploration. Awards: She received the NSF CAREER Award (2021) and NDSEG Fellowship (2011). Her lab is affiliated with the Center for Spatial Planning Analytics and Visualization (CSPAV) and the Information Visualization Lab. Labs and Collaborations: The Friendly Cities Lab focuses on socially just urban design through computational methods. Her work addresses issues like food security networks, pandemic impacts on biodiversity, and community mapping for activism. Grants and Outreach: Her NSF-funded projects emphasize public good applications, such as real-time pandemic risk communication and educational tools for migration data. She actively collaborates with non-profits and policymakers to translate research into actionable urban strategies.

Emily Soمشاهده پروفایل

استاد

• Disaster Risk Reduction
• Earthquake Engineering
• Urban Resilience
• +۵ مورد دیگر

Professor Emily So serves as Deputy Head of the School of Arts and Humanities at the University of Cambridge and directs the Cambridge University Centre for Risk in the Built Environment (CURBE). A chartered civil engineer with extensive field experience, she holds leadership roles in the Open-Oxford-Cambridge AHRC Doctoral Training Partnership and chairs the Faculty EDI Committee. Her research focuses on urban risk and resilience , particularly in earthquake-prone regions. Combining structural engineering with epidemiological approaches, she develops innovative casualty estimation models and engages directly with affected communities worldwide. Her work spans seismic safety, disaster epidemiology, and remote sensing applications for rapid damage assessment. Professor So's publication trends reveal strong emphasis on machine learning for disaster risk modeling , with recent work featuring graph neural networks, deep clustering for urban morphology, and LSTM-based population forecasting. Her research bridges engineering, social sciences, and data science to address resilience in developing nations. 2010 Shah Family Innovation Prize (Earthquake Engineering Research Institute) Fellow of the Institution of Civil Engineers (FICE) Scientific Advisory Group for Emergencies (SAGE) member advising UK government As Director of CURBE, she leads interdisciplinary collaborations with EEFIT, Global Earthquake Model (GEM), World Bank, and USGS. Her field investigations following major earthquakes inform practical solutions for vulnerable communities, notably contributing to the 2017 World Building of the Year design in China. Current work includes sabbatical research for 2025-2026 focused on decolonizing architectural approaches to disaster resilience. Professor So maintains active roles in professional organizations and international disaster response frameworks, with her CURBE team developing methodologies now implemented globally for seismic safety improvements.

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +۱۳ مورد دیگر

Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

Richard E. Turnerمشاهده پروفایل

استاد

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +۴ مورد دیگر

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

• Glaciology
• Remote Sensing
• Geodesy
• +۳ مورد دیگر

Tobias Bolch is a full Professor at the Institute of Geodesy, Graz University of Technology (TU Graz), Austria, specializing in cryospheric research with a focus on High Mountain Asia and the Tibetan Plateau. His work integrates advanced geodetic and remote sensing methodologies to address critical climate change impacts on glaciers and related systems. His research spans: Glacier dynamics and mass balance monitoring using InSAR and optical satellite data Rock glacier kinematics and permafrost interactions Glacial lake evolution and outburst flood hazards Climate-glacier-hydrology linkages in Central Asian river basins Long-term cryosphere reconstruction using historical satellite archives Analysis of his 2023-2025 publications reveals dominant trends in quantifying ice loss acceleration, particularly from lake-terminating glaciers, and the application of multi-sensor approaches (Sentinel, ASTER, UAV) for decadal-scale change detection. His work consistently emphasizes the hydrological consequences of glacier retreat for downstream water security across High Mountain Asia. No scientific awards or specific grant programs were documented in the provided materials. While student supervision details are absent, his position at TU Graz's Institute of Geodesy indicates active mentorship within geospatial and cryospheric research programs. His homepage (https://ifg.tugraz.at) serves as the primary hub for research dissemination through the university's PURE portal.

Kåre Synnesمشاهده پروفایل

استاد

• Pervasive Computing
• Mobile Computing
• Distributed Systems
• +۳ مورد دیگر

Kåre Synnes is a Professor in Pervasive and Mobile Computing at Luleå University of Technology, where he is affiliated with the Department of Computer Science, Electrical and Space Engineering. He is an active researcher and educator, recognized as an "Excellent lärare" (Excellent Teacher) and maintains his office in Luleå at location A3305a. Professor Synnes conducts research in pervasive computing and multimedia communication, with significant applications in eHealth. His work bridges theoretical computer science with practical implementations in distributed systems. He is also an affiliated researcher at the Centre for Distance-spanning Technology (CDT), focusing on technologies that enable communication across geographical boundaries. His recent publications demonstrate a clear trajectory toward sustainable computing solutions, with emphasis on circular economy applications, supply chain management, and environmental impact assessment. His research integrates computer science with economic and sustainability principles, showing how digital technologies can address contemporary environmental challenges through innovative auction systems, behavior analytics, and resource management frameworks. IST Prize winner as co-founder of Marratech AB Synnes has been deeply involved in European research projects since 1995, including multiple Framework Programme projects (FP4 through FP7). His work as an expert/reviewer for European projects since 2009 demonstrates his standing in the research community. His entrepreneurial success with Marratech AB, which was acquired by Google to form the basis for Google+ Hangouts, shows his ability to translate research into impactful commercial applications. Professor Synnes maintains strong connections with industry through his European project work and his entrepreneurial experience. His research group likely focuses on pervasive computing applications with real-world implementations, particularly in eHealth contexts, though specific lab details aren't provided in the available information.