Find Academic Supervisors

• Remote Sensing
• Machine Learning
• Earth Observation
• +3 more

Begüm Demir is a Professor and Head of the Remote Sensing Image Analysis (RSiM) Group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin. Her research focuses on scalable machine learning methods for remote sensing and Earth observation data analysis. Previously, she held positions at the University of Trento, where she was promoted to Associate Professor in 2017 and received an ERC Starting Grant for her BigEarth project. Key research interests include: Deep learning for satellite image analysis Big data processing in geosciences Earth observation data democratization Noise-robust machine learning models Recent projects highlight collaboration with Huawei in wireless communication-EO integration and leadership in initiatives like Agora-EO and TreeSatAI . Awards include the 2018 IEEE GRSS Early Career Award. Grants include funding from the German Research Foundation (DFG) and Federal Ministry of Education and Research (BMBF). Active in editorial roles for IEEE Geoscience and Remote Sensing Letters, and guest editorships in leading journals. Publicly accessible datasets like BigEarthNet and code repositories enhance her contributions to open science.

• Artificial intelligence
• Data compression
• Deep learning
• +15 more

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

Weimin HuangView profile

Professor

• Radar Remote Sensing
• Ocean Remote Sensing
• GNSS Reflectometry
• +3 more

Professor Weimin Huang is a full Professor in the Faculty of Engineering and Applied Science at Memorial University of Newfoundland, where he has served since 2010 and became a full professor in 2019. He held the position of Department Deputy Head from 2020 to 2023. Education: BSc in Radio Physics (Radio Wave Propagation and Antennas), Wuhan University, 1995 MSc in Radio Physics (Radio Wave Propagation and Antennas), Wuhan University, 1997 PhD in Space Physics, Wuhan University, 2001 MEng in Electrical and Computer Engineering, Memorial University of Newfoundland, 2004 Postdoctoral Fellowship in Electrical and Computer Engineering, Memorial University of Newfoundland, 2007 Research Focus: Huang specializes in radar-based ocean remote sensing , with core expertise in high-frequency ground wave radar (HF radar) , GNSS Reflectometry , and synthetic aperture radar (SAR) . His work targets ocean surface parameter mapping including wind speed, oil spills, ship detection, and sea ice monitoring through advanced digital image processing and applied electromagnetics . Recent innovations integrate deep learning (CNNs, physics-informed models) with radar data for enhanced environmental monitoring. Publication Trends: His 2025 publications reveal a strong shift toward AI-driven solutions in remote sensing, with 5 high-impact papers in IEEE TGRS and Remote Sensing focusing on wind speed estimation (using GNSS-R and wavelet-CNN hybrids), oil spill mapping via SAR, ship detection with HF radar, and climate change analysis. These works demonstrate cross-disciplinary integration of machine learning with geophysical remote sensing. Scientific Awards: No awards were documented in the source material. Advising & Collaboration: With 358 co-authors including Bahram Salehi and Biyang Wen, Huang maintains a robust global research network. While specific student supervision isn't listed, his leadership role and publication volume indicate active graduate mentoring. The text mentions no grant details. Research Infrastructure: His work operates within Memorial University's engineering faculty, leveraging radar facilities for ocean sensing. Collaborations span institutions including Wuhan University and SUNY, suggesting participation in international radar remote sensing consortia focused on maritime applications.

• Applied Geophysics
• Glaciology
• Geophysical Data Integration
• +3 more

Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.

Patrizia FERRETTIView profile

Associate Professor

• Stratigraphy
• Sedimentology
• Climate Science
• +3 more

Patrizia Ferretti is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. She holds responsibilities for laboratory teaching and research activities (RDRL) and is affiliated with the Research Institute for Complexity Sicurezza. Her research focuses on stratigraphy, sedimentology, and paleoclimatology, with a particular emphasis on reconstructing past climate and environmental changes using nannofossils, geochemical proxies, and sedimentological analysis. Her work often integrates advanced methodologies like machine learning for ice core particle detection and analytical chemistry for Martian simulant studies. Recent research highlights include studies on the Gelasian Stage boundary in Sicily, climate variability across the Piacenzian/Gelasian boundary, and the application of deep learning to glacial records. She contributes to international collaborations, such as the Ocean Drilling Program and Antarctic expeditions, and has published extensively in journals like Quaternary Science Reviews and Marine Micropaleontology . Her publications reflect a strong focus on orbital forcing mechanisms, Milankovitch cycles, and the Mid-Pleistocene Transition. Dr. Ferretti’s work also extends to methodological advancements, such as automated Mg/Ca determination in calcite and alkenone synthesis for climate proxies. She maintains a laboratory at the Epsilon Building, Campus via Torino, and her research often addresses interdisciplinary questions at the intersection of geology, oceanography, and planetary science.

• Landslide Susceptibility
• Geomorphology
• Natural Hazards
• +3 more

Chiara Martinello is a Researcher in Earth and Marine Sciences at the University of Palermo, Italy, with office hours held Tuesdays 3:00-5:00 PM at Via Archirafi, 20 - 4th floor. Her work focuses on advanced geomorphological analysis of landslide and coastal processes using statistical and machine learning methodologies. Her research spans landslide susceptibility mapping, coastal erosion vulnerability, and earthquake/tropical storm-induced geohazards. She addresses critical methodological challenges including incomplete landslide inventories, cell-size effects in modeling, and integration of preparatory/triggering factors. Fieldwork concentrates on Sicily (Italy) and El Salvador, with emphasis on practical risk assessment applications. Analysis of her 15 most recent publications reveals consistent innovation in statistical landslide modeling, particularly regarding inventory completeness validation and multi-typology susceptibility mapping. Recent work expands into coastal vulnerability indices and anthropogenic impacts on fluvial systems, demonstrating interdisciplinary integration of geospatial data, bathymetry, and hydrological modeling. No scientific awards are documented in available sources. Dr. Martinello supervises Master's theses as indicated by her 'Edited theses' role, though specific student names aren't provided. Research grant details remain undisclosed in public materials, though her extensive publication record suggests active project involvement. She operates within the Earth and Marine Sciences Department's research infrastructure at the University of Palermo, contributing to regional risk assessment initiatives through field data collection, statistical modeling, and validation frameworks for geohazard prediction.

Andrea CannataView profile

Associate Professor

• Seismology
• Volcanology
• Infrasound Analysis
• +7 more

Andrea Cannata is a Full Professor of Solid Earth Geophysics at the University of Catania , Italy. He serves as a key researcher at the National Institute of Geophysics and Volcanology (INGV-OE) , contributing to multidisciplinary studies in volcanology , seismology , and microseism analysis . His career spans institutions like the University of Perugia (2015–2018) and collaborations with global entities such as the Berkeley Seismological Laboratory and NIED, Japan . Education: PhD in Geodynamics and Seismotectonics (University of Catania, 2009), Master’s in Theoretical and Applied Geophysics (University of Pisa, 2004), Bachelor’s in Geological Sciences (University of Catania, 2002). Research Focus: Analysis of seismic and infrasound signals in volcanic environments, magma dynamics, volcano-earthquake interaction, seismic noise interferometry, and machine learning applications in geophysics. Publications highlight his expertise in volcanic tremors , lava fountain monitoring , microseism-storm correlation , and Antarctic seismo-climatic studies . His work spans 75+ ISI JCR papers , book chapters , and scientific reports , with significant contributions to machine learning in volcano monitoring and COVID-19 lockdown seismic noise analysis . Projects include i-waveNET for Mediterranean sea state monitoring and multiparametric studies of Mount Melbourne and Stromboli paroxysms . His Antarctic expeditions (2016–2017) advanced understanding of glacial seismicity and volcanic degassing .

• Machine learning
• Numerical analysis
• Numerical methods
• +10 more

Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.

• Structural Health Monitoring
• Earthquake Engineering
• Satellite Data Integration
• +7 more

Stefania Coccimiglio is a Research Fellow and Research Assistant at the Department of Structural, Geotechnical and Building Engineering (DISEG) at Politecnico di Torino. She also serves as an external lecturer and teaching assistant in the Master's Degree Course in Earthquake Engineering. Her work is centered on structural health monitoring, seismic monitoring, and the integration of satellite data for civil and heritage infrastructure assessment. Her research interests include Structural Health Monitoring , Earthquake Engineering , Satellite Data Integration , Soil-Structure Interaction , Modal Analysis , and Built Heritage Conservation . She applies advanced data fusion techniques and remote sensing to monitor the dynamic behavior of civil structures, particularly monumental and historical buildings. Her recent publications (2021–2025) demonstrate a strong focus on using interferometric satellite data (e.g., Copernicus), in situ measurements, and machine learning for structural monitoring. She frequently publishes in journals such as Scientific Reports , Geosciences , and Bulletin of Earthquake Engineering , and presents at key conferences including the European Workshop on Structural Health Monitoring and the International Brick and Block Masonry Conference. No scientific awards are explicitly mentioned in the provided texts. She has collaborated extensively with researchers such as Rosario Ceravolo, Gaetano Miraglia, Giorgia Coletta, and Erica Lenticchia. Although no formal advisees or grants are listed, her role as a recent doctoral graduate and active researcher suggests ongoing contributions to research teams and projects. She does not appear to lead a lab but is an integral member of research groups focused on structural dynamics and monitoring at PoliTO.

Emanuela Di MartinoView profile

Researcher

• Paleontology
• Paleoecology
• Marine Biology
• +7 more

Emanuela Di Martino is a Researcher at the University of Catania , with significant affiliations at the Natural History Museum, University of Oslo , and the Natural History Museum London . Her work bridges paleontology, marine biology, and evolutionary ecology, focusing on bryozoans as a model for understanding macroevolutionary processes and tropical biodiversity dynamics. Education: PhD in Geosciences, Utrecht University (2014) Master’s in Geological Sciences Applied to Land Management, University of Catania (2008) Bachelor’s in Geology Applied to Land Conservation, University of Catania (2006) Her research interests include: Paleobiodiversity hotspots in tropical regions Allometric and life-history evolution under climate change Paleoenvironmental reconstructions using multitaxon approaches Taxonomy and phylogeny of Mesozoic-to-Recent marine bryozoans Her publication trends emphasize: Long-term evolutionary patterns in bryozoans Macroevolutionary dynamics and diversification Ecological impacts of marine debris Morphometric analysis via AI tools (e.g., DeepBryo) Phylogenetic studies with genome-skimming Competitive outcomes in fossil communities Scientific Awards: 2025 Visiting Professorship at CR2P, Paris 2021 Medaglia Giovanni Merla, Società Paleontologica Italiana 2018 European Commission Seal of Excellence for project ESTIMATE She has led projects like SELECT (Norwegian RCN) and macroevolution.abc (ERC), and holds editorial roles at Journal of Paleontology and Zootaxa . Her work involves collaborations with institutions in Norway, the UK, Spain, and the US, and she contributes to understanding ancient ecosystems through colonial invertebrates.

Alessandra NegriView profile

Professor

• Paleontology
• Paleoecology
• Micropaleontology
• +5 more

Alessandra Negri is a Full Professor in the Department of Life and Environmental Sciences at the Polytechnic University of Marche (UNIVPM) in Italy, specializing in Paleontology and Paleoecology (GEOS-02/A). Her academic work focuses on micropaleontology, particularly the study of foraminifera and their applications in paleoenvironmental reconstructions. Professor Negri's research spans multiple areas of paleontological investigation, with a strong emphasis on Mediterranean geological history. Her work frequently examines the Messinian Salinity Crisis, sapropel formation events, and paleoenvironmental changes throughout the Neogene and Quaternary periods. She has developed expertise in using foraminifera as bioindicators for both ancient and modern environmental conditions, including studies on how these microorganisms respond to anthropogenic pollution such as cigarette butt contaminants. Her publication record demonstrates a consistent research trajectory focused on Mediterranean paleoceanography, with recent work increasingly incorporating interdisciplinary approaches including machine learning applications for foraminifera classification and analysis of deoxygenation events as analogues for contemporary climate change impacts. Professor Negri's research bridges traditional paleontological methods with modern environmental concerns, creating valuable connections between deep-time geological records and current anthropogenic challenges. Professor Negri maintains active research collaborations across Mediterranean institutions and has contributed significantly to understanding the region's complex geological history. Her work on the Messinian period, Pleistocene climate transitions, and modern applications of paleontological data positions her as a key contributor to Mediterranean earth sciences research.

Fabio Dell'AcquaView profile

Professor

• Remote Sensing
• Telecommunications
• Satellite Data Fusion
• +3 more

Prof. Fabio Dell'Acqua is a Full Professor at the Department of Industrial and Information Engineering , University of Pavia , Italy. His academic activities span remote sensing, telecommunications, and data fusion for risk assessment. Expertise : Satellite data processing, agricultural monitoring, urban change detection Projects : ESA ARTES Demonstration Project "Saturnalia", FabSpace 2.0 educational initiatives Research Focus : He investigates Earth observation applications through: Advanced machine learning for SAR and Hyperspectral RS data Time series analysis of agricultural landscapes Band selection strategies for remote sensing systems Publication Trends : Dell'Acqua's recent work integrates multispectral satellite data with neuromorphic coding , focusing on: Energy-efficient communication in distributed networks Vineyard biomass monitoring via optical-SAR fusion Manure application detection for environmental compliance Academic Roles : Active contributor to ESA projects and IEEE Geoscience initiatives, with teaching responsibilities in Geospatial Data Processing courses.

• Signal Processing
• Synthetic Aperture Radar (SAR)
• GNSS Reflectometry (GNSS-R)
• +4 more

Maurizio Di Bisceglie is an Associate Professor at the Department of Engineering (DING) of Università degli Studi del Sannio, Italy. He is an expert in signal processing for remote sensing applications, particularly in GNSS Reflectometry (GNSS-R) and Synthetic Aperture Radar (SAR) systems. His research focuses on: Advanced CFAR detection algorithms for extended targets in non-Gaussian clutter GNSS-R data analysis for ocean wind speed , altimetry , and inland water monitoring Statistical modeling of sea surface scattering and clutter characteristics Development of noise reduction and image enhancement techniques in SAR interferograms Applications of multi-spectral satellite data for environmental monitoring His recent publications show a trend toward spaceborne GNSS-R applications for coastal and flood monitoring , with a strong emphasis on machine learning and stochastic modeling . Key journals include IEEE Transactions on Geoscience and Remote Sensing and IEEE International Geoscience and Remote Sensing Symposium proceedings. He teaches Numerical Signal Processing and Sensors for Earth Observation at the University of Sannio. His work has been published in IEEE and other leading technical journals, with collaborations across Italy and international institutions.

• Fluvial Geomorphology
• Sediment Transport
• Flood Risk Assessment
• +4 more

Dr. Vittoria Scorpio is a tenured Associate Professor at the Department of Chemical and Geological Sciences , University of Modena and Reggio Emilia (Italy). She teaches Hydraulic Risk and Geographic Information Systems (GIS) and Digital Cartography courses within the Geosciences, Georisks and Georesources and Geological Sciences degree programs. Her work bridges fluvial geomorphology with environmental sustainability , focusing on sediment transport dynamics , flood hazard modeling , and river restoration . Hydraulic Risk course integrates fluvial geomorphology , flood mitigation , and IDRAIM methodology GIS and Digital Cartography emphasizes QGIS software , geospatial analysis , and digital mapping Her research spans multi-decadal river evolution in the Alps and Apennines , examining anthropogenic impacts (gravel mining, channelization, dams) and natural drivers (climate change, tectonics). Key studies include extreme sediment transport during the 2022 Misa River flood , debris flow connectivity in the Dolomites , and post-Little Ice Age river adjustments . She employs multi-temporal GIS analysis , field surveys , and international collaborations across Italy, Austria, and Brazil. Recent publications in Earth Surface Processes and Landforms and Geomorphology analyze sediment connectivity in proglacial streams , anthropogenic sediment imbalance , and bio-geomorphological river restoration . Her work informs flood hazard modeling and sustainable river management frameworks, particularly in highly modified alpine rivers .

Filippo StancoView profile

Professor

• Image Processing
• Computer Vision
• Machine Learning
• +7 more

Filippo Stanco is a Full Professor at the Department of Mathematics and Computer Science at the University of Catania. He serves as President of the Bachelor's Degree in Computer Science (since 2017) and Deputy Rector for Technological Innovation (since 2019). His career includes positions as Associate Professor (2014-2023), Assistant Professor (2006-2014), and research fellowships at the Universities of Catania and Trieste. Education: PhD in Computer Science (2003) from University of Catania Bachelor's Degree in Computer Science (1999, summa cum laude) from University of Catania His research spans image processing, computer vision, and machine learning with applications in cultural heritage preservation, medical imaging, industrial automation, and multimedia systems. He leads the Archeomatica Project developing digital tools for archaeology and coordinates multiple national research projects including DREAMIN and CLEAR. Stanco has published extensively on food image analysis, document authentication, video processing, and heritage conservation. His work frequently employs deep learning, 3D modeling, and color science techniques across interdisciplinary domains. Honors: Best Session Paper Award at IPAS 2020 Cookpad Student Travel Grant at CEA 2018 Meritorious Papers recognition in Computers in Biology and Medicine (2016) He coordinates the Archeomatica research group and serves on editorial boards for multiple journals including ACM Journal of Computing and Cultural Heritage and IET Image Processing. Stanco has supervised over 200 theses and teaches courses on multimedia systems, game development, and image processing.