جست‌وجوی استاد و پژوهشگر

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

Steven Vertovecمشاهده پروفایل

استاد

• International Migration
• Transnationalism
• Ethnic Diasporas
• +۷ مورد دیگر

Steven Vertovec is Founding Director of the Max Planck Institute for the Study of Religious and Ethnic Diversity in Göttingen, Honorary Joint Professor of Sociology and Ethnology at the University of Göttingen, and Supernumerary Fellow at Linacre College, Oxford. Previously, he was Professor of Transnational Anthropology at the University of Oxford and Founding Director of COMPAS. He remains an active academic leader in migration and diversity studies. Research Interests: His work centers on globalization, transnational social formations, international migration, ethnic diasporas, and urban diversity. He is best known for pioneering the concept of super-diversity , which examines the increasing complexity of migration patterns and urban populations. His research integrates sociological, anthropological, and policy-oriented perspectives. Recent Publications Trends: His most recent articles focus on super-diversity, complexity thinking in migration studies, visualization of urban diversity, public attitudes toward diversity and refugees, and the societal impacts of the pandemic on transnationalism. These works emphasize interdisciplinary and systemic approaches to understanding migration and integration. Scientific Awards: Honorary Doctorate in Social Sciences, Université de Liège (2018) Advising and Grants: While no direct students are listed, Vertovec has led major research projects funded by foundations and public agencies. He has advised the Volkswagen Foundation, UK government departments, the European Commission, UNESCO, the World Bank, and the G8. His leadership in COMPAS and the Max Planck Institute reflects extensive grant management and team supervision. Labs and Teams: He leads the GlobaldiverCities (GloDiv) project and the ProDiv initiative on diversity assent in urban Germany. His teams include researchers from multiple institutions and countries, working on topics such as asylum seekers’ needs, intergroup contact, and urban integration. The Max Planck Institute serves as a hub for interdisciplinary migration research in Europe.

Matthias Krauledatمشاهده پروفایل

استاد

• Machine Learning
• Brain-Computer Interface
• Signal Processing
• +۳ مورد دیگر

Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.

Marcus Bellamyمشاهده پروفایل

دانشیار

• Supply Chain Management
• Sustainable Operations
• Innovation
• +۶ مورد دیگر

Marcus Bellamy is an Associate Professor of Operations and Technology Management at the Questrom School of Business, Boston University . His research focuses on innovation , environmental sustainability , and performance dynamics in supply networks, using empirical models to analyze spatial resource proximity and interdependence. Education : PhD in Operations & Technology Management from Georgia Tech (2015), MS in Industrial Engineering (2010), BS in Aerospace Engineering from University of New Mexico (2006). His research bridges supply chain structure with outcomes like CSR practices , resilience , and economic welfare . He employs visual analytics and computational tools to study industries ranging from automotive to electronics , addressing policy impacts and platform economies like Uber . Recent work trends include analyzing two-sided sharing platforms , environmental disclosure in supply chains, and network resilience under risks. His empirical studies often integrate spatial spillovers and collaborative ecosystems . Scientific Awards : Isabel Anderson Career Development Professorship 2019 AOM Best Paper in Supply Chains 2013 AOM Operations Management Division Best Student Paper Fulbright Scholar (Madrid, Spain) NSF STEP Fellow Bellamy has served as a founder of the PhD Project Committee under the White House Initiative on Hispanic Education and collaborated with corporations like Lockheed Martin and General Motors on supply chain risk & resilience. His methodological contributions include interactive visual analytics systems for network evaluation.

Howard S. Neufeldمشاهده پروفایل

استاد

• Plant Physiological Ecology
• Air Pollution Effects on Plants
• Water Relations of Plants
• +۳ مورد دیگر

Howard S. Neufeld is a Professor in the Department of Biology at Appalachian State University, with expertise in plant physiological ecology and air pollution impacts. His research focuses on ozone effects on native flora in the Great Smoky Mountains National Park, water relations in plants, and invasive species ecology. Education Ph.D. from University of Georgia (1984) Neufeld's research examines plant responses to environmental stressors, particularly tropospheric ozone and water stress. His work on cutleaf coneflower, tall milkweed, and Galax urceolata has revealed critical insights into foliar injury mechanisms, antioxidant defenses, and seasonal adaptation strategies. He investigates how invasive species like Microstegium vimineum exploit light gaps and how hydraulic conductance affects plant drought tolerance. His publication record shows consistent focus on ozone impacts in southern Appalachian ecosystems, with recent work emphasizing seasonal variation in plant responses, stomatal behavior, and biochemical defense mechanisms. Key trends include the development of non-destructive monitoring techniques and spatial analysis of pollution effects across landscapes. Advising & Grants Directed 20 Master's theses on plant ecophysiology, invasive species, and pollution impacts Currently mentoring 7 graduate students on lichen nitrogen responses, heavy metal effects, and ozone physiology Funded by National Park Service, U.S. EPA, National Geographic Society, and Friends of the Smokies Collaborates with researchers from Auburn University, USDA, Oak Ridge, and international institutions Neufeld maintains active field research in the Great Smoky Mountains National Park and operates laboratory facilities for gas exchange and plant stress analysis, with ongoing projects examining nitrogen deposition effects on lichens and nickel pollution impacts on pines.

• Building Information Modeling (BIM)
• Geographic Information Systems (GIS)
• Digital Twin Technology
• +۳ مورد دیگر

Prof. Bernd Domer is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO) specializing in Building Information Modeling (BIM) , Geographic Information Systems (GIS) , and digital transformation of civil engineering . He leads multiple ongoing research projects including CU_OFROU_PAB (CHF278,844) focused on BIM-GIS workflows for noise barriers, and SousEtoile (CHF50,000) developing subsurface prediction models for urban planning. His work addresses critical challenges in software interoperability and point cloud processing for infrastructure digital twins. BA HES-SO in Architecture (HEPIA) BSc Civil Engineering (EPFL) BSc HES-SO in Civil Engineering (HEPIA) MSc HES-SO in Engineering (HES-SO Master) His research explores digital workflows for infrastructure projects, with over 15 recent publications examining topics like: Semantic segmentation of point clouds (2024) IFC standard optimization (2023) Underground confidence level modeling (2021) Swiss BIM implementation frameworks (2020) Construction waste management platforms (2018) He serves as Head of the MIC Group and co-directs the CAS in BIM Coordination . Active in international committees like EG-ICE and Bauen digital Schweiz , his work bridges academic research with practical implementation through collaborations with HEPIA , HEIG-VD , and institutions like the Swiss Federal Roads Office (OFROU) .

Farah Kamwمشاهده پروفایل

استادیار

• Information Visualization
• Visual Analytics
• Spatial-Temporal Data Analysis
• +۴ مورد دیگر

Farah Kamw is an Assistant Professor in the Department of Computer Science at Wayne State University. With a PhD in Computer Science (2019) from Kent State University, her expertise spans 18 years of software development, academic teaching, and research in information visualization and database management. Education : PhD (Kent State), MSc (University of Zakho), BSc (University of Baghdad) Her research focuses on Information Visualization and Visual Analytics of spatial-temporal data, particularly through 8 publications (2013-2021) addressing urban mobility patterns, trajectory analysis, and geospatial data integration. She has developed several open-source visual analytics tools including TrajAnalytics and SparseTrajAnalytics, applying both document and graph database techniques. Farah teaches core Computer Science courses such as Algorithm Design , Programming Languages , and Database Systems . Her technical skills include Python, C++, Java, SQL, NoSQL databases, and GIS technologies.

Seongjin Choiمشاهده پروفایل

استادیار

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +۳ مورد دیگر

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.

Tamas Monaمشاهده پروفایل

پژوهشگر

• Epidemiology
• Plant Pathology
• Meteorology
• +۵ مورد دیگر

Dr. Tamas Mona is a Postdoctoral Research Associate in the Department of Plant Sciences at the University of Cambridge , affiliated with the Epidemiology and Modelling Group . His work focuses on environmental suitability models for large-scale epidemiological forecasting, particularly in wheat rust outbreaks across Africa, the Middle East, and Asia. Collaborations include the UK Met Office, CIMMYT, and institutions in Ethiopia, Kenya, Bangladesh, and Nepal. PhD in Environmental Sciences (2019), Eötvös Loránd University MSc in Meteorology (2013), Eötvös Loránd University BSc in Physics with Meteorology (2011), Eötvös Loránd University Research interests integrate meteorological applications with epidemiological models to predict crop disease outbreaks. Key projects involve tracking transmission pathways for stem rust pathogens and analyzing how irrigation creates green bridges for intercontinental pathogen spread. Publications emphasize environmental science and computational epidemiology . Current collaborations span Sub-Saharan Africa (EIAR, ATI, KARLO) and South Asia (BWMRI, NARC) through initiatives like the Global Food Security IRC . Modeling frameworks developed by Mona contribute to policy advisory systems for emerging pest threats, aligning with DEFRA and UK government strategies.

Soumik Purk3ayasthaمشاهده پروفایل

استادیار

• Biostatistics
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Soumik Purkayastha is an Assistant Professor in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. He also serves as a Research Biostatistician at the Center for Healthcare Evaluation, Research, and Promotion (CHERP) within the Department of Veterans Affairs, focusing on improving healthcare outcomes for veterans. B.Sc. (Hons.), St. Xavier's College, Kolkata, 2014-17 M.Stat. (Biostatistics), Indian Statistical Institute, 2017-19 M.S. in Biostatistics, University of Michigan, 2019-21 Ph.D. in Biostatistics, University of Michigan, 2019-24 His research develops scalable statistical and machine learning methods for biomedical studies, emphasizing information-theoretic frameworks for association and causality without traditional causal inference assumptions. Applications include mediation analysis , instrumental variables , and spatiotemporal forecasting of infectious diseases like SARS-CoV-2. He integrates Bayesian and semi/non-parametric approaches with computational challenges in statistical modeling. His publications focus on asymmetric association methods, infectious disease compartmental models (e.g., SEIR-fansy), and data-driven pandemic resilience strategies. Key themes include causal discovery , collider detection , and patient-reported outcome correlation analysis in clinical studies. Prior to joining Pitt, he worked with the Abecasis Group and Diabetic Foot Consortium at the University of Michigan. He has developed open-source software tools like SEIRfansy , fastMI , and comet , contributing to epidemiological and statistical methodology.

Lisa Grant Ludwigمشاهده پروفایل

استاد

• Earthquake Science
• Seismology
• Geohazards
• +۶ مورد دیگر

Lisa Grant Ludwig is a Professor in the Department of Population Health and Disease Prevention at the University of California, Irvine (UCI). She is a nationally recognized expert in earthquake science, focusing on translating geophysical research into policy for disaster risk reduction. Her work bridges seismology, public health, and policy implementation. PhD in Geology with Geophysics minor (Caltech) MS in Environmental Engineering Science (Caltech) BS in Applied Environmental Earth Science (Stanford) Dr. Ludwig's research centers on earthquake dynamics, particularly along the San Andreas Fault, advancing disaster resilience through innovative nowcasting techniques using AI and machine learning. Her publications demonstrate expertise in seismic hazard modeling, geodetic imaging, and community preparedness studies. Recent publications highlight AI-enhanced earthquake prediction (QuakeGPT), temporal-spatial nowcasting models, and applications of geodetic data for crustal deformation analysis. These works integrate machine learning with traditional seismological methods to improve hazard forecasting. President of Seismological Society of America NASA 2012 Software of the Year Medal Featured on Science magazine cover Congressional Testimony provider Active Federal Advisory Committee member Her interdisciplinary approach combines geophysics, public health policy, and computational science. Current projects focus on earthquake nowcasting, fault zone analysis, and developing accessible geospatial tools like GeoGateway for disaster response.

Somak Duttaمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Genomics
• +۵ مورد دیگر

Somak Dutta is an Associate Professor at Iowa State University conducting interdisciplinary research at the intersection of statistical methodology, machine learning, and agricultural sciences. His work addresses critical challenges in plant breeding, genomics, and environmental modeling through innovative quantitative approaches. His academic foundation includes: PhD in Statistics, University of Chicago, 2015 M.Stat (First Division with Distinction), Indian Statistical Institute, Kolkata, 2010 B.Stat (Honours with Distinction), Indian Statistical Institute, Kolkata, 2008 Dr. Dutta specializes in developing advanced statistical frameworks for high-dimensional data analysis, Bayesian variable selection, and spatial modeling. His research directly impacts plant breeding programs through applications in phenotyping, haploid induction systems, drought response mechanisms, and genetic architecture studies across key crops including maize, soybean, and mungbean. He also contributes to meteorological science through machine learning applications for severe weather verification. Analysis of his 2023-2025 publications reveals two dominant research streams: (1) agricultural applications focusing on genomic selection, phenotyping technologies, and stress response modeling in crops, and (2) meteorological innovations applying machine learning to severe thunderstorm wind verification and precipitation forecasting. Both streams demonstrate his signature approach of developing novel statistical methodologies tailored to domain-specific challenges. No scientific awards were documented in the provided materials. Information regarding student advising, grant funding, laboratory infrastructure, or collaborative teams was not available in the source documentation.

Ellen Larsonمشاهده پروفایل

پژوهشگر ارشد

• Contemporary Chinese Art
• Video Art
• Modern Chinese Art
• +۵ مورد دیگر

Ellen Larson serves as a CAEA Postdoctoral Instructor in the Department of Art History at the University of Chicago, concurrently holding the position of Associate Director at the Center for the Art of East Asia (CAEA). Her academic and curatorial work centers on modern and contemporary art from Asia and Asian diasporic communities, with emphasis on moving image practices and institutional frameworks. Education PhD in Contemporary Chinese Art, University of Pittsburgh MA in Global Contemporary Art History, University of Pittsburgh MA in Modern Chinese History, Minzu University of China, Beijing Research Focus Larson’s scholarship investigates temporalities in Chinese moving image art from the late 1980s through early 2000s, examining post-industrial urban aesthetics, technological mediation, and diasporic identities. Her work critically engages AI technologies, virtual economies, and online communication platforms as artistic mediums, revealing how digital infrastructures reshape cultural memory and spatial perception in contemporary Chinese visual culture. Publication Trends Her 2019-2024 publications demonstrate rigorous engagement with Chinese video art’s evolution, consistently analyzing artists like Cao Fei and Su Yuhsin through lenses of utopian nostalgia, industrial heritage, and planetary futurism. These works reveal a methodological shift toward interdisciplinary approaches blending art history, media studies, and urban theory, particularly in examining how virtual platforms and monetary systems transform artistic production and reception. Awards & Recognition US Fulbright Program Getty Foundation Grant Asia Foundation Fellowship Henry Luce Foundation Support Dunhuang Foundation Award Center for Contemporary Art Research in Japan Program University of Chicago Provost’s Global Faculty Award Curatorial Leadership As Associate Director of CAEA, Larson directs major research initiatives while curating exhibitions that bridge academic scholarship and public engagement. Her grants from the Fulbright Program and Getty Foundation have enabled extensive fieldwork in China, facilitating collaborative projects with artists and institutions that document ephemeral media practices and preserve critical art historical archives for scholarly access. Exhibition Practice Larson’s curatorial portfolio—including Ephemeral Architectures (2023) and Materializing Memory (2019)—creates vital platforms for Chinese video art within U.S. institutions. These projects establish methodological frameworks for preserving time-based media while challenging Western-centric exhibition histories, particularly through her focus on intermediary spaces and counter-mapping strategies that reveal hidden narratives in urban landscapes.

Yves Boubenecمشاهده پروفایل

دانشیار

• Auditory Neuroscience
• Perceptual Attention
• Sound Encoding
• +۳ مورد دیگر

Dr. Yves Boubenec is an Associate Professor at École Normale Supérieure (ENS)-PSL University, Paris, France. He serves as Head of the LSP Neuro Platform and Director of Studies at the Department of Cognitive Studies. Academic Rank: Associate Professor Institution: ENS-PSL Departments: Cognitive Studies (ENS), LSP Neuro Platform Email: yves.boubenec@ens.psl.eu Research Focus: Boubenec investigates neural mechanisms of auditory perception and cognition using integrated methodologies spanning single-neuron electrophysiology to large-scale neuroimaging. His work reveals how context, learning, and multisensory interactions shape sound encoding in mammalian neocortex. Primary Research Themes Context-dependent auditory encoding Perceptual attention mechanisms Task-driven neural plasticity Self-supervised learning models Population-level cortical dynamics Human/ferret auditory comparisons Publication Trends: Recent work (2024-2025) examines speech production networks, premotor auditory categorization, and algebraic structures in sound learning. Earlier studies (2018-2022) focus on population gating, hierarchical auditory coding, and self-voice mechanisms. 2025 Self-voice frequency analysis Hierarchical ferret auditory cortex mapping Temporal window constraints 2024 Premotor category hemodynamics Self-supervised sound structures Human speech cortical encoding Methodological Expertise: Combines awake ferret functional UltraSound, Neuropixels recordings, and computational modeling to analyze neural representations across spatial scales. Specializes in translating animal model findings to human auditory processes.

Laurent Caraffaمشاهده پروفایل

پژوهشگر

• 3D Data Processing
• Surface Reconstruction
• Point Cloud Processing
• +۷ مورد دیگر

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.