Find Academic Supervisors

Silvia Katsarska-FilipovaView profile

Associate Professor

• Photogrammetry
• Remote Sensing
• 3D Modeling
• +5 more

Silvia Katsarska-Filipova is an Associate Professor at the Department of Photogrammetry and Cartography within the Faculty of Geodesy at the University of Architecture, Civil Engineering and Geodesy (UACEG) in Sofia, Bulgaria. She holds a Doctor of Science degree in Photogrammetry and Remote Sensing Methods (2014) and a Master of Science in Geodesy from the same institution (1995-2000). Currently, she teaches courses including Unmanned Aerial Photogrammetry and Transformation and Interpretation of Space Images, and conducts exercises in Digital Photogrammetry, Close-range Photogrammetry and Laser Scanning. Doctor of Science in Photogrammetry and Remote Sensing Methods (2014) Master of Science in Geodesy, University of Architecture, Civil Engineering and Geodesy (1995-2000) Dr. Katsarska-Filipova's research focuses on digital photogrammetry, image processing, 3D modeling of surfaces and volumetric bodies, methods for obtaining and analyzing geospatial information, unmanned aerial systems for image capture and processing, applications of remote sensing, and GIS. Her work bridges theoretical advancements with practical applications in cultural heritage documentation, environmental monitoring, and urban planning. She has developed expertise in using drone technology for precise geospatial measurements and analysis, particularly for volume computations in quarries and other applications. Her recent publications (2022-2024) demonstrate a strong focus on integrating photogrammetry with remote sensing for diverse applications including urban heat island analysis, forest fire monitoring, water body assessment, and photovoltaic park efficiency evaluation. She has increasingly incorporated digital twin technology and multi-sensor approaches in her research, showing a progression from traditional photogrammetric techniques to more integrated, technology-driven solutions that combine close-range photogrammetry, laser scanning, and satellite imagery. Her work frequently involves practical applications in environmental monitoring and cultural heritage preservation. Dr. Katsarska-Filipova has supervised numerous diploma students on topics ranging from photogrammetric documentation of architectural heritage to analysis of burned territories using satellite imagery. She has served as Quality Manager for her department since 2017 and has participated in multiple research projects including 'Information platform for research and monitoring of the environment' (2018-2019), 'Use of multispectral and thermal images in analysis and assessment of energy efficiency' (2021-2022), 'Creation of 3D models of architectural objects based on hybrid technology for laser scanning' (2022-2023), and 'Digital geodetic twins in UACEG' (2023-2024). She is a member of the Union of Geodesists and Land Surveyors in Bulgaria and has received positive assessment from the Commission for the Attestation of the Academic Staff at the Faculty of Geodesy of UACEG (375.9 points) dated January 7, 2020. Her research has been widely cited internationally, particularly her 2016 work on volume computation of stockpiles using UAV measurements, which has become a reference in the field of drone-based surveying applications.

• Machine Vision
• 3D-camera design
• Real-time image processing
• +5 more

Professor Stephan Hußmann is a faculty member at the West Coast University of Applied Sciences (FHW) in the Department of Microprocessor Technology and Electronics within the Faculty of Engineering. He has been a full-time Professor at FHW since December 2004, after working as a Lecturer at the University of Auckland from 2001-2003 and as a project leader for PMDTec GmbH in 2004. His research interests focus on Machine Vision applications, 3D-camera design, real-time image processing, embedded systems design, and automation in agriculture and forestry applications. Hußmann has developed innovative optical multi-sensor systems for quality control and inspection, with particular emphasis on drone-based AI sensor systems for agricultural applications in times of climate change. His work bridges theoretical research with practical industrial applications, particularly in the organic farming sector. Analysis of his recent publications reveals a strong trend toward drone-based agricultural applications, with significant focus on weed detection and control using deep learning approaches. His research spans multiple disciplines including computer vision, robotics, agricultural engineering, and real-time processing systems. The majority of his recent work focuses on practical implementations for organic farming challenges, demonstrating a consistent commitment to solving real-world agricultural problems through technological innovation. Top Twenty Faculty Teachers award (2002) Best Lecturer-Computer Systems Engineering Department award (2003) Senior member of IEEE (since 2011) Professor Hußmann has supervised numerous PhD, Master's, and Bachelor's students, with a particular focus on practical applications in machine vision and agricultural technology. His research has been supported by significant funding, including multiple projects from the Federal Ministry of Education and Research (BMBF) totaling over 8.2 million euros. Current major projects include drone-based AI sensor systems for agriculture in times of climate change, water rescue drones, and aerial photogrammetric surveys. He co-founded the Machine Vision Technology Institute (Ma.Vi.Tec) in 2009, which later became eyespec GmbH, and is the acting chairman of the registered association Initiative Bildverarbeitung e.V. Since 2018, he has led the FHW research group 'Digitalization in Agriculture and Forestry,' which resulted in the founding of naiture® GmbH & Co. KG in 2018. His teams regularly collaborate with industry partners through the Initiative Bildverarbeitung e.V., which organizes lectures where industry and university members discuss potential joint projects.

Riadh MunjyView profile

Professor

• Photogrammetry
• Unmanned Aircraft Systems (UAS) Mapping
• Remote Sensing
• +5 more

Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.

• Photogrammetry
• Remote Sensing
• Underwater Imaging
• +7 more

Dr. Petra Helmholz is an Associate Professor in the School of Earth and Planetary Sciences at Curtin University, specializing in Photogrammetry and Remote Sensing. She holds a German Dipl.-Ing. (M.Sc.) and Dr.-Ing. (Ph.D.) in Geomatics. Her research focuses on underwater, terrestrial, and airborne photogrammetry, with applications in health, agriculture, heritage mapping, and engineering. She has received multiple awards, including the Asia Pacific Excellent Award in Education (2018) and WA Spatial Awards. She serves as National Chair of the Remote Sensing & Photogrammetry Commission (SSSI) and is an Associated Editor of the journal PE&RS. Helmholz leads the Bachelor of Surveying program at Curtin and holds licenses in remote piloting and surveying. Her work bridges geospatial technologies with interdisciplinary fields like medicine and environmental science. Education: 2006–2012: Ph.D. in Photogrammetry, Leibniz University Hannover 2003–2005: Certified Licensed Surveyor, Germany 1997–2003: B.Sc./M.Sc. in Surveying, TU Berlin Research Interests: Underwater and airborne photogrammetry, 3D reconstruction for medical and heritage applications, thermal simulation of urban environments, and geospatial data analysis. Her projects include facial analysis for rare disease diagnosis, coral growth estimation, and pipeline monitoring. Publications highlight innovations in underwater imaging techniques, urban heat island studies, and medical 3D modeling. She actively contributes to international bodies like ISPRS and ASPRS, co-chairing working groups on geospatial data standards and applications.

Marc OlanoView profile

Associate Professor

• Computer Graphics
• Real-Time Systems
• Game Development
• +5 more

Dr. Marc Olano is an Associate Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He specializes in real-time 3D graphics, non-graphics applications of graphics hardware, and game development. As director of UMBC’s Game Development Track and 3D photogrammetric scanning facility, he focuses on advancing interactive technologies and environmental monitoring systems like Ecosynth. His research collaborations include work with gaming companies such as Firaxis, Epic Games, and Activision, as well as contributions to virtual reality (VR) applications in healthcare and scientific visualization. Dr. Olano holds a Ph.D. in Computer Science from the University of North Carolina and a B.S. in Electrical Engineering from the University of Illinois. Education: Ph.D., Computer Science, UNC (1998); B.S., Electrical Engineering, UIUC (1990). Research Interests: Computer graphics, GPU acceleration, VR, game development, and ecological 3D scanning. Key Roles: Director of Game Development Track, Editor-in-Chief of the Journal of Computer Graphics Techniques. Olano’s work bridges academia and industry, emphasizing practical applications like texture compression for games, VR-based rehabilitation systems, and UAV-driven environmental analysis. His research often leverages GPU computing to solve real-world problems, from optimizing rendering algorithms to enabling large-scale ecological data collection through Ecosynth. Publications span VR systems, subsurface scattering techniques, and GPU algorithms, reflecting his commitment to advancing interactive and computational graphics. His teaching and mentorship in the Game Development Track prepare students for roles in game design, while his lab facilities provide hands-on experience with cutting-edge technologies.

• Photogrammetry
• Remote Sensing
• Unmanned Aerial Vehicle (UAV)
• +7 more

Elzbieta Pastucha is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), where she is affiliated with the SDU UAS Center and SDU Climate Cluster. Her research focuses on UAV-based photogrammetry, remote sensing, and geospatial data analysis, with applications in environmental monitoring, urban mapping, and precision agriculture. Education: Ph.D. in Surveying, Photogrammetry, and Remote Sensing, AGH University of Science and Technology (Awarded: January 21, 2016) Research Interests: Her work spans advanced photogrammetric techniques, including smartphone-based 3D modeling, radiometric correction of UAV imagery, and classification of urban environments using neural networks. She applies these methods to solve real-world problems in infrastructure monitoring, geological displacement, and agricultural yield estimation. Her research integrates computer vision, geospatial engineering, and machine learning to enhance data accuracy and usability. Publication Trends: Recent publications (2022–2024) demonstrate a strong focus on UAV photogrammetry, with emphasis on image normalization, deformation correction, and environmental classification. Her work frequently involves interdisciplinary collaboration and appears in high-impact journals such as Remote Sensing , Sensors , and Engineering Geology . Scientific Awards: No awards or fellowships explicitly mentioned in the text. Advising and Grants: While no formal advisees are listed, she is actively teaching courses in statistics, data analysis, and robotics for UAS. She leads and participates in externally funded projects, including the EU-supported 'WildDrone' project as project manager and 'Præcisionsfrøavl' as a project participant, both running from 2023 to 2026. Labs and Teams: She is a key member of the SDU UAS Center and contributes to the SDU Climate Cluster. Her work is conducted within collaborative teams involving researchers from engineering, computer science, and environmental disciplines. She collaborates with researchers across institutions, particularly in UAV-based environmental and agricultural monitoring.

Ahmed ShakerView profile

Professor

• Remote Sensing
• Geomatics Engineering
• Image Processing
• +8 more

Ahmed Shaker is a Professor in the Department of Civil Engineering at Toronto Metropolitan University, where he also serves as Associate Dean for International Affairs in the Faculty of Engineering and Architectural Science (FEAS) and Director of the Remote Sensing Innovation Lab (RSIL). His work bridges advanced remote sensing technologies with civil engineering applications. Education: PhD, The Hong Kong Polytechnic University, 2004 MASc, Cairo University, 1998 BSc, Cairo University, 1993 Dr. Shaker's research centers on remote sensing, particularly in multispectral LiDAR data processing, image registration, radiometric correction, and data fusion. He explores how sensor data can be used to map and classify land surfaces with high precision, enabling applications in urban planning, environmental monitoring, and infrastructure assessment. His work emphasizes innovation in 3D laser scanning and the integration of spectral and spatial data for improved surface characterization. His recent publications reflect a strong focus on extracting meaningful information from complex remote sensing datasets. Themes include land/water discrimination, artifact correction in LiDAR intensity data, and the use of geospatial regression models for environmental quality assessment. The research spans disciplines such as environmental science, urban studies, and cultural heritage, demonstrating the versatility of remote sensing in solving real-world engineering problems. Scientific Awards: Dean’s Teaching Award, Ryerson Award, 2015 Bronze Medal Award, Canadian Remote Sensing Society (CRSS), 2011 Faculty Scholarly, Research and Creative Activity Award, Toronto Metropolitan University, 2011 Marie Curie Incoming International Fellowship, European Commission, 2006 Dr. Shaker has secured significant research funding and leads the Remote Sensing Innovation Lab, where he mentors students and collaborates on international projects. He has supervised numerous research initiatives and is actively involved in academic service, including leadership roles in ISPRS and the Canadian Remote Sensing Society. His advisory work supports the advancement of geospatial technologies in engineering practice. He is associated with the Remote Sensing Lab at Toronto Metropolitan University, which focuses on developing cutting-edge methodologies for processing and analyzing LiDAR and satellite imagery. The lab fosters interdisciplinary collaboration and innovation in geospatial data science.

• 3D City Modeling
• Laser Scanning
• Photogrammetry
• +7 more

Hongchao Fan is a Professor in the Department of Civil and Environmental Engineering at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on 3D city modeling (digital twins) , laser scanning , photogrammetry , and spatial data mining using crowdsourcing data , with applications in urban infrastructure, cultural heritage preservation, and solar energy assessment. Key research areas: 3D building reconstruction from point clouds Knowledge graph integration for urban data OpenStreetMap (OSM) data inequality analysis Solar energy potential modeling in Nordic cities Machine learning for geospatial applications Current projects: Digitalization for Culture Heritage (2025-2028, Norwegian Directorate for Higher Education and Skills) MobilityLab Stor-Trondheim (MoST) (2024-2026, digital twins for transportation models) Hellios (2022-2024, solar energy in Nordic cities) Deep learning 3D building models (NTNU Digital, 2021-2025) Technical expertise: Airborne Laser Scanning (ALS) data processing, semantic facade reconstruction, photogrammetric algorithms, and GIS-based decision support systems. He serves as Associate Editor for Big Earth Data and Geo-Spatial Information Science .

• Geomatics
• Cultural Heritage
• 3D Modeling
• +3 more

Giacomo Patrucco is an Associate Professor at the Department of Architecture and Design (DAD) of the Polytechnic University of Turin, Italy. He specializes in geomatics for cultural heritage, focusing on 3D metric survey, artificial intelligence, and multispectral imaging. His research integrates machine learning and deep learning techniques into photogrammetric pipelines for heritage degradation identification, museum artefact digitization, and archaeological site monitoring. Education: PhD in Geomatics (2023, Polytechnic University of Turin), MSc in Architecture for Sustainable Design (2016, Polytechnic University of Turin) His recent publications highlight advancements in synthetic training datasets for decay detection, airborne LiDAR classification for landscape morphologies, and multisensor 3D documentation. Key trends include AI-driven automation, thermal-optical data fusion, and UAV-based heritage mapping. He actively collaborates with international labs (GiFLE, Valencia; MAIER, Hierapolis) and leads the LabG4CH at Polytechnic University of Turin. Awards: Best Poster SIFET2017 Best Paper ARQUEOLÓGICA 2.0 2021 CIPA HD Video Contest 2023 Professional Memberships: Effective Member, SIFET (2023–) Effective Member, CIPA Heritage Documentation (2020–) Effective Member, ISPRS (2019–) Patrucco advises PhD students Marco Avena and Marco Cappellazzo, participates in commercial projects (e.g., Iraqi Ministry of Culture training, Casale Monferrato restoration), and contributes to editorial boards like HERITAGE (2023–). His work aligns with SDGs 4 (Quality Education), 5 (Gender Equality), and 11 (Sustainable Cities).

• Computed Tomography
• Geoinformatics
• Image Processing
• +4 more

Dr. KAPITÁNY Kristóf is an Associate Professor at the Department of Photogrammetry and Geoinformatics within the Faculty of Civil Engineering at Budapest University of Technology and Economics. His office is located in Room K. ép / I. em. 31/8, with consultation hours every Wednesday from 12:00-13:00. He teaches courses including Civil Engineering Informatics (BMEEOFTAT42) and Numerical Methods (BMEEOFTMK51, BMEEOAFMB51). His research centers on advanced imaging and computational techniques for civil engineering, with key focus areas: Object reconstruction from image series (CT/X-ray analysis of cerebral vasculature, concrete, asphalt, and artworks) Geospatial analysis of urban systems (e.g., bike-sharing networks) Material science applications (organic insulation via SEM, fiber-reinforced concrete) AI-driven efficiency in construction (algorithmic design, digital twins) Recent publications (2015-2024) demonstrate strong emphasis on computed tomography for material diagnostics, with growing exploration of AI strategies. Key domains include structural material degradation analysis, non-destructive testing, geospatial data processing, and heritage conservation, reflecting consistent innovation in imaging methodologies. Awards include the #építő250 Scholarship for academic merit. No information is currently available regarding student supervision, research grants, or laboratory affiliations.

Jan-Peter MundView profile

Professor

• Geographical Information Systems (GIS)
• Remote Sensing
• Precision Forestry
• +5 more

Jan-Peter Mund is a Professor at the Faculty of Forest and Environment, Eberswalde University for Sustainable Development, holding a research professorship in GIS and Remote Sensing since 2018. His work bridges academic roles with practical applications in Precision Forestry and UAV-based monitoring . Academic Appointments: Research Professor (2018–2026), Co-Leader of the M.Sc. Forest Information Technology program Research Focus: Geographical Information Systems (GIS), Remote Sensing, UAV applications for forest monitoring, and digital forest inventories His publications from 2019–2022 demonstrate expertise in UAV photogrammetry for tree height measurement, LiDAR integration , and 360° virtual forest tour development. He employs deep learning (U-Net networks) and multi-criteria optimization frameworks in forest management contexts. Research trends show consistent engagement with forest technology innovation through: Advanced UAV data processing 3D modeling for volume estimation Thermal analysis in urban green spaces Virtual reality educational tools

Arnaud Le BrisView profile

Researcher

• Remote Sensing
• Land Cover Classification
• Image Processing
• +6 more

Arnaud Le Bris is a Researcher in remote sensing, land cover classification, and image processing at the French National Geographic Institute (IGN), assigned to the Laboratory of Geographic Information Science and Technology (LaSTIG) since 2005. He is a member of the STRUDEL research team and has established himself as a leading expert in high-resolution land cover classification, hyperspectral imaging, and data fusion methodologies. His research focuses on: High resolution land cover classification from very high spatial resolution data Hyperspectral and super-spectral imaging techniques Data fusion for remote sensing applications Processing and analysis of historical aerial imagery Quality evaluation and change detection in geographic databases Automatic georeferencing of archival photogrammetric surveys Dr. Le Bris has published extensively in top remote sensing journals and conferences, with recent work emphasizing CNN-based semantic segmentation for land cover classification, historical aerial image processing, and multi-source data fusion. His research often addresses complex urban and natural environments, with applications in environmental planning, urban development, and territorial evolution studies over time. His publications demonstrate a consistent focus on methodological innovation in spectral band selection, radiometric correction, and geometric processing of both contemporary and historical imagery. Dr. Le Bris actively contributes to major research projects including HYEP (Hyperspectral imagery for Environmental urban Planning), URCLIM (Urban Climate Services), MAESTRIA, and HIATUS (Historical Aerial Images Time Series Analysis). These projects address critical challenges in urban planning, climate adaptation, and land use monitoring through advanced remote sensing techniques. As an academic mentor, he has supervised numerous PhD students, post-doctoral researchers, and interns from various institutions including Université Paris Est, National School of Geographic Sciences, and international universities. His supervision spans topics from 3D city model evaluation to hyperspectral data fusion and historical image analysis.

Bruno ValletView profile

Researcher

• Geographic Information Science
• Computer Vision
• Remote Sensing
• +7 more

Bruno Vallet is a Senior Researcher at IGN (French National Institute of Geographic and Forest Information) within the LASTIG lab and leads the ACTE research team since 2019. His work focuses on geospatial data processing, including LiDAR and image registration, 3D urban modeling, and computer vision applications. He contributes to projects like AI4GEO and Time Machine , specializing in large-scale point cloud analysis and structured city reconstruction. Education : Habilitation (HDR) in Geographic Information Science (Univ Paris-Est, 2016), PhD in Computer Science (Institut National Polytechnique de Lorraine, 2008), and Master's in Computer Vision (Telecom ParisTech, 2005). His research integrates surface reconstruction , semantic labelings , and uncertainty propagation , with methodologies applied to autonomous navigation and urban change detection. Recent publications emphasize data fusion, visibility computation, and deep learning for 3D scene analysis. He co-supervises PhD students and leads teaching activities at ENSG (National School of Geographic Sciences), covering image processing and 3D data structures. Bruno Vallet also chairs the ISPRS Working Group II/4 on 3D Scene Reconstruction, demonstrating leadership in photogrammetry and remote sensing communities.

• Timber Engineering
• Bio-based Composite Materials
• Non-destructive Testing Technology
• +4 more

Dr Ahmed Mohamed is a Lecturer at Edinburgh Napier University's School of Computing, Engineering, and the Built Environment, where he has built his academic career since joining as a PhD student in 2010. He currently serves as program leader for the global online MSc Construction Project Management and teaches mathematics and civil engineering subjects at undergraduate and postgraduate levels. His research focuses on innovative applications of photogrammetric and stereo vision techniques for evaluating material properties in timber and composite structures. Key areas include timber engineering, bio-based composite materials in construction, non-destructive test technology, and development of high strength green concrete. His work demonstrates consistent methodological innovation in structural testing, particularly using optical systems to measure torsional behavior and shear modulus in timber beams. Analysis of his publication record reveals a strong thematic concentration on photogrammetric approaches to structural testing, with particular emphasis on torsion testing methodologies for timber and composite materials. His research shows progression from fundamental measurement techniques to practical applications in construction materials, with increasing complexity in experimental setups and analysis methods over time. Member of Editorial Board for Engineering Future Sustainability Keynote speaker at International Forum on Infrastructure and Civil Engineering (ICEFORUM2023) Participant in BE-ST Fest 2022 'Festival of the zero carbon built environment' Demonstrated Bamboo-Timber composite Gridshell Structure at COP 26 Dr Mohamed has successfully secured research funding from multiple prestigious sources including the Royal Academy of Engineering, British Council, Scottish Funding Council, and Energy Technology Partnership. His current PhD students include Kyaw Saw (researching high strength concrete for green buildings) and Shen Minhe (developing bamboo-timber composite beams). He previously completed his own PhD on photogrammetric and stereo vision techniques for evaluating material properties in timber structures between 2010-2016. His research projects demonstrate practical applications of his expertise, including work on prestressed bamboo-timber composites, monitoring timber expansion for Jacket Pile Gripper applications, and computational fluid dynamics modeling for spill-proof coffee cup design. These projects reflect his ability to bridge fundamental research with industry applications in sustainable construction.

• Obstructive Sleep Apnea
• Neural Networks
• Multimodal Data Fusion
• +4 more

Jiali Xie is a University Researcher at Eindhoven University of Technology, affiliated with the Electrical Engineering school and Signal Processing Systems department. They work within the Biomedical Diagnostics Lab and Eindhoven MedTech Innovation Center, focusing on medical signal processing applications. Research spans obstructive sleep apnea diagnostics using multimodal signals (ECG, audio, respiratory effort) and medical deep learning systems. Key approaches include multi-task neural networks, convolutional architectures, and sensor fusion techniques for non-invasive monitoring. Recent work demonstrates strong applications in sleep staging, severity assessment, and real-world clinical implementation. Their publication trends show concentrated focus on sleep-disordered breathing (87.5% of recent articles) with emerging contributions to remote sensing (25% of 2023 output). Methodologically, neural networks dominate (100% of medical publications) with emphasis on multi-modal data integration (66%) and frequency-scale analysis (44%). Active in two major projects: MEDUSA (Medical Data Utilization Solutions Accelerator, 2020-2026) developing multimodal signal analysis for sleep apnea, and an earlier multimodal signal analysis project (2016-2021) focused on unobtrusive sleep apnea characterization. Media coverage includes expert commentary on building reconstruction methods from Guilin University of Technology.