Find Academic Supervisors

Sergii SkakunView profile

Associate Professor

• Remote Sensing
• Agricultural Monitoring
• Machine Learning
• +4 more

Sergii Skakun is an Associate Professor at the University of Maryland, College Park with a joint appointment in the Department of Geographical Sciences and the College of Information Studies (iSchool). He holds a PhD in Computer Science from the National Academy of Sciences of Ukraine (2005) and an M.S. in Applied Mathematics from Kyiv Polytechnic Institute (2004). His research focuses on advancing remote sensing data fusion, machine learning applications for Earth observation, and agricultural monitoring. Skakun has led projects funded by NASA, NSF, and international agencies, including initiatives on war-induced agricultural damage mapping and climate change impacts in conflict zones. Education: PhD in Computer Science, National Academy of Sciences of Ukraine (2005) M.S. (Hons.) Applied Mathematics, Kyiv Polytechnic Institute (2004) B.S. Applied Mathematics, Kyiv Polytechnic Institute (2002) Research Interests: Remote sensing of land cover/use change Machine learning in geospatial analysis Disaster monitoring and conflict zone agriculture Earth observation for food security His work has produced over 70 peer-reviewed papers and collaborations with NASA Harvest, CEOS, and international teams. Notable recent projects include artillery crater mapping in Ukraine and climate analog velocity estimation using optical flow. Grants & Leadership: Two-time NASA FINESST grant recipient (2021–2025) PI for NASA Rapid Response projects on Ukraine’s agricultural monitoring Co-leads CEOS Cloud Masking Inter-comparison Exercise (CMIX) Labs & Teams: Active contributor to NASA Harvest and Terrestrial Information Systems Laboratory (NASA Goddard), focusing on operationalizing satellite intelligence for global food security challenges.

• Remote Sensing
• Deep Learning
• Land Use Dynamics
• +5 more

Robert Masolele is a Researcher at the Laboratory of Geo-information Science and Remote Sensing , part of Wageningen University . His work focuses on leveraging Earth Observation (EO) and artificial intelligence (AI) to analyze land use dynamics, deforestation drivers, and forest carbon emissions. He holds a Ph.D. in Remote Sensing and Deep Learning (part of CIFOR's Global Comparative Study on REDD+) and an M.Sc. in Geo-information Science and Earth Observation from University of Twente . With +3 years of experience in forest resource assessment for the Tanzanian Forest Service, his expertise spans spatial statistical modeling , machine learning , and deep learning applications in environmental problems. Research Interests: - Land use characterization and dynamics using satellite time series (Sentinel-1, Sentinel-2, Landsat) - Deforestation monitoring and proximate drivers analysis - AI-driven approaches for automated land use classification - Carbon stock assessment in tropical forests - REDD+ compliance and policy support through transparent monitoring frameworks. Key Projects: Pan-tropical land use classification using spatio-temporal deep learning (2021) Deforestation drivers in the Democratic Republic of Congo (2024) Miombo woodlands species modeling in Tanzania (2024) Peat forest disturbances in the Cuvette Centrale (2024) Publications Trends: Recent work emphasizes integrating AI with high-resolution EO data to address deforestation and land use change challenges. His 2021 study on pan-tropical land use classification established the efficacy of spatio-temporal models, while 2024 publications highlight artisanal mining impacts and peat forest conservation. Labs/Teams: Core member of the Remote Sensing and REDD+ Monitoring Group at Wageningen, collaborating with CIFOR and global initiatives like the Global Comparative Study on REDD+.

Alex PoultonView profile

Professor

• Marine Ecology
• Ocean Biogeochemistry
• Plankton Ecology
• +3 more

Alex Poulton is a Professor of Plankton Ecology and Biogeochemistry at Heriot Watt University's The Lyell Centre for Earth and Marine Sciences and Technology in Edinburgh. He holds a PhD in Biological Oceanography from the University of Southampton and previously worked at the National Oceanography Centre (NOC) as a Senior Researcher and Principal Researcher. His research focuses on marine ecology, ocean biogeochemistry, plankton diversity, and carbon cycling, with a particular emphasis on coccolithophores and their role in climate change. Current projects include studying carbon export via internal tides (CarTRidge), coccolithophore controls on ocean alkalinity (CHALKY), and the impacts of bottom trawling on seabed carbon storage. He has participated in 20 research cruises across major oceans and published over 115 peer-reviewed articles. Poulton is an editorial board member for the Journal of Plankton Research and contributes to NERC peer-review processes. His research interests encompass global plankton ecology, nutrient dynamics, and the interplay between biodiversity and biogeochemical processes. Recent work highlights include investigating Southern Ocean carbon flux dynamics, diatom vertical distribution patterns, and the ecological impacts of climate change on marine systems. Poulton also explores novel scientific paradigms, such as the adaptation of fossilized coccolithophores to environmental shifts ('swimming with dinosaurs').

• Graph Neural Networks
• Recommendation Systems
• Image Processing
• +4 more

Shichao Zhang is a Professor at Qingdao University's College of Computer Science and Technology, Remote Sensing Information and Digital Earth Center. His research spans Graph Neural Networks, Recommendation Systems, Image Processing, and Quantum Machine Learning. Research Trends & Fields: His recent work focuses on Contrastive Learning and Fair Graph Representations Quantum Algorithms for Noisy Data Classification Advanced Hashing Techniques for Cross-Modal Retrieval Multi-Causal Analysis in Recommender Systems Transformer-Based Models for Vision Tasks Long-Tailed Data Handling in Image Classification

Kashinath BasuView profile

Senior Lecturer

• Network Virtualization
• Cloud Computing
• AI/ML in Networking
• +7 more

Dr. Kashinath Basu is a Senior Lecturer in Computer Science at Oxford Brookes University, affiliated with the School of Engineering, Computing and Mathematics. He holds a PhD in Computer Science and specializes in network virtualization, cloud computing, AI/ML for infrastructure management, and smart grid communication. His research explores SDN/NFV, cybersecurity frameworks, and quantum-resistant cryptography. Education: PhD in Computer Science, with academic and professional experience spanning over two decades. Research focuses on next-gen networks, cloud orchestration, and AI-driven security solutions. Current projects include softwarization of networks, smart grid communication protocols, and QoS-aware cloud services. Research interests span from foundational network architecture design to applied cybersecurity in smart cities. He has authored influential papers on topics like SD-WAN traffic prediction using ML, QoS-aware service selection in cloud environments, and control frameworks for smart grids. His work bridges theoretical network research with real-world infrastructure challenges. Professional contributions include editorial roles for journals like IEEE Transactions on Smart Grid and active participation in IEEE Working Groups on SDN/Quantum Communication. He has organized major conferences such as IEEE MobileCloud and ITNAC, reflecting his leadership in advancing network technologies. Labs/Teams: Member of the Cloud Computing and Cybersecurity Research Group, Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, and the Dependable Systems Engineering Research Centre. Collaborates with industry partners on projects related to autonomous cyber defense and blockchain for smart city infrastructure.

Badr-Eddine Boudriki SemlaliView profile

Researcher

• Remote Sensing
• Big Data Analytics
• Machine Learning
• +6 more

Badr-Eddine Boudriki Semlali, born in 1992 in Tangier, Morocco, serves as a Researcher at the Department of Computer Sciences within the Faculty of Sciences and Techniques of Tangier (FSTT) at Abdelmalek Essaâdi University, while concurrently pursuing his Ph.D. in the RSLab of the Signal Theory and Communications department at the Polytechnic University of Catalonia (UPC) in Barcelona, Spain. He completed both his Bachelor's and Master's degrees in Computer Sciences Engineering at FSTT in 2017. His research pioneers big data analytics architectures for remotely sensed earth observation, targeting critical environmental challenges including air pollution monitoring, forest fire detection, climate change modeling, ionospheric scintillation analysis, and earthquake prediction. Technical proficiencies span Linux/Windows systems, network/database administration, distributed/embedded systems programming, cloud computing, HPC, and machine learning for classification tasks. His contributions have been recognized through prestigious international fellowships: Erasmus+ KA107 Fellowship (awarded twice) VLIR-UOS Fellowship Campus MARE NOSTRUM Fellowship of Murcia He actively engages in pedagogical activities involving teaching and student supervision, serves as a reviewer for the IJEMA journal, and maintains memberships in IEEE Young Professional and ASAF.

• Natural Resource Management
• Land-Water Systems Interaction
• Remote Sensing
• +7 more

Associate Professor In-Young Yeo is a distinguished academic at the University of Newcastle's School of Engineering, where she serves as an Associate Professor in Civil, Surveying, and Environmental Engineering. With over 15 years of academic experience, she previously held positions at Ohio State University, Cornell University, and University of Maryland. Her work focuses on the critical intersection between landscapes and water resources, using sophisticated remote sensing and modeling tools to improve natural resource management and sustainability. Her research spans the nexus of land and water systems where human and physical systems interact. She pioneers integrative approaches using remote sensing, in-situ data, and process-based models to understand emergent land surface properties and hydrologic processes. Her work addresses water cycle changes across scales, soil moisture-stream flow connectivity, soil and water quality implications of conservation practices, and optimal land use management strategies for environmental sustainability under climate change pressures. Professor Yeo's publication record demonstrates a clear trajectory toward more sophisticated multi-sensor integration approaches and practical applications for agricultural water management. Her recent work increasingly combines machine learning with traditional remote sensing methods, focusing on soil moisture profiling, wetland dynamics monitoring, and developing integrated systems for resource management decision support. Her scientific contributions have been recognized with numerous prestigious awards: 2024 Best Research Paper of 2024, The Soil and Water Conservation Society 2021 Science Note - USDA NRCS Conservation Effects Assessment Project 2018 International Research Visiting Fellowship, University of Newcastle 2017 Women in Research Fellowship, University of Newcastle 2009 Editor's Highlights - AGU Geophysical Research Letters Professor Yeo actively mentors graduate students and offers PhD scholarship opportunities focused on land surface variables and ecosystem health monitoring. She has secured significant research funding from diverse sources including ARC, NASA, NOAA, USDA, Soil CRC, and Sydney Water. Her collaborative approach has led to leadership roles in international research initiatives including NASA-LCLUC and NASA-GRACE programs, the G20 initiative for agricultural monitoring, and the CRC for High Performance Soils. She leads a dynamic research team collaborating with Soil CRC, CSIRO, Australian Universities, and international space agencies. Her work with the US Conservation Effects Assessment Project (CEAP) and US Environmental Protection Agency has been particularly influential in demonstrating the effectiveness of conservation practices for improving soil productivity and water quality. She serves on the educational committee with the Surveying & Spatial Sciences Institute (SSSI) NSW and has contributed significantly to program development at the University of Newcastle.

Anne ObermannView profile

Lecturer

• Seismology
• Geothermal Energy
• Hydrogeology
• +3 more

Dr. Anne Obermann is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zürich, Switzerland. She is affiliated with the Schweiz. Erdbebendienst (SED) and conducts research at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BULGG). Her work focuses on seismic monitoring, geothermal energy systems, and hydraulic stimulation experiments. Key projects include CO2 mineral storage characterization at Iceland's geothermal sites and real-time seismic data processing using tools like DUGseis. Research interests revolve around the integration of geophysical imaging techniques (seismic tomography, noise interferometry) with hydro-mechanical processes in subsurface environments. She has led studies on induced seismicity, reservoir engineering, and fault structure analysis in fractured rock systems. Notable contributions include developing monitoring frameworks for geothermal reservoirs and evaluating stress heterogeneity impacts during stimulation. Publications highlight advancements in microseismic event detection using deep learning, multi-scale monitoring systems, and CO2 storage integrity. Collaborations involve international projects like CarbFix2 (Iceland) and BedrettoLab (Switzerland). Her work bridges theoretical geophysics with applied engineering solutions for sustainable energy and subsurface resource management.

Ryan SchultzView profile

Lecturer

• Seismology
• Induced Seismicity
• Geothermal Systems
• +3 more

Dr. Ryan Schultz is a Lecturer at the Department of Earth and Planetary Sciences, ETH Zürich, Switzerland. He specializes in seismology with a focus on induced seismicity risks in energy production and subsurface engineering projects. His work addresses hydraulic fracturing, geothermal systems, and CO2 injection-induced earthquakes. Schultz holds a Ph.D. in Seismology from Stanford University (2019–2022), an M.Sc. in Geophysics from the University of Alberta (2010–2012), and dual B.Sc. degrees in Physics and Chemistry from the University of Alberta (2003–2009). Research interests include seismic hazard mitigation, fault reactivation mechanisms, and real-time monitoring technologies. Key projects involve developing protocols for induced seismicity management in geothermal and carbon capture initiatives, such as the Trüllikon CO2 test injection and Alberta No. 1 geothermal site. His work also explores machine learning applications for microseismic data analysis and reinforced learning for seismic risk control. Publications highlight trends in bounding induced earthquake magnitudes, optimizing traffic light protocols, and assessing subsurface fluid dynamics. Schultz is affiliated with the Schweiz. Erdbebendienst (SED) and contributes to international collaborations, including the DEEP project and FORGE geothermal experiments. His expertise spans fieldwork, computational modeling, and policy-driven risk frameworks for sustainable energy development. Prior roles include Scientific Assistant at ETH Zürich (2023–present), and extended experience with the Alberta Geological Survey (2012–2021), where he investigated induced seismicity in shale basins. His research integrates geological, geophysical, and engineering perspectives to address global challenges in subsurface energy development.

Ali BehrangiView profile

Professor

• Remote Sensing
• Precipitation Measurement
• Climate Extremes
• +2 more

Ali Behrangi is a University Distinguished Scholar and Professor in the Department of Hydrology and Atmospheric Science at the University of Arizona. He holds joint appointments in Civil Engineering, Geosciences, and Remote Sensing programs. Research focuses on satellite remote sensing of precipitation, extreme weather events, and global water cycles. Primary investigations develop innovative methods for precipitation measurement using multi-sensor approaches, machine learning techniques, and climate model evaluation. Current projects include high-latitude precipitation retrieval, cloud property characterization, and hydrological modeling in arid regions. Recent publications demonstrate advancements in satellite precipitation products (GPCP, IMERG), machine learning applications in hydrology, and cryosphere monitoring. Research consistently addresses challenges in observing and modeling water cycles under changing climate conditions. Leadership roles include development of next-generation precipitation products for the Global Precipitation Climatology Project (GPCP). Research contributes to improved understanding of drought, flood, and hydrological extremes through enhanced observation systems.

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +5 more

Prof. Gerald Steinbauer-Wagner is an Associate Professor at TU Graz's Institute of Software Engineering and Artificial Intelligence. He specializes in autonomous intelligent systems, focusing on decision-making architectures for robots in uncertain environments. His research integrates software engineering, AI, and robotics to develop reliable systems for applications in disaster response, planetary exploration, and industrial automation. Research Interests: Steinbauer-Wagner's work addresses challenges such as robot navigation in unstructured terrains, human-robot collaboration, and trustworthiness in autonomous systems. Key areas include explainable AI, fault diagnosis, and multi-robot coordination. His team explores solutions for off-road robotics, collective perception, and certification of autonomous systems. Recent Projects: Current initiatives include developing autonomous systems for subterranean rescue missions (ROBO-MOLE), optimizing robot localization using machine learning, and creating educational frameworks for AI literacy in schools. His group also contributes to the RoboCup Logistics League and planetary exploration cascades through analog missions like AMADEE-20. Awards & Recognition: No specific awards listed, but his work has been recognized through extensive publications in top venues like IEEE/RSJ IROS, ICRA, and RoboCup symposiums. Lab & Teams: Leads the Autonomous Intelligent Systems (AIS) research group at TU Graz, collaborating with industry partners like the Smart Factory. His team develops integrated robot systems for production, logistics, and disaster response scenarios.

• Condensed Matter Physics
• Materials Science
• Nanotechnology
• +5 more

Joseph Kioseoglou is a Professor in the Department of Condensed Matter and Materials Physics at the School of Physics, Aristotle University of Thessaloniki. He holds leadership roles, including Deputy Head of the School of Physics (since 2022) and Director of the Theoretical and Computational Solid State Physics Laboratory (since 2020). His academic career includes roles as Assistant Professor (2009), Associate Professor (2016), and Full Professor (2021). Education: B.Sc. (Physics, 1993), M.Sc. (Materials Physics, 2000), Ph.D. (2004), all from Aristotle University of Thessaloniki. Research interests focus on computational and experimental condensed matter physics, particularly in nanomaterials, magnetic materials, thermal management, and semiconductor properties. He employs advanced techniques like ab initio simulations, machine learning, and experimental characterization to study defects, nanocluster behavior, and material interfaces. His work spans over 100 articles, emphasizing topics such as nanoparticle coalescence, phononic crystals, and spintronic systems. He has contributed to the design of nanostructured materials for applications in energy storage, catalysis, and quantum devices. Professional service includes roles like Deputy President of the Board of Directors at the Urban Public Transport Organisation of Thessaloniki (2020–2022). His research lab specializes in theoretical and computational modeling of solid-state systems.

Andrew CrooksView profile

Professor

• Geographical Information Science
• Geocomputation
• Agent-based Modeling
• +3 more

Andrew Crooks is a Professor in the Department of Geography at the University at Buffalo's College of Arts and Sciences. He holds a PhD from University College London (2007) and specializes in Geographical Information Science, Geocomputation, Agent-based Modeling, Social Network Analysis, Urban Geography, and Computational Social Science. His research integrates advanced computational methods with urban and environmental challenges, focusing on topics like disaster resilience, pandemic modeling, and synthetic population dynamics. Key research contributions include frameworks for urban digital twins, synthetic population modeling, and agent-based simulations of human behavior and disease spread. He teaches courses such as GEO 281 (Web-Based GIS), GEO 120 (Maps: Earth from Above), and GEO 503 (Spatial Simulation). His work bridges geospatial technologies with social sciences, addressing issues like climate adaptation, urban shrinkage, and vaccine uptake dynamics through innovative data-driven approaches. Crooks' recent articles highlight trends in generative AI for urban modeling, crowdsourced environmental monitoring, and hybrid space information diffusion. His methodologies emphasize interdisciplinary collaboration, leveraging social media, citizen science, and large-scale computational tools to solve complex geographical problems. He has contributed to disaster response strategies, urban policy design, and pandemic preparedness through actionable insights derived from spatial and agent-based models.

Milt HalemView profile

Research Professor

• Computational Science
• Climate Modeling
• Machine Learning
• +3 more

Milt Halem is a Research Professor in the Department of Computer Science and Electrical Engineering (CSEE) at the University of Maryland, Baltimore County (UMBC). With a career spanning nearly 70 years, he transitioned from NASA to UMBC in 2003, where he continues to contribute as a computational scientist focusing on leveraging advanced computing technologies—from historical vacuum tubes to modern AI—to address complex scientific challenges. His work integrates computational methods with environmental and climate science, emphasizing real-time validation, wildfire prediction, and digital twin systems. Research Interests: Halem’s research revolves around computational science, climate modeling, and AI-driven environmental analytics. He pioneers applications such as wildfire digital twins, machine learning for weather forecasting, and planetary boundary layer estimation. His interdisciplinary approach bridges machine learning, atmospheric science, and geospatial analysis to tackle global challenges like deforestation impacts and air quality forecasting. Collaborations & Mentoring: Halem has fostered partnerships with government agencies and industry, securing research funding and equipment. He teaches a popular graduate course and has mentored over a dozen Ph.D. students, guiding their research in computational methods and environmental science. His work emphasizes the synergy between computational tools and real-world environmental problems, aiming to advance both theoretical understanding and practical solutions.

• Geophysics
• Seismic Monitoring
• Environmental Geology
• +5 more

Dr. Olivia Collet is a Research Fellow at Curtin University's School of Earth and Planetary Sciences (EPS), Faculty of Science and Engineering. She contributes to the Office of the Provost, demonstrating leadership in academic governance. Her research focuses on geophysical signal processing, distributed acoustic sensing (DAS), and rock physics, with particular emphasis on seismic monitoring, CO2 sequestration, and deep learning applications in geoscience. Collet's work bridges computational methods with environmental geology, addressing challenges in subsurface imaging and carbon storage monitoring. Her education and prior roles are not explicitly detailed in the profile, but her extensive publication record spans over a decade, reflecting deep expertise in seismic anisotropy, fluid-rock interactions, and machine learning-driven geophysical analysis. Notable projects include CO2 plume detection via DAS Rayleigh waves and noise reduction techniques for raw DAS data. Collet collaborates internationally, as evidenced by multi-author publications from institutions like the University of Western Australia and global research consortia. Publications (2011–2025) highlight her contributions to: 1) Stress and fluid effects on rock anisotropy, 2) Machine learning for seismic data interpretation, 3) Environmental monitoring via fiber-optic sensing, and 4) Theory-guided reservoir characterization. Her work often intersects with industry needs in petroleum engineering and environmental policy.