جست‌وجوی استاد و پژوهشگر

Bal Krishna Nyaupaneمشاهده پروفایل

استادیار مهمان

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Dr. Bal Krishna Nyaupane serves as a Visiting Assistant Teaching Professor in the Department of Computer Science at the University of Missouri–St. Louis, within the College of Arts and Sciences. His academic role emphasizes teaching and research at the intersection of computing technologies and societal impact. Education: MS in Computer Science, Mälardalen University, Sweden (2014) Dr. Nyaupane's research spans artificial intelligence ethics , where he examines algorithmic accountability and digital feudalism; machine learning applications in Alzheimer's disease biomarker discovery and satellite imagery analysis; and computer science education innovation , developing AI-enhanced pedagogical tools for coding instruction. His interdisciplinary approach integrates computational methods with biological, ethical, and educational challenges. Publication trends from 2023-2025 reveal three dominant threads: (1) Ethical AI frameworks addressing trust, data ownership, and societal impacts; (2) Bioinformatics research applying machine learning to neurodegenerative disease mechanisms; and (3) Educational technology innovations for AI-era programming pedagogy. These areas demonstrate consistent focus on real-world applications of computing with strong interdisciplinary connections to neuroscience, ethics, and education.

Erik-Jan van Kesterenمشاهده پروفایل

استادیار

• Data Science
• Computational Social Science
• Bayesian Statistics
• +۷ مورد دیگر

Dr. Erik-Jan van Kesteren is an Assistant Professor in the Methodology and Statistics department at Utrecht University , where he leads the ODISSEI Social Data Science (SoDa) team. His work bridges statistical methodology with computational social science, with a focus on synthetic data, Bayesian inference, and open research infrastructure. Current research initiatives include: Developing privacy-preserving synthetic data frameworks Improving computational approaches to structural equation modeling Addressing geospatial sampling bias in volunteer-collected datasets Exploring text-based personality computing and language association tools He maintains the department's compute server infrastructure and contributes to open-source projects like ArtScraper and osmenrich. Contact via e.vankesteren1@uu.nl or erikjanvankesteren@pm.me

• Remote sensing
• Evapotranspiration
• Hydrologic modeling
• +۱۲ مورد دیگر

Dr. Justin Huntington is a Research Professor of Hydrology at the Desert Research Institute (DRI) in Reno, Nevada, with a joint affiliation in the Graduate Program of Hydrologic Sciences at the University of Nevada Reno. His work focuses on remote sensing applications for water resource management, particularly in arid and semi-arid regions of the western United States. Education: Ph.D. in Hydrologic Sciences, University of Nevada Reno (2011) M.S. in Hydrologic Sciences, University of Nevada Reno (2003) B.S. in Environmental Sciences/Hydrology, University of Nevada Reno (2000) Huntington's research spans remote sensing, evapotranspiration, irrigation, drought, hydrologic modeling, and groundwater-dependent ecosystems. He develops operational tools using satellite data (particularly Landsat) and cloud computing platforms like Climate Engine to translate scientific findings into water management applications. His work emphasizes stakeholder engagement to address real-world challenges in water rights, climate change impacts, and consumptive use estimation. As a member of the 2018-2023 Landsat Science Team, he contributes to advancing earth observation capabilities for hydrologic applications. Recent publications reveal a strong trend toward integrated remote sensing approaches for continental-scale water monitoring, with significant focus on OpenET satellite-based evapotranspiration products, reservoir evaporation modeling, and groundwater-surface water interactions. His research demonstrates increasing integration of machine learning, multi-sensor data fusion, and field-scale applications to address water scarcity in the western U.S. His projects receive primary funding from the U.S. Bureau of Reclamation, U.S. Geological Survey, U.S. Bureau of Land Management, NASA, western state water agencies, and private sector partners. Huntington actively collaborates with federal, state, and tribal entities including the Navajo Nation on drought monitoring initiatives. Based in DRI's Division of Hydrologic Sciences, Huntington leads research teams developing operational tools like Climate Engine for cloud-based analysis of climate and remote sensing data. His group maintains strong partnerships with water management agencies across the western U.S. to implement satellite-derived water use data in operational decision-making contexts.

Baran Kaynakمشاهده پروفایل

استادیار

• Cloud Manufacturing
• Blockchain Technology
• Resource Allocation
• +۵ مورد دیگر

Baran Kaynak serves as an Assistant Professor in the Department of Information Systems Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has held academic positions since 2013 (promoted from Lecturer to Assistant Professor in 2022). His institutional affiliation spans teaching, research, and project leadership within Turkey's higher education framework. His educational trajectory culminates in a Doctorate in Industrial Engineering from Sakarya University (2012-2021) with thesis work on Blockchain-Based Cloud Manufacturing Model , preceded by a Master's degree (2011-2013) focusing on wireless sensor networks in manufacturing logistics and a Bachelor's in Industrial Engineering (2007-2011) from the same institution. Dr. Kaynak's research integrates cloud computing, blockchain, and AI to solve industrial and environmental challenges. Core contributions include hybrid decision support systems for cloud manufacturing resource allocation using genetic algorithms and AHP, GPU-accelerated solar potential analysis tools, and custom web components for earth science data (e.g., Geo-WC for NOAA/USGS integration). His work bridges theoretical algorithms with practical implementations in manufacturing, renewable energy, and hydrological systems. Recent publications (2023-2025) reveal expanding applications: digital twin frameworks for urban flood analysis, IoT platforms for hydrological education (HydroSignal), and mmWave sensor-based anomaly detection in assembly lines. These works consistently demonstrate cross-disciplinary innovation, particularly in adapting blockchain for secure resource sharing and developing real-time analytics for environmental monitoring. He leads the UZEP project (2020), a Sakarya University-funded cloud-based distance education platform developed during the pandemic. While no formal student advisees are listed, his research includes student consultancy services using AI for course grade prediction and adaptive neuro-fuzzy systems for academic success modeling. Dr. Kaynak collaborates with Sakarya University's Distance Education Research and Application Center and contributes to open-source tools for earth science organizations. His current work focuses on city-scale digital twins for climate resilience and blockchain-enhanced manufacturing ecosystems, positioning him at the intersection of industrial informatics and sustainable technology development.

Luísa Vieira Luccheseمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Remote Sensing
• Hydrology
• +۷ مورد دیگر

Luísa Vieira Lucchese is a Research Assistant Professor at the University of Pittsburgh's Department of Geology and Environmental Science since August 2024. Her work focuses on the intersection of Machine Learning , Remote Sensing , and Hydrology , with an emphasis on modeling river geomorphology , freshwater monitoring , and climate change impacts on hydrological systems. PhD and Master's in Water Resources and Environmental Sanitation from Universidade Federal do Rio Grande do Sul (UFRGS), Brazil PhD Sandwich at Friedrich-Schiller-Universität Jena, Germany Research Interests include: Applying Remote Sensing and Machine Learning to study sediment flux , landslide susceptibility , and global freshwater systems Developing open-source models and spatial cross-validation techniques Advocating for open-source GIS software through her RS/GIS Blog , which includes tutorials on QGIS, GDAL, and SAGA GIS. Her scientific awards include: CAPES National Award for PhD Theses (2023) Academic Accolade for PhD Thesis (IPH-UFRGS, 2022) Academic Accolade for Master’s Thesis (IPH-UFRGS, 2018) Young Researcher Award UFRGS (2014) Luísa collaborates with Prof. John Gardner’s lab on NASA-funded projects to estimate global sediment flux using AI. She also presented at major conferences such as EGU General Assembly , WorCAP , and Spatial Lightning Talks UCSB .

Bernhard Höfleمشاهده پروفایل

استاد

• Geoinformatics
• 3D Point Cloud Analysis
• Environmental Monitoring
• +۵ مورد دیگر

Prof. Dr. Bernhard Höfle is a Professor of Geoinformatics and 3D Geospatial Data Processing at the University of Heidelberg, where he leads the 3DGeo Research Group. His work focuses on computational methods for analyzing 3D/4D point clouds, with applications in environmental monitoring, natural hazard assessment, and vegetation studies. Research Interests: 3D/4D point cloud analysis for environmental observation Development of computational algorithms for geospatial data Integration of machine learning in remote sensing applications Virtual laser scanning simulations (VLS-4D) Real-time monitoring systems for critical infrastructure Digital Twin integration across scales Article Trends: His recent publications emphasize real-time 3D environmental monitoring, virtual simulation of dynamic scenes, multi-temporal analysis of natural processes, and machine learning applications for point cloud classification. They span from 2025 back to 2023 and cover diverse applications including rock glaciers, forest monitoring, building damage assessment, and bee activity tracking. Key Techniques: His work prominently features terrestrial laser scanning, UAV-based LiDAR, and open-source software development. He has contributed datasets like FOR-species20K and developed simulation frameworks such as HELIOS++.

• Simulation-based Learning
• Agent-based Simulation
• Stochastic Simulation
• +۴ مورد دیگر

Ashkan Negahban is an Associate Professor of Engineering Management at Penn State University's Engineering Division (Great Valley). His work focuses on simulation-based learning, stochastic modeling, and optimization techniques in engineering education and transportation systems. NSF-funded project on immersive simulated environments for STEM education (2020–2025) Active in hybrid simulation methodologies and spatial analysis validation Contributions to bike-sharing system analytics and UAV communication modeling His research combines agent-based simulation with deep reinforcement learning , particularly examining immersion effects in educational settings. Key application areas include smart building energy control and manufacturing process optimization . Recent publications demonstrate a strong focus on Wasserstein distance applications, 3D blockage modeling , and stochastic supply chain planning . His technical contributions span simulation validation methods, bike station inventory analysis, and material handling system optimization.

Albert J. Kettnerمشاهده پروفایل

دانشیار

• Hydrology
• Sediment transport
• Flooding
• +۴ مورد دیگر

Albert J. Kettner is an Associate Professor at the University of Colorado Boulder and serves as the Research Associate Director of INSTAAR (Institute of Arctic and Alpine Research) and Director of the DFO Flood Observatory. His work focuses on understanding and modeling Earth surface processes, particularly related to hydrology, sediment transport, and flooding on a global scale. Dr. Kettner received his educational training in the Netherlands: PhD: Delft University of Technology, 2007 MS: Wageningen University, 1997 BS: Wageningen University, 1993 His research primarily focuses on modeling sediment flux responses within river basins to human impact and climate change. He develops methodology to simulate sediment properties based on provenance and studies influences of rapid climate change (deglaciation) during the late Quaternary on sediment fluxes to the ocean. His work integrates remote sensing data with numerical modeling to understand hydrological extremes, flood dynamics, and sediment transport processes in changing environments. Analysis of his recent publications reveals a strong trend toward understanding climate change impacts on hydrological systems, with particular emphasis on how warming temperatures affect sediment transport in mountainous regions, flood dynamics, and delta morphology. There's a clear research trajectory incorporating satellite data for flood monitoring and advancing FAIR (Findable, Accessible, Interoperable, Reusable) principles into community modeling software infrastructure. Dr. Kettner has received notable recognition for his scholarly contributions: PROSE Award for Best Earth Science Book, Association of American Publishers, 2019 As Director of the DFO Flood Observatory and Associate Director of INSTAAR, Dr. Kettner leads significant research initiatives in flood monitoring and Earth surface dynamics. He assumed the role of acting director of INSTAAR on July 10, 2023, following Merritt Turetsky's return to faculty positions. His work has been supported by substantial grants, including a $2.56 million National Science Foundation grant for the OpenEarthScape project, where he serves as a co-PI alongside Eric Hutton, Irina Overeem, and Mark Piper. Dr. Kettner is deeply involved with the Community Surface Dynamics Modeling System (CSDMS), where he contributes to advancing research in geosciences through model integration tools. His DFO Flood Observatory provides critical flood and river information using remote sensing approaches to water monitoring, serving as an important resource for understanding global flood patterns and risks, particularly in the context of climate change.

Arthur G Richardsمشاهده پروفایل

استاد

• Trajectory Optimization
• Unmanned Aerial Vehicles (UAVs)
• Model Predictive Control
• +۴ مورد دیگر

Arthur G Richards serves as Professor of Robotics and Control within the Dynamics and Control department at the University of Bristol's School of Engineering Mathematics and Technology. His research specializes in trajectory optimization for aerospace applications, focusing on UAV autonomy, spacecraft rendezvous, and air traffic management through advanced optimization techniques. His educational foundation includes an M.Eng. from the University of Cambridge and S.M./Ph.D. degrees from MIT. Research interests center on solving complex aerospace challenges through: Non-convex optimization for obstacle avoidance in cluttered environments Robust model predictive control for real-time disturbance compensation Distributed optimization enabling large-scale vehicle cooperation Scalable algorithms for high-traffic scenarios with minimal fuel consumption Analysis of his 132 research outputs reveals evolving emphasis on reliability-aware UAV path planning, interpretable reinforcement learning for aircraft control, and swarm robotics with real-world validation. Recent work increasingly integrates machine learning with traditional control theory while addressing practical constraints like sensor noise and system failures. Professor Richards has supervised 22 research students and secured funding for 11 projects, including the active Aerial Robotics for Search and Rescue (2022-2026) and PORTAL (2022-2024) initiatives. His industry collaborations with Thales and focus on technology transfer demonstrate strong academic-industrial integration. He actively contributes to the Smart Networks for Sustainable Futures and Robotics research groups, developing frameworks for multi-robot systems that balance theoretical rigor with practical deployment requirements in conservation, inspection, and exploration scenarios.

• Image Analysis
• Bioenergy
• Cartography
• +۱۲ مورد دیگر

Paolo Zatelli is an Associate Professor at the University of Trento , Department of Civil, Environmental and Mechanical Engineering. His academic career spans over two decades, with research expertise in geomatics, photogrammetry, and open-source GIS applications. Department of Civil, Environmental and Mechanical Engineering (since 2001) Researcher in ICAR06 (Topography and Cartography) International collaborations in geospatial projects Education includes a degree in Environmental and Territory Engineering (1993, 110/110 with honors) and a PhD in Topographic and Geodetic Sciences (1998). He has taught courses in GIS, Digital Cartography, and GPS surveying. Research Interests focus on geospatial technologies and their applications. Key areas include digital photogrammetry , wavelet applications in geodesy , GPS survey planning , and open-source GIS development . His work extends to environmental monitoring, historical cartography analysis, and sustainable forest management systems. Publication Trends show sustained contributions to FOSS4G (Free and Open Source GIS) applications, with recent work covering topics like landscape metrics , EV charging infrastructure , beta diversity analysis , and historical map digitization using GRASS GIS and QGIS. Scientific Contributions include: Member, IAG Special Study Group 4.187 on Wavelets in Geodesy Organizing committee, IV Hotine-Marussi Symposium Key developer of GRASS GIS applications Contributor to GOCE satellite geodesy projects Teaching and Mentorship highlights his role in both academic and professional GIS training, with over 20 theses supervised. His Technical Contributions involve developing open-source tools for spatial database management, statistical analysis, and georeferencing workflows.

• Earth Observation and Remote Sensing
• Machine Learning and AI
• Citizen Science and Participatory Planning
• +۴ مورد دیگر

Rosa Maria Aguilar Bolivar is an Assistant Professor at the Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente. Her work bridges geospatial technology, machine learning, and participatory planning, with a focus on urban sustainability and food security challenges.

• Geographic Information Systems (GIS)
• Urban Planning
• Land Use Analysis
• +۴ مورد دیگر

Victor Manuel Rodriguez Espinosa is a Professor in the Department of Geology, Geography and Environment at the University of Alcalá's Faculty of Philosophy and Letters. He leads the research group 'Geographic Information Technologies and Territorial Analysis,' focusing on GIS applications in socio-environmental challenges. His work spans urban simulation, risk mapping, and land-use optimization. His research integrates spatial analysis with environmental planning, notably in projects like green infrastructure design for the Henares Corridor (Madrid) and cross-border GIS frameworks in Central America. He collaborates internationally, including capacity-building initiatives at the National Autonomous University of Honduras. Recent publications emphasize urban cadastral systems, deforestation monitoring, and spatial methodologies. He actively mentors PhD students in geospatial topics and has developed educational frameworks for GIS competency in higher education.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

Anil Madhavapeddyمشاهده پروفایل

استاد

• Planetary Computing
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Anil Madhavapeddy is the Professor of Planetary Computing at the University of Cambridge Computer Laboratory, where he co-leads the Energy & Environment Group and is a member of the Systems Research Group. He is also a Fellow at Pembroke College where he serves as Director of Studies in Computer Science. Madhavapeddy completed his PhD from the University of Cambridge in 2003 and his BEng in Information Systems Engineering from Imperial College in 1999. He holds a JM Keynes Fellowship since 2022 for his work combining computer science with economics, and serves on the management committee of the Cambridge Conservation Initiative where he co-directs 4C (Cambridge Centre for Carbon Credits) and the Centre for Earth Observation. His research spans computer systems and programming languages with a strong focus on applying these technologies to global conservation, biodiversity, and climate change challenges. He leads the OCaml Labs group and has made significant contributions to open-source projects including OCaml, Docker, Xen, and OpenBSD. His work often bridges computer science with environmental science, developing computational approaches to address planetary-scale challenges. Madhavapeddy's recent publications demonstrate a clear trajectory toward integrating programming language research with environmental monitoring and conservation. His work spans from foundational programming language techniques to applied geospatial computing systems, with increasing emphasis on biodiversity measurement, carbon credit systems, and planetary-scale environmental monitoring. JM Keynes Fellowship (2022-present) As an educator, Madhavapeddy teaches undergraduate courses including Foundations of Computer Science, Software & Security Engineering, and Cloud Computing. He mentors MPhil and PhD students and co-founded the award-winning book 'Real World OCaml' (2nd Edition, 2022). He has co-founded several companies including Unikernel Systems, High Energy Magic, Segfault, and Tarides to translate research into real-world impact. Madhavapeddy leads the OCaml Labs group at Cambridge and works closely with the Energy & Environment Group, collaborating with colleagues from Plant Sciences, Zoology, Economics, and NGOs including UNEP-WCMC and the IUCN. His current efforts are primarily focused on conservation technology through partnerships with organizations like Canopy PACT.

Klaus Böhmمشاهده پروفایل

استاد

• Geoinformatics
• Spatial Data Analysis
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Klaus Böhm serves as a Professor at Mainz University of Applied Sciences within the School of Engineering, affiliated with the i3mainz institute (Institute for Spatial Information and Surveying Technology). His research bridges geospatial technologies with artificial intelligence, focusing on practical applications in urban planning, healthcare, and educational environments through projects like BAM (Big Data Analytics) and TOPML (Machine Learning). His primary research domains include geospatial explainable AI (GeoXAI), mixed reality decision support systems, and health informatics applications. He pioneers methods for visualizing uncertainty in AI models, developing geoparsing techniques using LLMs, and creating spatial navigation frameworks for VR environments. His work consistently addresses real-world challenges in elderly mobility, smart city infrastructure, and medical education through spatial analytics. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Integration of XAI with geospatial data for transparent decision-making in urban planning, (2) Development of mixed reality tools for STEM education and dermatology training, and (3) Spatio-temporal correlation analysis for smart city applications like parking optimization and public transport accessibility. His methodology emphasizes interactive visualization of complex spatial relationships and uncertainty quantification. Dr. Böhm leads multiple funded research initiatives including AIMR (AI-based decision support in mixed reality), RAFVINIERT (spatial intelligence for senior care), and FlexGeo (geo-service integration). These projects demonstrate sustained grant acquisition capability across EU and national funding programs, typically involving interdisciplinary teams from computer science, urban planning, and healthcare sectors. As a core member of the i3mainz research institute, he contributes to Germany's geospatial technology ecosystem through collaborative projects with municipal authorities and healthcare providers. The institute's work focuses on translating academic research into operational tools for spatial data infrastructure, particularly in environmental monitoring and public service optimization contexts.