جست‌وجوی استاد و پژوهشگر

Kathy Fontaineمشاهده پروفایل

مدرس ارشد

• Data Policy
• Ethics in Data Science
• International Data Sharing
• +۳ مورد دیگر

Kathy Fontaine serves as Senior Lecturer in Information Technology and Web Science at Rensselaer Polytechnic Institute and Program Manager for the RPI-IBM AI Research Collaboration. She joined RPI in 2014 after 25 years at NASA Goddard Space Flight Center where she developed international data access policies through CEOS WGISS, GEO, and USGEO. Her educational background includes: B.S. in Physics with Astrophysics Option from New Mexico Institute of Mining and Technology (1984) M.A. in Science, Technology and Public Policy from The George Washington University (2002) Ph.D. in Public Policy and Public Administration from Walden University (2013) Dr. Fontaine's research integrates data science policy with ethical frameworks, focusing on international data sharing cultures and volunteer organization dynamics. She develops courses like Big Data Policy and Ethical Informatics that address data scientists' societal responsibilities. Her work examines how policy implementations affect global earth observation systems and research data ecosystems, with particular attention to cross-cultural collaboration challenges in scientific consortia. Analysis of her publications reveals strong interdisciplinary connections between earth sciences and computer science, with emerging trends in data dexterity training, knowledge graph applications for social equity, and mineral inventory data legacies. Her research increasingly bridges technical data infrastructure with ethical considerations in data sharing. Dr. Fontaine actively contributes to scientific communities through the Earth Science Information Partners (ESIP), where she serves on GEO's Programme Board, and maintains affiliations with AGU, ACM Web Science, IEEE GRSS, and IEEE SSIT. Her current leadership in the RPI-IBM AI Research Collaboration extends her mission to develop responsible AI frameworks.

Thomas E. Bittnerمشاهده پروفایل

دانشیار

• Formal Ontology
• Applied Ontology
• Ontology of Space and Time
• +۳ مورد دیگر

Thomas E. Bittner serves as an Associate Professor in the Department of Philosophy at the University at Buffalo, specializing in formal and applied ontology with particular focus on spatial and temporal reasoning. His academic work bridges philosophy, computer science, and geographic information systems. His research interests encompass Formal and Applied Ontology , Ontology of Space, Time and Spatial Entities , Spatio-Temporal Reasoning , and Bio-Medical Ontology . He investigates fundamental questions about spatial relations, vagueness in geographic boundaries, and quantum-inspired models for geographic information systems, developing rigorous formal frameworks applicable to both philosophical and computational domains. His publication record demonstrates consistent engagement with cutting-edge topics in ontology, particularly exploring quantum geography concepts and computational implementations of formal ontologies. Key themes across his work include the representation of spatial vagueness, quantum-inspired geographic modeling, and verifiable computational ontology systems. Bittner holds a PhD from the Technical University of Vienna and teaches courses including Logic and Spatial Ontology. His contact information lists office location at 109 Park Hall on UB's North Campus with phone (716) 645-5149.

• Machine Learning
• Pattern Recognition
• Classification
• +۶ مورد دیگر

Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Maciej Zięba is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specifically within the Department of Artificial Intelligence . His work spans machine learning, deep learning, and computer vision, with a focus on hyperspectral imaging, autonomous systems, and 3D modeling. Recent research includes uncertainty-aware sensor deployment for autonomous vehicles, low-light image enhancement algorithms, and probabilistic regression frameworks for tabular data. He has co-authored publications on flow-based models, hypernetworks, and neural radiance fields (NeRF) applied to 3D face rendering. Contact: maciej.zieba@pwr.edu.pl

Antonella Poggiمشاهده پروفایل

دانشیار

• Database Theory
• Data Integration and Exchange
• Knowledge Representation and Reasoning
• +۷ مورد دیگر

Antonella Poggi is an Associate Professor in the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome, holding the position in Computer Science and Engineering (IINF-05/A). She recently obtained the National Scientific Qualification as full professor in July 2024. Her research interests include: Database theory, data integration, and exchange Knowledge representation and reasoning Ontologies, knowledge graphs, and Description Logics Data governance and personal information management Metamodeling and semi-structured data Recent publications (2021-2025) demonstrate a consistent focus on ontology-based data access, with key contributions in query answering, data abstraction, and knowledge graph semantics. Her work bridges theoretical foundations with practical applications, as evidenced by co-founding OBDA Systems Srl. Dr. Poggi has led the MODEUS research project (MIUR SIR) and participated in international collaborations. She is an active member of the academic community, serving as General Chair for IRCDL 2024 and CIKM 2026, and Program Co-Chair for multiple conferences including KEOD and ODOCH. Her academic service includes extensive program committee memberships for top conferences (ICDT, EDBT, AAAI, etc.) and leadership in organizing workshops and conferences in digital libraries and knowledge engineering.

Amity Doolittleمشاهده پروفایل

مدرس ارشد

• Environmental Justice
• Natural Resource Management & Policy
• Environmental Anthropology and Sociology
• +۵ مورد دیگر

Amity Doolittle is a Senior Lecturer II at the Yale School of the Environment (YSE), Yale University. Her research focuses on property rights, natural resource management, and the social and political processes shaping environmental inequities. She employs interdisciplinary methods from anthropology, political science, environmental history, and political ecology. Notable projects include historical land-use studies in New Haven, Connecticut, and analyses of indigenous rights in global climate policy. She teaches graduate courses on qualitative research methods, environmental justice, and property rights, emphasizing active, collaborative learning. Doolittle holds a B.A. from Harvard University and M.E.S./Ph.D. from Yale University. Her office is located in Kroon Hall, Yale’s environmental campus. Education: B.A., Harvard University; M.E.S., Ph.D., Yale University Her research interests include legal pluralism, historical resource conflicts, and the intersection of colonial and postcolonial discourses. Current projects examine urban land-use changes in New Haven and the rhetorical strategies of indigenous leaders in climate negotiations. She advises 10–12 master’s students annually on thesis projects and placements worldwide. Teaching highlights include Qualitative Research Methods , Environmental Justice , and Political Ecology of Tropical Forests . Her courses integrate hands-on methods, such as analyzing oral histories and policy documents. Labs/Teams: Collaborates with the Urban Resources Initiative (URI) on greenspace stewardship and community-led projects.

Natalia Noldeمشاهده پروفایل

استاد

• Statistics
• Probability
• Multivariate Extreme Value Theory
• +۵ مورد دیگر

Natalia Nolde is a Professor in the Department of Statistics at the University of British Columbia, Faculty of Science. Her research focuses on multivariate extreme value theory , probabilistic modeling , and applications in quantitative risk management across finance, insurance, hydrology, and geosciences. Her work explores non-classical approaches to multivariate extremes, particularly through limit set geometry and asymptotic dependence structures , offering novel insights into tail dependence and risk assessment. Recent publications highlight her expertise in copula-based risk modeling , financial stress testing , and geohazard prediction . Current students include: Daniel Hadley Jonathan O.K. Agyeman

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +۳ مورد دیگر

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

• Computational Sociology
• Gender Studies
• Social Movements
• +۴ مورد دیگر

Laura K. Nelson is an Associate Professor of Sociology at the University of British Columbia , where she also directs the Centre for Computational Social Science . Her work bridges computational methods with sociological inquiry, focusing on gender inequality, social movements, and organizational dynamics. She previously held faculty roles at Northeastern University and affiliated with institutions like the NULab for Texts, Maps, and Networks and the Network Science Institute . Education: PhD in Sociology (2014), University of California, Berkeley MA in Sociology (2009), University of California, Berkeley BA in Sociology (2006), University of Wisconsin-Madison (Phi Beta Kappa) Research Interests span computational sociology, social movement strategy, intersectionality, and STEM equity. She pioneered frameworks like computational grounded theory and radical objectivity , integrating machine learning with qualitative paradigms. Recent publications analyze gender dynamics in emergency medicine, feminist movement histories, and the NSF ADVANCE program’s impact on equity. Her 2024 Social Science Quarterly paper quantifies ADVANCE’s interdisciplinary reach. Awards include the 2020 Best Meta-Reviewer at SocInfo20 and Outstanding Faculty of the Year at Northeastern University. She serves on editorial boards for American Journal of Sociology , Poetics , and Acta Sociologica . She co-PIs a National Science Foundation grant studying gender-equity dissemination in higher education networks and supervises graduate student Jinyang Yu . Her lab, Centre for Computational Social Science , drives open-source methodological innovation.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +۳ مورد دیگر

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

Cynthia Brewerمشاهده پروفایل

استاد

• cartographic communication
• visualization
• map design
• +۷ مورد دیگر

Cynthia Brewer is a Professor of Geography and Information Sciences and Technology at Pennsylvania State University. She currently serves as the Associate Dean for Faculty Affairs in the College of Information Sciences and Technology (IST), a position she began in August 2024. Previously, she was a faculty member in the College of Earth and Mineral Sciences (EMS) where she served as head of the Department of Geography from 2014 to 2021. Dr. Brewer's research focuses on cartographic communication and visualization, map design, color theory, multi-scale mapping, atlas production, and topographic maps. She is especially well known for her ColorBrewer online tool for selecting map color schemes and her work on ScaleMaster for organizing multiscale mapping. Her research falls primarily within the Geospatial Big Data Analytics and Spatial Modeling and Remote Sensing research clusters at Penn State. Her publications include more than 30 peer-reviewed articles and 60 additional publications and cartographic design resources, generating over 7,000 citations. She has authored four books, including the popular 'Designing Better Maps' with the 3rd edition published by Esri Press in 2024. Carl Mannerfelt Gold Medal from the International Cartographic Association (ICA) in 2023 O. M. Miller Cartographic Medal from the American Geographical Society (AGS) in 2019 Henry Gannett Award for Exceptional Contributions to Topographic Mapping from the U.S. Geological Survey (USGS) in 2013 EMS Wilson Award for Outstanding Service in 2014 As an administrator, Dr. Brewer has extensive experience in departmental leadership, having served as head of the Department of Geography for seven years. She has also served on numerous university committees, was elected to the University Faculty Senate, and served as an Administrative Fellow shadowing the Provost. She is not currently taking on graduate advisees due to her full-time administrative role in IST.

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

Julian Adamekمشاهده پروفایل

پژوهشگر

• Computational Cosmology
• Theoretical Cosmology
• Large-scale structure of the Universe
• +۳ مورد دیگر

Julian Adamek is a computational cosmologist and lead developer of gevolution , a general-relativistic N-body code for cosmological simulations. His work focuses on modeling relativistic effects in cosmic structure formation to better understand gravity’s role on large scales and dark energy. Research Interests: Computational Cosmology, Theoretical Cosmology, Large-scale structure of the Universe, Relativistic N-body simulations. Technical Leadership: Lead developer of gevolution , a public cosmological simulation code available via GitHub. Recent publications span diverse applications of deep learning in geospatial analytics, environmental monitoring, and computer vision, including phenology modeling, biomass mapping, conflict assessment, and 3D reconstruction from point clouds. Key Trends: Integration of AI/ML for environmental tasks, cross-domain applications (cosmology, ecology, forestry), and satellite data processing. Technical Focus: Transformer networks, diffusion models, super-resolution imaging, and ensemble learning for uncertainty quantification. Julian collaborates with researchers in cosmology and geospatial science, though specific students or awards are not mentioned in the provided texts.

Ankit Kariryaaمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Geospatial Analysis
• +۴ مورد دیگر

Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.

Steven Mansonمشاهده پروفایل

استاد

• Geographic Information Science
• Environmental Science
• Biocomplexity
• +۱۲ مورد دیگر

Dr. Steven Manson is a Professor in the Department of Geography, Environment, and Society at the University of Minnesota's College of Liberal Arts, where he also served as Associate Dean for Research and Graduate Programs. He directs the Human-Environment Geographic Information Science (HEGIS) laboratory and leads major data science initiatives like the National Historical Geographic Information System (NHGIS) and IPUMS Terra. PhD in Geography, Clark University (2002) BA Honours in Geography, University of Victoria (1995) His research focuses on geographic information science and human-environment systems , using agent-based modeling and big data to analyze land use change, urban dynamics, and sustainability challenges. Recent work explores spatiotemporal data harmonization and geospatial cyberinfrastructure . The articles reveal trends in GIScience methodology , urbanization analysis , and data-intensive sustainability research . Key contributions include self-organizing map applications for health data and hybrid statistical-GIS techniques for environmental policy. Scientific accolades include: Ecological Society of America Sustainability Science Award NASA Earth System Science Fellow McKnight Land Grant Professorship As Principal Investigator for NHGIS and IPUMS Terra, he secured over $40M in NSF, NIH, and DOJ grants for spatiotemporal data infrastructure. Outreach initiatives include developing open geospatial textbooks adopted globally and collaborating with Twin Cities K-12 programs.