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Hannu HyyppäView profile

Researcher

• Laser Scanning
• 3D Modeling
• Geoinformatics
• +5 more

Hannu Hyyppä is a Research Director and Project Employee at Aalto University's Department of Built Environment, affiliated with the MeMo research group. He leads the Research Institute of Measuring and Modelling for the Built Environment, focusing on advanced laser scanning, 3D modeling, and geoinformatics. His work spans interdisciplinary collaborations across engineering, geography, and arts, with a strong emphasis on applications in cultural heritage preservation, urban planning, and environmental monitoring. Education: Doctoral degree (D.Sc.) in Engineering and Technology, Helsinki University of Technology (2000) Licentiate degree in Engineering and Technology, Helsinki University of Technology (1989) Master's degree in Engineering and Technology, Helsinki University of Technology (1986) Research Interests: Laser scanning technologies, point cloud utilization in forestry and urban mapping, virtual reality for cultural heritage, and sustainable infrastructure modeling. His expertise includes photogrammetry, geographic information systems (GIS), and decision support systems for environmental management. Recent Contributions: Over 550 publications and 30+ active projects, including the Centre of Excellence in Laser Scanning Research (2014-2019) and the Pointcloud project (2015-2021). His work advances applications in autonomous road inspection, 3D cultural reconstructions, and smart city technologies. Awards: Recipient of the 2019 Kansallinen avoimen tieteen palkinto for innovative open science contributions. Grants & Leadership: Principal Investigator for projects like DICA (Digital Cultural Heritage) and ToToRo (Automatic Road Inspection). Active in organizing workshops and international conferences on 3D technologies and laser scanning. Labs/Teams: Oversees the MeMo group and collaborates with national organizations like the Finnish Geospatial Research Institute. Develops tools for real-time 3D mapping and virtual environments.

• Forest Inventory and Monitoring
• LiDAR Technology Applications
• Forest Regeneration Quantification
• +3 more

Tobias Ofner-Graff is a researcher at the Institute of Forest Growth within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based at Peter-Jordan-Straße 82, 1190 Wien, his work focuses on advanced forest monitoring technologies. His research interests include: LiDAR and remote sensing applications in forestry Automated forest inventory systems Forest regeneration quantification Airborne Laser Scanning (ALS) data analysis Sustainable forest harvesting planning Recent project contributions include: Leading lidar-based forest monitoring systems development Developing spatial forest growth models Implementing digital inventory workflows His publications demonstrate expertise in: Quantifying forest resources through 3D point clouds Advanced timber stack measurement techniques ALS data integration for forest modeling Mobile laser scanning applications Forest climate adaptation strategies

Stella PytharouliView profile

Senior Lecturer

• Microseismics
• Geodetic Deformations
• Slope Instability
• +6 more

Dr. Stella Pytharouli is a Senior Lecturer in Civil and Environmental Engineering at the University of Strathclyde. With over 20 years of expertise in structural and ground deformation monitoring/analysis, her research focuses on subsurface characterization and slope instability early warning systems through microseismic monitoring, geodetic technologies, and machine learning integration. MEng (2002) - University of Patras MSc (2004) - University of Patras PhD (2007) - University of Patras Her research combines advanced signal processing with geodetic monitoring (terrestrial/aerial) to develop AI-driven solutions for UK landslide sites. Key areas include: Microseismic monitoring of weak seismic events Geometric and kinematic analysis of ground deformations Integration of geotechnical data with machine learning Climate change impact on slope stability Low-cost sensor development for environmental monitoring Recent publications highlight her work on AI-based seismic classification models, tiltmeter applications, and 3D reconstruction techniques. Her group includes 4 PhD students and 1 postdoc. Scientific recognitions include: Geophysical Research Letters front cover selection (2011) EOS Research Spotlight (2019) Lampadarios Prize from Academy of Athens (2009) TOPCON Award for young researchers (2008) As Director of Postgraduate Research (2020-present), she supervises PhD students and teaches land surveying modules. Current projects address slope stability analysis, climate change correlations, and seismic data automation.

Lori Graham-BradyView profile

Professor

• Stochastic finite element methods
• Probabilistic mechanics
• Stochastic simulation of material properties
• +5 more

Lori Graham-Brady is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University's Whiting School of Engineering. She serves as Vice Dean for Faculty and directs the Center on AI for Materials in Extreme Environments (CAIMEE), while also holding secondary appointments in Mechanical Engineering and Materials Science and Engineering. Her research focuses on stochastic mechanics, multiscale modeling, and machine learning applications for understanding material variability under extreme conditions. Research areas include probabilistic mechanics, AI-driven materials design, and fragmentation modeling. Leadership roles: Director of CAIMEE, former Director of Center for Materials in Extreme Dynamic Environments, founding Director of HT-MAX, and founding Associate Director of HEMI (2012-2024). Education: PhD in Civil Engineering and Operations Research from Princeton University. Her recent work emphasizes AI for multiscale mechanics, error propagation in material characterization, and digital microstructure generation. Publications highlight stochastic modeling of ceramics, composites, and metals under impact and high-strain-rate loading. Scientific awards include the Presidential Early Career Award, Huber Civil Engineering Research Prize, and Fellowships in ASCE EMI and USACM. She led NSF IGERT programs and serves as Associate Editor for the ASCE Journal of Engineering Mechanics.

Dirk Van HulleView profile

Professor

• Beckett Studies
• James Joyce Studies
• Textual Editing
• +3 more

Dirk Van Hulle holds the position of Professor of Bibliography and Modern Book History within the University of Oxford's Faculty of English, with significant cross-institutional leadership roles. Chair of the Oxford Centre for Textual Editing and Theory (OCTET) Director of the Centre for Manuscript Genetics at the University of Antwerp Co-director of the Beckett Digital Manuscript Project Editor of the Journal of Beckett Studies Series Editor for Cambridge UP's 'Elements in Beckett Studies' His research centers on Beckett Studies, James Joyce Studies, and textual scholarship methodologies, specializing in genetic criticism to trace literary creation through manuscript evolution. Van Hulle bridges traditional bibliography with digital humanities, examining how modernist authors like Beckett and Joyce transformed drafts into finished works, with particular focus on material textuality and archival preservation. Analysis of his major publications reveals consistent emphasis on digital approaches to manuscript analysis, especially regarding 20th-century literary giants. Works like Modern Manuscripts (2014) and James Joyce's Work in Progress (2016) demonstrate methodological innovation in reconstructing compositional processes through digital archives, highlighting the Beckett Digital Manuscript Project's role in advancing genetic criticism. His scholarly recognition includes: 2019 Prize for a Bibliography, Archive or Digital Project from the Modern Language Association (MLA) Van Hulle directs the Oxford Centre for Textual Editing and Theory (OCTET), which develops interdisciplinary frameworks for textual scholarship, and the University of Antwerp's Centre for Manuscript Genetics, specializing in genetic analysis of literary manuscripts. The Beckett Digital Manuscript Project under his co-direction provides a comprehensive digital archive of Beckett's manuscripts, integrating high-resolution images with transcriptions and scholarly annotations to enable global research access.

Sheng SangView profile

Assistant Professor

• Mechanical Engineering
• Biomedical Engineering
• Machine Learning
• +7 more

Sheng Sang is an Assistant Professor in the Department of Engineering Sciences at Bethany Lutheran College. His research lies at the intersection of Mechanical Engineering and Biomedical Engineering, with a strong emphasis on machine learning applications in composite materials and elastic metamaterials. His research interests include: Mechanical & Biomedical Engineering Machine Learning on Composites Elastic Metamaterials and Composites Optimization of Medical Devices Finite Element Modeling and Simulation Dr. Sang's recent publications demonstrate a consistent focus on integrating deep learning techniques with mechanical systems, particularly in predicting composite microstructures, tracking particles in complex systems, and optimizing wave propagation in metamaterials. His work frequently employs 3D CNNs and other neural architectures to solve inverse problems in material science. Scientific awards and recognition include: Dr. Lehtola Fellowship Research Grant ($9,000, PI), 2021–2023 Graco Engineering Lab Development Grant ($60,000), 2020–2022 He has been actively involved in teaching a wide range of engineering courses such as Fluid Mechanics, Solid Mechanics, Thermodynamics, and Computer-Aided Design. His research is supported by external grants, indicating active supervision and project leadership. Dr. Sang has collaborated with researchers across disciplines, including neuroscience and medical imaging, particularly in studies involving deep brain stimulation and fMRI. He is affiliated with research teams working on: Active elastic metamaterials design Machine learning for material characterization Optimization of biomedical devices using swarm intelligence Development of advanced simulation tools for composite systems

Sidonie ChristopheView profile

Academic

• Geographic Information Sciences
• Geovisualization
• Map Design
• +12 more

Sidonie Christophe is a Senior Researcher (Directrice de Recherche, DR1) at UMR LASTIG, a joint research unit of Université Gustave Eiffel, IGN-ENSG, and EIVP. She serves as co-director of the LASTIG laboratory and leads research in geovisualization, map design, and interactive spatial data exploration. She also holds a part-time advisory role (60%) at the French Ministry of Higher Education and Research in the domain of digital technology, environment, climate, and sustainable urban development. PhD in Geographic Information Sciences Senior Researcher, DR1, MTECT Co-Director, LASTIG Laboratory (since 2021) Former Team Leader, GEOVIS (Geovisualization, Interaction, and Immersion) Advisor, Environment and Urban Climate, French Ministry of Higher Education & Research Her research centers on innovative methods for 2D/3D and nD geospatial data visualization, with a focus on enabling spatio-temporal understanding through visual and non-visual spatial thinking. Her work integrates principles from geographic information science, human-computer interaction, and computer graphics. Key areas include urban climate visualization, tactile and augmented reality for accessibility, expressive cartographic rendering, and cognitive aspects of map design. She investigates how aesthetic and semiotic choices impact map comprehension and utility. The 15 most recent publications reflect a strong trend in interactive and accessible geovisualization, particularly for urban and environmental applications. Topics include neural map style transfer, 3D urban climate analysis, tactile maps for the visually impaired, augmented reality in geography, and visual analytics for crisis and climate data. There is a consistent emphasis on user-centered design, interdisciplinary integration, and the development of tools for decision-making under uncertainty. Scientific Recognition and Service: Invited speaker at major conferences (IEEEVIS, AGILE, ICC, CPGIS) Co-organizer of international workshops (e.g., GeoVIS, ISPRS AR/VR sessions) Leader of national research projects (ANR ORACLES, ANR ACTIVmap, ANR ECOCIM) Recipient of international mobility grants (AMICI I-SITE FUTURE) Contributor to national glossaries and research strategy (e.g., French photogrammetry glossary) Advising and Grants: Sidonie Christophe actively supervises PhD students and postdoctoral researchers, including Markie Jiang, Maria-Jesus Lobo, and Alexandre Mielniczek. She leads or participates in multiple funded research projects such as ANR ORACLES (marine flooding visualization), ANR ACTIVmap (tactile 3D maps), and ANR ECOCIM (eco-design of city information models). Her advisory role at the MESR involves shaping national research strategy in digital and environmental sciences. Labs and Research Teams: She is a core member of the GEOVIS team (Geovisualization, Interaction, and Immersion) at LASTIG and previously served as its leader. As co-director of LASTIG, she plays a central role in the leadership and strategic direction of the entire laboratory, which comprises over 100 researchers across four teams: ACTE, GEOVIS, MEIG, and STRUDEL.

Caroline CrockettView profile

Assistant Professor

• Engineering Education
• Electrical Engineering Pedagogy
• Conceptual Understanding in Engineering
• +5 more

Caroline Crockett is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia, part of the School of Engineering and Applied Science. She holds a Ph.D. in Electrical Engineering from the University of Michigan and a B.S. from the University of Virginia. Her academic role emphasizes teaching and engineering education research. B.S., Electrical Engineering, University of Virginia, 2015 Ph.D., Electrical Engineering, University of Michigan, 2022 Her research interests are centered on engineering education, particularly how students develop conceptual understanding in core electrical engineering topics and learn to troubleshoot. She also has a background in machine learning and image reconstruction, though her current work focuses on pedagogy. She actively engages undergraduate students in research projects related to education and is a member of IEEE and ASEE. Her recent publications reflect a strong trend toward engineering education research, especially in signals and systems comprehension and affective student responses to active learning. Earlier work includes advanced image reconstruction techniques using bilevel optimization, showing a transition from technical signal processing to educational research. Caroline Crockett does not supervise graduate students or maintain a research lab, but she mentors undergraduates interested in engineering education. She has taught several key courses including Fundamentals of ECE 2, Fundamentals of ECE 3, and an Introduction to Machine Learning. She is committed to undergraduate instruction and curriculum development. She is involved in the broader academic community through her memberships in IEEE and ASEE, contributing to educational research and dissemination through conferences and publications. Outside of her academic duties, she enjoys knitting, reading, crafts, and spending time with her family and rescue dog Oreo. She is also a licensed amateur radio operator.

Alvaro Fernandez QuilezView profile

Associate Professor

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +13 more

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

Krista A. EhingerView profile

Associate Professor

• Computer Vision
• Artificial Intelligence
• Human Vision
• +7 more

Krista A. Ehinger is an Associate Professor and co-lead of the AI group at the University of Melbourne's School of Computing and Information Systems. She holds a PhD from MIT and has held postdoctoral positions at York University and Harvard Medical School. Her research focuses on the intersection of human and computer vision, including scene recognition, visual search, and depth perception. Methodologically, she combines Bayesian models, deep learning, and behavioral experiments like eye tracking. Current projects explore AI applications in space systems (e.g., SpIRIT satellite) and ethical implications of workplace surveillance via computer vision. Recent work emphasizes amodal completion (e.g., reconstructing occluded objects) and AI reasoning systems. She collaborates on medical imaging (TCAM-Diff model), autonomous driving (truck speed detection), and 3D reconstruction. Her lab actively engages in open-source tools like the SUN Database for scene understanding. Professional activities include AI ethics discussions and academic service. She advises students on Masters/PhD projects and contributes to conferences like CVPR and NeurIPS.

Fraser KingView profile

Assistant Professor

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +13 more

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

• Medieval Literature
• Digital Humanities
• German Studies
• +4 more

Manfred Kern serves as Professor and Head of the Department of German Studies at Paris Lodron University Salzburg. With an extensive publication record dating back to 1995 and continuing through 2025, he maintains an active research profile in medieval literature and digital humanities. His leadership role as department head and project manager for significant research initiatives demonstrates his standing within the academic community. Professor Kern's research interests center on medieval literary cultures, particularly through the lens of digital humanities. His work on the Wenceslas Bible represents a significant interdisciplinary contribution, combining traditional philological approaches with cutting-edge computer vision techniques. His recent editorial work on disability representation in art ( Kunst und Gebrechen ) demonstrates expanding scholarly interests at the intersection of cultural history and visual studies. His research consistently bridges historical scholarship with contemporary methodological innovations. Kern's publication trends reveal a strategic evolution from traditional literary studies toward digital humanities applications. His most recent work (2024-2025) shows a strong emphasis on computational analysis of historical manuscripts, particularly the Wenceslas Bible project, while simultaneously developing theoretical frameworks for understanding disability representation in historical art contexts. This dual focus represents a sophisticated integration of technical and humanistic approaches to cultural heritage. As project manager for significant research initiatives including WB-DEA (The Wenceslas Bible - Digital Edition and Analysis, 2022-2024), TALC_ME (Textual and Literary Cultures in Medieval Europe, 2014-2017), and ALIENA (Old literature re-aestheticized in the experience space, 2010-2013), Professor Kern has secured substantial research funding and built collaborative networks across institutions. His organizational role in numerous academic events, including the IZMF lecture series and the In Nomine conference on medieval naming practices, further demonstrates his leadership in the field. Professor Kern actively participates in departmental and university governance, serving on habilitation committees and appointment committees (Berufungskommission). His academic service extends to editorial work and conference organization, reflecting his commitment to scholarly community building. His leadership of the German Studies department at Paris Lodron University Salzburg positions him at the center of academic activity in his field.

James M Martin-HaydenView profile

Associate Professor

• Hydrogeology
• Groundwater Modeling
• Environmental Geology
• +3 more

James M Martin-Hayden serves as an Associate Professor in the Department of Earth, Ecological and Environmental Sciences within the College of Natural Sciences and Mathematics at the University of Toledo. With over 25 years of service at the institution, he has established himself as a prominent hydrogeologist specializing in groundwater systems and environmental geology. Education: B.A. from University of Maine, M.S. and Ph.D. from University of Connecticut (1994) Current Position: Associate Professor of Geology Department: Earth, Ecological and Environmental Sciences University: University of Toledo Dr. Martin-Hayden's research focuses on hydrogeology, with particular emphasis on groundwater-surface water interactions in wetland ecosystems, numerical groundwater modeling, and hydrogeologic field methods. His work primarily investigates the Oak Openings Region of northwest Ohio, examining how drainage modifications have altered natural groundwater flow regimes that support wet prairies. He has made significant contributions to understanding wellbore flow dynamics, groundwater sampling methodologies, and the impacts of evapotranspiration on groundwater recharge. His research integrates field observations, numerical modeling, and geophysical techniques to address complex hydrogeological problems. Analysis of Dr. Martin-Hayden's publication record reveals a consistent research trajectory focused on hydrogeological systems, particularly in the Great Lakes region. His work spans from fundamental investigations of wellbore flow dynamics to applied studies of regional aquifer systems and wetland hydrology. Recent publications demonstrate increasing integration of geophysical methods with traditional hydrogeological approaches, reflecting an evolution toward more comprehensive characterization of complex subsurface systems. His research shows particular strength in addressing the hydrological impacts of human modifications to natural systems, especially in wetland ecosystems. Dr. Martin-Hayden has mentored several students, including Lucas Groat (thesis on Hydrogeology of Wet Prairies) and Pryanka More (undergraduate thesis on Influences of ET on Groundwater Recharge). His collaborative work shows strong connections with researchers including Timothy G. Fisher, Kennedy Okioghene Doro, and Richard H. Becker, indicating active participation in research networks focused on Great Lakes geology and hydrogeology.

Jes FrellsenView profile

Associate Professor

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +5 more

Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +3 more

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.