جست‌وجوی استاد و پژوهشگر

Chad Babcockمشاهده پروفایل

استادیار

• Remote Sensing
• Forest Inventory
• Geospatial Analysis
• +۳ مورد دیگر

Chad Babcock serves as an Assistant Professor at the University of Minnesota, specializing in the integration of remote sensing technologies with geostatistical methodologies for natural resource management. His work focuses on developing statistically rigorous spatial and spatio-temporal analysis frameworks to address critical challenges in forest inventory, environmental monitoring, and sustainable resource management through interdisciplinary collaboration. Education background: PhD in Forest Sciences from University of Washington (2017) MS in Applied Statistics from Michigan State University (2014) MS in Geography from Michigan State University (2014) BS in Forest Management from Michigan State University (2009) His research program centers on advancing geostatistical approaches using remotely sensed data to predict natural resource characteristics. Key focus areas include Bayesian statistical modeling for spatial uncertainty quantification, forest inventory system optimization, and the development of spatio-temporal frameworks for environmental change detection. This work bridges forestry, geospatial science, and statistics to create practical solutions for resource monitoring and management. Dr. Babcock teaches courses in Remote Sensing and Geospatial Analysis of Natural Resources and Environment, emphasizing methodological innovation and real-world application. His collaborative research approach engages scientists across ecology, remote sensing, and statistics to tackle complex environmental challenges through data-driven solutions.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Lu Ganمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Compressed Sensing
• Machine Learning
• +۱۰ مورد دیگر

Dr. Lu Gan is a Senior Lecturer in the Department of Electronic and Computer Engineering at Brunel University London, within the College of Engineering, Design, and Physical Sciences. She earned her B.Eng and M.Eng in Electronic and Information Engineering from Southeast University, China, in 1998 and 2000, followed by a Ph.D. in Information Engineering from Nanyang Technological University, Singapore, in 2004. Before joining Brunel in 2008, she held faculty positions at the University of Newcastle, Australia (2004-2006) and the University of Liverpool, UK (2006-2007). Her research spans fundamental signal processing theories, machine learning, and applications in image/video coding, non-destructive terahertz/ultrasound imaging, wireless communications, and sparse antenna arrays. Education: B.Eng (Electronic and Information Engineering), Southeast University, China (1998) M.Eng (Electronic and Information Engineering), Southeast University, China (2000) Ph.D (Information Engineering), Nanyang Technological University, Singapore (2004) Her research focuses on structured sparse signal processing for infrared/terahertz systems, super-resolution in non-destructive imaging, deep learning for terahertz data, non-orthogonal pilot design for 5G systems, and separation of singing voice from music. She has secured funding from EPSRC, Innovate UK, BBSRC, UK Atomic Energy Authority, and TWI. She actively contributes to academic service as an Associate Editor for IEEE Signal Processing Letters, IEEE Transactions on Circuits and Systems-I, and as a Meta Reviewer for ICASSP 2025. Her work has been recognized with a Best Paper Award from the Journal of The British Blockchain Association in 2022 and a Gold Medal at the IEEE Audio and Acoustic Signal Processing Challenge (DCASE) in 2022. Dr. Gan also serves as a reviewer for top journals like IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing, and participates in grant panels for the Royal Society and EPSRC. Recent publications emphasize cross-domain speech enhancement architectures, terahertz data reconstruction via spatio-temporal dictionary learning, and coprime array designs using Chinese remaindering over quadratic fields. Her work bridges compressive sensing, lattice structures, and practical applications in healthcare and communication systems. She is a Senior Member of IEEE, Fellow of the Higher Education Academy (UK), and actively contributes to departmental leadership as Course Director for MSc Wireless Communication Systems, Level 3 Coordinator, and Social Media Administrator.

• Geoinformatics
• Geographic Information Systems
• Spatial Databases
• +۵ مورد دیگر

Assoc. Prof. Renata Ďuračiová, PhD., serves as Associate Professor and Head of the Department of Global Geodesy and Geoinformatics at the Faculty of Civil Engineering, Slovak University of Technology in Bratislava. Her academic role centers on advancing geoinformatics through rigorous research in spatial data quality, uncertainty modeling, and mathematical GIS methodologies while leading departmental operations and curriculum development. Her research spans spatial databases, geospatial analyses, and environmental applications including Lidar processing, building footprint detection, and solar radiation modeling. Key contributions address bark beetle infestation prediction using satellite data and machine learning, alongside innovations in fuzzy logic for spatial database querying and heterogeneous data integration. Her work bridges theoretical geoinformatics with practical solutions for forest management and urban planning challenges. Analysis of her 2017-2025 publications reveals consistent focus on spatial data quality assessment, terrain parameter effects on environmental modeling, and algorithmic development for Lidar/satellite data processing. Recurring themes include uncertainty quantification in spatial analyses, cross-source data integration (e.g., OpenStreetMap/INSPIRE), and climate-driven ecological modeling. Her technical expertise manifests in specialized tools like point-cloud solar radiation calculators and predictive models for forest disturbances. Prof. Ďuračiová actively contributes to the geospatial community through leadership roles in the Cartographic Society of the Slovak Republic (Executive Committee since 2011) and Slovak Association for Geoinformatics (Presidency since 2011). Her professional engagement emphasizes advancing geoinformatics standards and applications within Central European contexts while mentoring through courses like Database Systems in GIS and Geodata Quality Analysis.

• Machine Learning
• Deep Learning
• Bioinformatics
• +۶ مورد دیگر

Wolf Beat is an Associate Professor at the Fribourg School of Engineering and Architecture, part of the University of Applied Sciences of Western Switzerland (HES-SO). He is affiliated with the Institute of Complex Systems (iCoSys) where he conducts interdisciplinary research at the intersection of machine learning, bioinformatics, and document analysis. His educational background includes: BA HES-SO in Architecture from Fribourg School of Engineering and Architecture MSc HES-SO in Business Administration MSc HES-SO in Engineering PhD in "Reducing the complexity of OMICS data analysis" from University of Würzburg (2017) Dr. Beat's research spans multiple disciplines with a focus on applying machine learning techniques to solve complex problems. His work in bioinformatics has contributed to advancements in DNA sequencing analysis and genomic research, while his more recent work explores time series forecasting, document analysis, and anomaly detection. He has developed innovative approaches for handling historical manuscripts through computer vision techniques and has applied machine learning to geotechnical engineering problems. His research often bridges theoretical machine learning concepts with practical industrial applications. An analysis of his recent publications reveals a strong trend toward foundation models and diffusion models for time series analysis, alongside continued work in bioinformatics and document analysis. His research demonstrates a consistent pattern of applying cutting-edge AI techniques to domain-specific problems across diverse fields including healthcare, engineering, and digital humanities. Dr. Beat is actively involved in multiple research projects including ModIA (2024-2025, 15,000 CHF) and as principal investigator for the Foundation model for time series forecasting project (2024-2025, 100,000 CHF). His past projects include Monitoring des Transformateurs de Puissance (2023-2024, 220,000 CHF) and GREENum (2022, 24,700 CHF), demonstrating his ability to secure funding for interdisciplinary research. He collaborates extensively within the HES-SO network and with external partners, often serving as a co-researcher or principal investigator on projects that combine expertise from engineering, computer science, and domain-specific applications. His research team at iCoSys Institute includes multiple researchers working on AI and complex systems, and he has been involved in numerous projects with colleagues including Montet Frédéric, Pasquier Benjamin, Von Barnekow Alec, and Maillard Philippe. His work demonstrates a consistent commitment to developing practical AI solutions that address real-world challenges across multiple domains.

• Data Management
• Mobile Data Management
• Sensor Networks
• +۹ مورد دیگر

Demetris Zeinalipour is a Professor of Computer Science at the University of Cyprus, where he founded and directs the Data Management Systems Laboratory (DMSL). He holds a Ph.D. (2005) and M.Sc. (2003) in Computer Science and Engineering from the University of California - Riverside, CA, USA and a B.Sc. in Computer Science from the University of Cyprus (2000). His career includes research positions at Akamai Technologies, University of Athens (as a Marie-Curie Fellow), University of Pittsburgh, and the Max Planck Institute for Informatics (as a Humboldt Fellow). He is an ACM Distinguished Speaker (2017-2020), a Senior Member of ACM and IEEE, and serves on the editorial boards of ACM Transactions on Spatial Algorithms and Systems and Distributed and Parallel Databases. His primary research spans Data Management in Computer Systems and Networks, with expertise in Mobile, Sensor and Spatio-Temporal Data Management; Big Data Management in Parallel and Distributed Architectures; Network, Blockchain and Telco Data Management; Crowd, Web 2.0 and Indoor Data Management; Data Privacy Management; Data Management for Sustainability; and AI and Data Management. His work bridges theoretical foundations with practical applications, resulting in numerous systems including Anyplace Indoor Information Service, ChatUCY, GreenCap, EcoCharge, and Triabase. His research has evolved from early work in sensor networks to current focus on sustainable computing applications addressing climate change challenges. Professor Zeinalipour's publication record shows a strong trajectory toward sustainability-focused data management, with recent work on energy management for smart homes, electric vehicle charging, and solar self-consumption. His research integrates multiple disciplines including spatial databases, mobile computing, IoT systems, and blockchain technologies, with increasing emphasis on addressing real-world environmental challenges through computing solutions. Scientific Recognition IEEE ICDE'24 Outstanding Reviewer Award IEEE MDM'21 Best App Paper Award IEEE MDM'18 Best of MDM Award and Best Demo Award Humboldt Fellow (2016) ACM Distinguished Speaker (2017-2020) ACM and IEEE Senior Member status He has advised numerous PhD students and postdocs, many of whom have established successful careers in both academia and industry. His laboratory maintains strong industry connections and contributes to open-source development. Professor Zeinalipour is deeply committed to addressing real-world challenges through computing research, with recent work focusing on climate change mitigation and sustainable energy management solutions.

Alexander Pfaffمشاهده پروفایل

استاد

• Environmental Economics
• Conservation Policy
• Deforestation
• +۳ مورد دیگر

Alexander Pfaff is the Bernard Rhodes Distinguished Professor of Public Policy at Duke University, with secondary appointments in the Department of Economics and Division of Environmental Social Systems. He is affiliated with the Duke Center for International Development, Duke Initiative for Science & Society, and DuPRI's Population Research Center. His research focuses on the interplay between economic development and environmental conservation, particularly in tropical forests. He examines how conservation policies like Protected Areas Payments for Ecosystem Services (PES) Certification Programs interact with development policies (roads, land rights) to influence land use, deforestation, and human exposures to environmental risks. Key trends in his recent publications include Evaluation of conservation policy effectiveness Behavioral spillovers from environmental incentives Carbon sequestration through tropical forest protection Spillover effects in deforestation Policy design for sustainable resource use Scientific awards include Best Paper Award, Society of Risk Analysis (2011) Duke’s Bernard Rhodes Distinguished Professor (2025) TBJ’s Leaders in Diversity Lifetime Achievement Award (2020)

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +۶ مورد دیگر

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.

Kateryna Morozovskaمشاهده پروفایل

پژوهشگر

• Computational Methods for Energy Systems
• Physics-Informed Machine Learning
• Renewable Energy Integration
• +۳ مورد دیگر

Kateryna Morozovska is a Researcher at the KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems and the School of Electrical Engineering and Computer Science . Her work bridges computational methods and energy systems development, focusing on physics-informed machine learning for renewable energy integration and power transformer optimization. Education B.S. and M.S. in Electrical Mechanics from Zaporizhzhya National Technical University (2013) European Energy Masters program with mobility at DTU (Denmark), TU Delft (Netherlands), and NTNU (Norway) PhD in Electrical Engineering from KTH (2020) on 'Dynamic rating for applications in renewable energy' Licentiate from KTH (2019) Research Focus Kateryna's research emphasizes Physics-Informed Neural Networks (PINNs) for power system optimization, including transformer thermal modeling, cellulose degradation analysis, and renewable energy integration. Her projects explore dynamic rating techniques to enhance grid efficiency and sustainability, funded by Vinnova and applied in PV-power plants and wind farms. Scientific Contributions She has developed frameworks for transformer cost analysis, wind farm sizing, and sensor placement optimization using PINNs and MILP. Her work addresses environmental trade-offs in wind energy, such as raw material mining impacts, and investigates thermodynamic challenges in nanocellulose and power systems. Affiliations Kateryna collaborates with industry partners through the PINN Summer School and contributes to SweGRIDS and Mendeley communities. Her teaching includes hands-on PINN training and multi-GPU machine learning.

Mirjana Bevandaمشاهده پروفایل

پژوهشگر

• Landscape Ecology
• Spatial Data Analysis
• GIS and Remote Sensing
• +۴ مورد دیگر

Dr. Mirjana Bevanda is a PostDoc researcher at the University of Würzburg, affiliated with the Chair of Remote Sensing within the Faculty of Philosophy. Her work bridges ecological research with remote sensing and GIS technologies, focusing on spatio-temporal dynamics of animal movement and biodiversity conservation. Education: PhD (2015) in Biology from University of Bayreuth; Diploma (2008) in Biology from Phillips University Marburg and Julius Maximilians University Würzburg. Research Trends: Her publications highlight the integration of remote sensing data with ecological models to analyze animal movement patterns, habitat connectivity, and biodiversity conservation. Key methodologies include spatial data analysis, fractional land cover assessments, and interdisciplinary training frameworks. Grants & Projects: She has contributed to initiatives funded by the German Federal Ministry of Education and Research (BMBF), including the WASCAL DE COOP project addressing climate change adaptation in West Africa.

Ben Spycherمشاهده پروفایل

دانشیار

• Childhood Cancer Epidemiology
• Pediatric Respiratory Epidemiology
• Spatial Epidemiology
• +۳ مورد دیگر

Ben Spycher serves as Associate Professor and Head of Research Group at the Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland. He leads the Environmental & Spatial Epidemiology group while contributing to the Paediatric Cancer Epidemiology and Pediatric Respiratory Epidemiology groups. His academic credentials include: PhD in Biostatistics/Epidemiology, University of Bern (2006-2010) MSc in Agricultural Economics, ETH Zurich (1992-1997) BSc in Statistics, Freie Universität Berlin (2002-2005) Dr. Spycher's research centers on childhood cancer epidemiology (spatial clustering, population mixing, risk factors) and pediatric respiratory disorders (phenotypes, natural history), employing advanced spatial statistics and cohort study methodologies. His work bridges environmental exposure assessment with clinical outcomes through rigorous epidemiological frameworks. Recent publications (2024-2025) reveal strong thematic continuity in spatial cancer epidemiology (UV radiation, transformer proximity), pediatric growth/obesity metrics, and respiratory phenotyping, while expanding into methodological innovations for missing data and non-Gaussian modeling. The SwissPedHealth initiative demonstrates his leadership in national data infrastructure development. His career progression shows consistent institutional commitment: Research Fellow at ISPM (2012-2025), Postdoctoral Fellow at University of Bristol (2010-2011), and current Associate Professor since 2025. Fieldwork spans Switzerland, UK, and international collaborations including Papua New Guinea.

Carles Bona Garciaمشاهده پروفایل

استاد

• Numerical Relativity
• Theoretical Physics
• General Relativity
• +۳ مورد دیگر

Dr. Carles Bona Garcia serves as a Full Professor in the Department of Physics at the University of the Balearic Islands (UIB), Palma de Mallorca, Spain, where he maintains an active research and teaching profile. His academic home is the Mateu Orfila i Rotger Physics building (Room F313), and he is the principal investigator for the Gravitation, Relativistic Astrophysics and Computing (GRAC) research group—a consolidated R&D unit at UIB. His research spans theoretical and computational physics with emphasis on: Numerical relativity and high-performance computing frameworks General relativity, cosmology, and gravitational wave modeling Complexity theory in dynamical systems Development of scientific simulation platforms (notably the Simflowny ecosystem) Analytical mechanics and numerical differentiation techniques Professor Bona Garcia's publication trajectory reveals a consistent focus on advancing computational methodologies for Einstein's equations while bridging theoretical physics with practical applications. His work on Simflowny (versions 2-3) demonstrates cross-disciplinary impact, extending from numerical relativity to biomedical fluid dynamics. Recent publications emphasize cosmological modeling, black hole physics, and complexity metrics in spatio-temporal systems, reflecting both depth in gravitational physics and breadth across computational science. He actively teaches Analytical Mechanics for the Physics degree program (2020-2025), maintaining structured office hours requiring student appointments. His scholarly presence is documented through ORCID, ResearcherID, Scopus, and Dialnet profiles, underscoring his integration into the international physics community.

• Evolutionary Ecology
• Quantitative Genetics
• Population Dynamics
• +۴ مورد دیگر

Paul Acker is an evolutionary ecologist at the Norwegian University of Science and Technology (NTNU), focusing on how biodiversity responds to environmental change. His work integrates empirical studies with conceptual frameworks to understand interactions between natural selection, phenotypic plasticity, and genetic variation in wild populations. Affiliation: Department of Biology, NTNU Projects: Spatio-Seasonal Eco-Evolutionary Dynamics (ERC-funded), Early-life eco-evolutionary dynamics of migration (NFR-funded), Centre for Biodiversity Dynamics (CBD) Research interests center on eco-evolutionary responses to environmental variability, with key traits including: Seasonal migration Survival and reproduction strategies Phenotypic plasticity in discrete traits Quantitative genetic analysis of threshold traits Population dynamics under climate change His methodology combines Bayesian statistics, capture-recapture models, and quantitative genetics to analyze long-term ecological monitoring data. Teaching includes PhD courses on: Quantitative genetics in wild populations (BI8091) Natural selection analysis via Bayesian capture-recapture models Plasticity in adaptive evolution (BI8083) He previously taught capture-recapture methodologies at the University of Aberdeen. Supervision involves co-guiding five PhD students across institutions in Western Britanny, NTNU, and Montpellier, focusing on migration phenology, plumage trait selection, and metapopulation dynamics. He has supervised eight past master's students. Scientific awards include ERC Advanced Grant (via Prof. Jane Reid) SQUID Student Fellowship ESEB Hewitt Mobility Award Mobility grants from UBO and ISBLUE Collaborations span institutions in UK (University of Aberdeen), France (Montpellier SupAgro), and Norway (NTNU, CBD). He contributes to EURING, ESEB, and BOU conferences.

Nataša Djurdjevac Conradمشاهده پروفایل

پژوهشگر

• Agent-Based Modeling
• Stochastic Processes
• Network Analysis
• +۷ مورد دیگر

Dr. Nataša Djurdjevac Conrad is the Head of the 'Computational Humanities' research group and Deputy Head of the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB) since 2022. Her work bridges mathematics, archaeology, and social science through data-driven modeling of spatio-temporal processes. Research Focus : Modeling opinion dynamics, innovation dissemination (e.g., Roman road activation in ancient Tunisia), and epidemic spreading (MODUS-COVID project). Methodologies : Expertise in agent-based models (ABMs), stochastic metapopulation models, ODE/SDE compartmental models, and network analysis techniques like Galerkin projection and random-walk-based clustering. Awards : 'Paper of the Semester' award (2023) by MATH+ Activity Group 'Mathematics of Data Science' Corresponding member of the German Archaeological Institute (DAI) since 2022 Projects : Leads interdisciplinary work on cultural dispersion, epidemic policy design, and ancient innovation spreading.

• Precision Horticulture
• Spectro-optical Measurement Methods
• LiDAR and Hyperspectral Sensing
• +۴ مورد دیگر

Manuela Zude-Sasse is a Research Professor at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) in Potsdam, Germany, and former Professor at Beuth University of Applied Sciences Berlin (2009–2017). As Working Group Leader for Precision Horticulture, she focuses on spectro-optical measurement methods (LiDAR, hyperspectral analysis) for fruit quality assessment, canopy modeling, and data science applications in bioeconomy. M.Sc. in Chemistry/International Agronomy (1996, TU Berlin) Ph.D. in Horticulture summa cum laude (1999, TU Berlin) Habilitation in Applied Plant Physiology (2004, Humboldt University) Her research spans Precision Fruit Production , Plant Phenotyping , and 3D Sensor Integration , with projects like CrackSense (fruit cracking prediction) and horDIGrow (resource-efficient horticulture). She employs LiDAR, hyperspectral imaging, and time-resolved fluorescence to analyze temperate and tropical fruits. Recent publications in Plant Phenomics and Horticulturae demonstrate her work on spatial chlorophyll estimation via LiDAR and fruit water stress indices. Her studies in the Journal of Food Engineering (2024) and Postharvest Biology and Technology (2023) highlight machine learning applications for ripeness detection and 3D canopy analysis. Technology Transfer Award (State of Brandenburg, 2003) Silver Medal for Innovation (Agritechnica, 2017) She leads the SMART Farming Technology Research Center (SFTRC) collaboration in Malaysia and serves on editorial boards for Plant Phenomics , International Agrophysics , and Biosystems Engineering . Her 3D point cloud methodologies enable non-contact fruit biomechanics and nutrient management studies.