جست‌وجوی استاد و پژوهشگر

Robert Kennedyمشاهده پروفایل

استاد

• Remote Sensing
• Geospatial Analysis
• Landscape Ecology
• +۵ مورد دیگر

Robert Kennedy is a Professor at Oregon State University, specializing in satellite remote sensing and landscape dynamics. His work focuses on analyzing large-scale environmental changes using time-series imagery, with a particular emphasis on disturbance dynamics, carbon cycles, and geospatial modeling. Kennedy leads projects supported by agencies like NASA, the National Park Service, and the US Forest Service, integrating field data with satellite observations to inform ecological and climate policy. Education: PhD, Forest Science, Oregon State University (2004) MA, Environmental Biology, University of Colorado, Boulder (1994) BS, Biology, University of Houston (1992) Research Interests: Kennedy’s research bridges geospatial analysis and environmental science, addressing challenges such as forest carbon dynamics, wildfire impacts, and land-use changes. His methodologies include novel algorithm development for Landsat time-series data and uncertainty quantification in spatial models. He emphasizes interdisciplinary collaboration, particularly in linking biogeochemical models with remote sensing outputs. Key Collaborations & Projects: Development of the LandTrendr algorithm for detecting landscape change Carbon Monitoring System (CMS) for forest biomass mapping Analysis of post-disturbance vegetation recovery in fire-prone regions Advising & Labs: Kennedy mentors graduate students in geospatial science and remote sensing, and leads the Environmental Monitoring, Analysis, and Process Recognition Lab. His lab focuses on creating tools to democratize carbon market data and improve climate policy frameworks. Recent work includes studies on snow drought in the Pacific Northwest and post-conflict land-use changes in Colombia’s Amazon region.

Katie Ovenمشاهده پروفایل

پژوهشگر

• Disaster Risk Management
• Social Vulnerability
• Resilience Studies
• +۶ مورد دیگر

Katie Oven is a human geographer at Northumbria University specializing in social vulnerability and resilience to disasters, particularly earthquakes and landslides in South and Central Asia. Her research bridges social, political, and development geography, emphasizing empirical work in Nepal, Northern India, and Kazakhstan. She is a Co-Investigator on the NERC/DFID-funded SHEAR project, focusing on post-earthquake reconstruction in Nepal involving collaboration with NGOs and government bodies. Previously, she held an Assistant Professor (Research) position at Durham University. Her academic qualifications include a PhD in Geography (2010). Her research interests encompass disaster risk governance, socio-political dimensions of risk management, and interdisciplinary methodologies combining social and biophysical sciences. She actively collaborates with natural scientists, NGOs, and policymakers to address hazard mitigation challenges. Oven’s recent work includes exploring disaster resilience frameworks in post-Soviet contexts and co-authoring studies on landslide inventories, rainfall-triggered hazards, and cascading earthquake hazards. She supervises postgraduate research, including Bina Limbu’s project on rural households’ experiences in Nepal. Her projects often highlight community engagement and policy-relevant outcomes. Key collaborations include the ‘Earthquakes without Frontiers’ project and partnerships with Nepal’s National Reconstruction Authority. Her research emphasizes methodological innovation, particularly integrating ethnographic and physical science approaches in disaster studies.

Matt Boutellمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Robotics Education
• +۲ مورد دیگر

Matt Boutell is an Associate Professor in the Department of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. His research focuses on multimedia content understanding, robotics education, and computer vision techniques for scene classification. Education: PhD in Computer Science, University of Rochester (2005) MS in Electrical and Computer Engineering, University of Rochester (1989) BS in Electrical Engineering, University of Rochester (1985) His research spans computer vision (semantic scene classification, image orientation detection), educational robotics (simulator development), and computer science pedagogy. Recent publications show strong interest in AI applications for education and security systems. Work integrates low-level vision features with contextual metadata for improved classification. Awards recognize innovations in multimedia analysis and semantic scene understanding. He co-developed Rose-Hulman's multidisciplinary robotics minor and created educational tools like the iRobot Create simulator. As advisor, he guides students in robotics capstone projects and mentors research in vision systems. He coordinates the robotics curriculum and maintains active industry collaborations.

Michael Leitnerمشاهده پروفایل

استاد

• Geography
• Criminology
• Geospatial Technology
• +۷ مورد دیگر

Dr. Michael Leitner holds the Carl O. Sauer Professorship in the Department of Geography & Anthropology at Louisiana State University (LSU). He also serves as Director of the LSU Graduate Certificate in Crime Mapping & Geospatial Intelligence Analytics. Additionally, he is an Adjunct Professor in the Department of Experimental Statistics at LSU, Adjunct Faculty in the Department of Geoinformatics at the University of Salzburg (Austria), and an Affiliated Scholar at the Max Planck Institute for the Study of Crime, Security and Law (Freiburg, Germany). His roles span academic leadership and interdisciplinary collaboration across multiple institutions and fields. Dr. Leitner's educational background includes a Ph.D. from the State University of New York at Buffalo (1997), and M.A. degrees from both SUNY Buffalo (1993) and the University of Vienna, Austria (1990). He earned his B.A. from the University of Vienna in 1987. His academic journey reflects a strong foundation in geography and interdisciplinary studies. His research focuses on geospatial technology applications, including Geographic Information Systems (GIS), small Unoccupied Aerial Systems (sUAS), and spatial analysis/modeling/mapping of crime and health. Ongoing projects explore spatiotemporal forecasting of crime events, geospatial privacy issues, and combining spatial video geonarratives with physiological data to evaluate perceived safety. He also investigates the interplay between urban environments, social factors, and crime patterns in cities like Vienna and Baton Rouge. Recent publications highlight interdisciplinary efforts in crime modeling, public health geographies, and geospatial privacy. He collaborates internationally, particularly in Poland and Austria, and integrates emerging technologies like sUAS for low-altitude remote sensing and social media data for crime prediction. Dr. Michael Leitner has not listed any scientific awards or honors in the provided information. In advising and grants, he directs the Crime Mapping & Geospatial Intelligence Analytics program and advises the Crime and Policy Evaluation Research group (CAPER) in the Sociology Department. No specific grants or advisee students are detailed. His teaching includes courses like Geospatial Technology , Crime GIS , and Methods of Spatial Analysis , emphasizing practical skills in spatial data science. He is affiliated with LSU's Lambda Alpha Research, African & African American Studies, Cartographic Information Center, and Climate Extremes and Modeling Group. He leads or contributes to the Critical Ecologies Research Group, DIVA, FACES, Geomorphology Lab, Louisiana Office of State Climatology, Miles Richardson Research Lab, PAST, PLES Lab, SCIPP, and the TIGeRS Lab. These collaborations reflect his commitment to advancing geospatial research across disciplines.

Liping Yangمشاهده پروفایل

استادیار

• GIScience
• Geospatial Artificial Intelligence (GeoAI)
• Computer Vision
• +۶ مورد دیگر

Liping Yang is an Assistant Professor in the Department of Geography and Environmental Studies and the Department of Computer Science at the University of New Mexico (UNM). She also holds an affiliate faculty position at the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Her research focuses on geospatial artificial intelligence (GeoAI), computer vision, and machine learning applied to geospatial big data analytics, disaster management, and indoor navigation. She directs the GeoAIR Lab, which develops innovative methods for GeoAI and geovisualization. Education: Ph.D. in Spatial Information Science and Engineering from the University of Maine (2015), M.S. in Cartography and GIS from Fujian Normal University (2009), and B.S. in GIS from Yunnan Normal University (2006). Postdoctoral positions at Los Alamos National Laboratory (LANL) and Penn State University. Research interests include GIScience, GeoAI, visual big data analytics, geovisualization, and indoor navigation. Her work emphasizes developing AI-driven solutions for smart cities, disaster management, and infrastructure monitoring. Awards: National Scholarship (China, 2004), Yunnan Province Government Scholarship (2005), Michael J. Eckardt Dissertation Fellowship (2014–2015), and Graduate Research Assistantship at University of Maine. Teaching includes courses on advanced GIScience, internet mapping, and geospatial statistics. Her students have developed interactive web maps for topics like water quality in the Navajo Nation and worldwide shark attacks. Labs/Teams: Directs the GeoAIR Lab, collaborating with institutions like LANL and Penn State. Active in organizing workshops on diagram image retrieval (DIRA) at CVPR.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Dr. Sergey Prokudin is a Lecturer at the Department of Computer Science, ETH Zürich, affiliated with the Professur für Computer Vision. His research focuses on computer vision, machine learning, medical imaging, and robotics. Key areas include 3D reconstruction, neural fields, and uncertainty quantification in biomedical applications. His work bridges theoretical advancements in neural networks with practical applications in healthcare and robotics. Research interests span dynamic scene understanding, spatiotemporal modeling, and medical imaging processing. Notable projects include digital twin creation for spinal surgery, robust MRI parameter estimation, and avatar generation from single images. He has published extensively on topics like Gaussian splatting, residual neural fields, and point cloud alignment. No scientific awards have been explicitly listed. His advising and grants focus on interdisciplinary projects combining computer vision with medical engineering. He is part of ETH's Computer Vision group and contributes to labs exploring 3D modeling and medical visualization technologies.

Lianhua Chiمشاهده پروفایل

مدرس

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۷ مورد دیگر

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

• Robotics
• Control Systems
• Environmental Monitoring
• +۵ مورد دیگر

Shafiqul Islam serves as Assistant Professor of Robotics & Mechatronics and Director of Dual Degree Engineering Programs at Xavier University of Louisiana (XULA). His academic home resides within the College of Engineering, where he leads initiatives bridging undergraduate education with partner engineering institutions. His research spans Robotics, Control Systems, and Environmental Monitoring , with recent work shifting toward climate-related applications in Gulf Coast communities. Key interests include: Machine learning for environmental prediction (greenhouse gases, precipitation, air quality) Advanced control systems for robotics and power grids Community-based air quality monitoring using sensor networks Teleoperation and multi-agent coordination protocols Analysis of his 15 most recent publications (2023-2025) reveals a strategic pivot from pure robotics toward environmental applications , particularly Gulf Coast climate challenges. While earlier work focused on quadrotor control and teleoperation, recent papers emphasize deep learning models for pollution monitoring, aerosol analysis, and community engagement in environmental science. This evolution demonstrates interdisciplinary adaptation to regional ecological concerns. No scientific awards or student advisement records are publicly documented. His engineering leadership role drives XULA's dual-degree initiatives, though specific grant details remain undisclosed in available materials. Islam directs the XULA Surface-Based Measurement Initiative , deploying sensor networks for real-time environmental monitoring across New Orleans communities. This program integrates student researchers and local residents in climate data collection, emphasizing environmental justice in Gulf Coast urban settings.

• Data Mining
• Machine Learning
• Urban Mobility
• +۲ مورد دیگر

Fabio Pinelli is an Associate Professor of Computer Science at IMT Lucca, affiliated with the SySMA research unit. His work bridges data mining, machine learning, and real-world applications in urban mobility and distributed systems. Research focuses on developing analytical frameworks for spatiotemporal data, including mobility pattern mining, federated learning security, and blockchain fraud detection. Recent projects examine corporate digitalization patterns and Ethereum smart contract vulnerabilities. Publications demonstrate consistent innovation in mobility data processing (MAT-BUILDER system), explainable AI for financial security, and methodologies for semantic enrichment of trajectory data. Work emphasizes practical implementations in intelligent transportation and cybersecurity domains.

• Artificial Intelligence
• Big Data
• Business Intelligence
• +۷ مورد دیگر

CHEN Yi Ting is an Associate Professor of Computer Science in the Department of Operations, Information and Decisions at MBS in France. She holds adjunct professorships at Texas Tech University (USA) and National Cheng Kung University (Taiwan), and collaborates with the Karlsruhe Institute of Technology (Germany). As a co-founder of three AI companies in Asia and Europe, she bridges academia and industry, specializing in AI and IoT applications for operational optimization. Her research interests include smart cities, transportation, energy, telecommunications, and agriculture. Education: BSc, MSc, and PhD in Computer Science from National Chiao Tung University (Taiwan) Research Focus: Leveraging AI and IoT to enhance operational efficiency in sectors like smart cities, energy systems, and agricultural monitoring. Key themes include digital twin technology, predictive analytics, and sustainable business practices. Recent work emphasizes transportation logistics, customer engagement analytics, and econometric risk modeling. Articles Trends: Publications span predictive modeling for travel time, IoT-driven agriculture, and fintech applications. Notable contributions include frameworks for freight logistics optimization and machine learning approaches to energy demand forecasting. Awards: No explicit awards listed in the provided text. Advising & Grants: No student advisees listed. Collaborations include partnerships with industry and academic institutions, though specific grant details are not provided. Labs/Teams: Engaged with cross-disciplinary teams at MBS and partner institutions, focusing on AI-driven solutions for operational challenges.

• Machine Learning
• Data Science
• Artificial Intelligence
• +۱۴ مورد دیگر

Jesse Read is a Professor (HDR) in the ORAILIX team of the Data Analytics and Machine Learning pôle at École Polytechnique, part of Institut Polytechnique de Paris. He leads research in machine learning with a focus on multi-label classification, data streams, and reinforcement learning, with applications spanning medicine, energy, and transportation systems. His research interests center around two major themes: multi-label and multi-target prediction (modeling multiple interconnected tasks), and learning from sequential data and data streams. He has particular interest in explainability, uncertainty analysis, robustness, domain shift, transfer learning, and continual learning. His methodological toolkit includes deep neural networks, probabilistic graphical models, Monte Carlo methods, and classical machine learning algorithms. Professor Read has built a strong research program with significant funding, including an ANR grant for Dynamic Graph Signal Processing in 2024. His work on Classifier Chains for Multi-label Classification received the prestigious Test of Time Award at ECML-PKDD 2019. His research bridges theoretical advances with practical applications across diverse domains. Test of Time Award at ECML-PKDD 2019 for Classifier Chains for Multi-label Classification As an educator, Read coordinates multiple courses including Deep Reinforcement Learning, Reinforcement Learning and Autonomous Agents, and Machine Learning. He co-coordinates the Trustworthy and Responsible AI (TRAI) Masters program and the Artificial Intelligence and Data Science Track. He has supervised numerous PhD students whose work spans predictive maintenance, reinforcement learning for routing problems, rail network modeling, and medical applications. His River library has become a standard tool for stream learning in Python, resulting from the merger of Creme and scikit-multiflow.

Gerald Corzo Perezمشاهده پروفایل

مدرس

• Hybrid Machine Learning
• Process-Based Modelling
• Spatiotemporal Analysis
• +۵ مورد دیگر

Gerald Corzo Perez is a Lecturer and researcher specializing in hybrid machine learning and process-based modeling at IHE Delft Institute for Water Education. His work focuses on spatiotemporal analysis of extreme climate events, hydrological modeling, and AI applications in water resources management. He has coordinated major projects including the Dominican Republic's national climate change atlas and contributed to drought risk assessments in Central America and Ethiopia. His research integrates remote sensing, citizen science, and social media data for disaster detection and management. Research Interests: Machine learning for hydrological forecasting Climatic impacts on water resources Spatiotemporal drought dynamics Natural language processing for environmental monitoring Reservoir optimization under climate change Key Achievements: Recipient of the 2012 Tison Award (IAHS) Lead developer of the 'intelligent serious game' for citizen science data collection Authored over 40 high-impact journal publications and multiple book chapters Collaborations: Maintains a global network through projects in Vietnam, Colombia, Ecuador, Nicaragua, and Kazakhstan. Active in EU-funded initiatives like the WATCH program and the Hatillo Reservoir optimization project.

• Climate Science
• Carbon Dioxide Removal (CDR)
• Earth System Modeling
• +۳ مورد دیگر

Anusha Sathyanadh is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), within the Faculty of Engineering. Her current research focuses on ocean-based Negative Emission Technologies (NETs) and their integration with land-based solutions like BECCS to achieve climate neutrality, as part of the EU Horizon OceanNET project. She holds a PhD in Atmospheric and Space Sciences from Savitribai Phule Pune University (2018) and has conducted postdoctoral research at the Swedish University of Agricultural Sciences (SLU). Education: B.Sc. Physics (Calicut University, India), M.Sc. Meteorology (Cochin University of Science and Technology, India). Her PhD work explored monsoon boundary layer regimes and their moisture/energy budgets, contributing to improved understanding of spatiotemporal variations in the region. She has authored over 15 peer-reviewed articles in journals like JGR, Atmospheric Research, and Journal of Hydrology. Research interests span Earth system modeling, carbon cycle interactions, climate mitigation strategies, and monsoon dynamics. Her recent work emphasizes synergistic CDR approaches and their Earth system impacts. She has presented at conferences including the Norwegian Geophysical Society Assembly and NTNU Department Seminars, focusing on CDR efficacy and net-zero pathways.

Andrew T. Myersمشاهده پروفایل

استاد

• Offshore Wind Energy
• Structural Engineering
• Probabilistic Modeling
• +۴ مورد دیگر

Andrew T. Myers is a Professor and Associate Chair for Graduate Studies in Civil and Environmental Engineering at Northeastern University. He leads the Sustainable Structures Group, focusing on offshore wind energy, structural resilience, and probabilistic modeling. His research addresses hurricane risk mitigation, innovative turbine designs (e.g., T-Omega Wind's floating platforms), and multi-hazard assessment for offshore infrastructure. Education: Ph.D., Structural Engineering and Geomechanics, Stanford University (2009) M.S., Civil and Environmental Engineering, Stanford University (2006) B.S., Civil Engineering, Johns Hopkins University (2004) Key Research Interests: Myers investigates fixed and floating offshore wind structures, computational simulation of structural behavior, and probabilistic modeling of extreme environmental conditions. His work emphasizes hurricane risk assessment, cost-effective turbine designs, and sustainable infrastructure solutions. Notable projects include the DOE-funded Academic Center for Reliability and Resilience of Offshore Wind (ARROW) and the National Offshore Wind Research and Development Consortium's hurricane risk mitigation program. Awards & Recognition: 2023 Constantinos Mavroidis Translational Research Faculty Award 2021 College of Engineering Faculty Fellow 2020 Cleantech Open Northeast Winner (T-Omega Wind) 2016 NSF CAREER Award Grants & Collaborations: Myers has secured funding from NSF, DOE, and industry partnerships to advance offshore wind technology. His projects include optimizing thin-walled tube towers, developing multiline anchor systems, and assessing hurricane impacts on wind farms. He collaborates with institutions like UMass Amherst and industry leaders such as T-Omega Wind. Labs & Teams: He directs the Sustainable Structures Group, which bridges academia and industry to tackle challenges in offshore wind energy. The group focuses on structural innovation, risk-based design, and scalable solutions for global energy transitions.

• Cybersecurity
• Privacy
• Data management
• +۴ مورد دیگر

Dr. Wanlun Ma is a Postdoctoral Research Fellow at the School of Science, Computing and Emerging Technologies , Swinburne University of Technology . He earned a Bachelor's and Master's in Information and Communication Engineering from the University of Electronic Science and Technology of China (UESTC) in 2017 and 2020, respectively, followed by a Ph.D. in Computer Science from Swinburne University of Technology in 2024. His research focuses on Trustworthy and Responsible AI , particularly in adversarial machine learning, network security, and privacy preservation. Education B.E., M.E. in Information and Communication Engineering, UESTC (2017, 2020) Ph.D. in Computer Science, Swinburne University of Technology (2024) Research Interests Trustworthy AI systems Adversarial machine learning Network security and privacy AI ethics and accountability Grants Australian Research Council (ARC) grant (2025-2029) for AI Models for Digital Manufacturing Supervision Available to supervise Ph.D. candidates