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Tasnuva Ming KhanView profile

Researcher

• Paleoecology
• Marine Biology
• Biodiversity Assessment
• +4 more

Dr. Tasnuva Ming Khan is a Postdoctoral Researcher at the Institute of Paleontology , GeoZentrum Nordbayern , Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , where she focuses on paleoecology and marine biodiversity using interdisciplinary methods. She completed her PhD in Zoology at the University of Cambridge and British Antarctic Survey (expected 2025) and holds an MSc in Geosciences (2021) from FAU. Education: PhD Zoology (expected 2025) – University of Cambridge & British Antarctic Survey MSc Geosciences (2021) – Friedrich-Alexander-Universität Erlangen-Nürnberg BSc Earth Systems Science (2018) – Cornell University Research Interests include scale-dependent drivers of marine community structure, asymmetric range shifts in planktonic foraminifera under the AGELESS project , and the influence of colonial history/global economics on fossil record biases. Her work integrates Bayesian network inference , metacommunity theory , and spatial point process analyses to study modern and fossil marine ecosystems. Key Publications cover topics like deep-sea benthic community dynamics in Antarctica, thermographic studies of humpback whales, and geothermal energy projects at Cornell. Her recent studies (2024–2025) focus on automated detection of Antarctic benthic organisms and environmental heterogeneity impacts on polar ecosystems. Collaborations span institutions including University of Oxford , University of Colorado Boulder , and University of Washington , with affiliations in projects like Deep-time Ecology Group and AGELESS (Leveraging long-term planktonic diversity data for biodiversity protection in areas beyond national jurisdiction).

Tengjiao JiangView profile

Researcher

• Computer Vision
• Machine Learning
• Structural Health Monitoring
• +2 more

Tengjiao Jiang is a Researcher at the Department of Structural Engineering , Faculty of Engineering , Norwegian University of Science and Technology (NTNU) . Their work focuses on computer vision and deep learning applications for structural monitoring of transportation infrastructure. Research Interests : Computer Vision (CV) Machine Learning (ML) Optical Measurement Structural Health Monitoring (SHM) Condition Assessment of Transportation Infrastructures Recent Research Trends : Their publications (2020–2025) emphasize computer vision systems for railway and bridge monitoring, including uplift measurement, corrosion detection, displacement analysis, and damage assessment using CNNs and transformers. Key methods involve IMU sensors, line tracking, photogrammetry, and 3D reconstruction. Scientific Awards : Best Paper Award at IMAC-XLII conference (Jan 2024) Olympiad Medal in Olympiad in Engineering Science (May 2023) Supervision : Co-supervises PhD student Ziyue Lu (2022–present) and has co-supervised multiple Master’s students on topics like vision-based bridge inspection, modal identification, and vibration response analysis. Labs & Outreach : Conducts field tests on the Oslo Airport Line with vision-based monitoring systems.

• Unmanned Aerial Vehicles (UAVs)
• Non-Destructive Testing
• Sensor Technology
• +4 more

Dr. Dayi Zhang is a postdoctoral researcher in the Department of Electronic and Electrical Engineering at the University of Strathclyde. He holds a PhD in Autonomous Unmanned Aerial Vehicle for Non-Destructive Testing (awarded May 2019) and contributes to the UN Sustainable Development Goals, particularly Quality Education and Industry Innovation. As a topical advisory panel member for Drones Journal (since 2024), he bridges UAV technology with industrial applications. Research interests focus on unmanned aerial vehicles for non-destructive testing , with expertise in ultrasonic inspection , eddy current sensing , and force-torque sensor integration . His work addresses challenges in pipeline monitoring , metallic structure assessment , and autonomous robotic positioning , often combining sensor fusion and remote sensing techniques. Key research areas include UAV-deployed sensor systems Structural health monitoring Electromagnetic acoustic resonance Capacitive and pressure sensor design Photogrammetric and LiDAR-based localization Scientific recognition includes the 2024 IEEE TIM Outstanding Reviewer and Innovator of the Future (2025) awards. Collaborations span nuclear facilities, aerospace, and manufacturing domains, with ongoing editorial contributions to Discover Sensors and Drones journals.

Kristina MastanjevićView profile

Associate Professor

• Food Safety
• Analytical Chemistry
• Brewing Technology
• +5 more

Kristina Mastanjević serves as an Associate Professor of Biotechnology at the University of Osijek's Faculty of Food Technology, Croatia. Her academic profile centers on food safety research within Biotechnical sciences, with student consultations held Mondays 9-11 AM in office 57/18 at Franje Kuhača 18, Osijek. She maintains active research engagement through publications in high-impact journals while teaching core food science disciplines. Her research program critically examines hazardous compounds in traditional Croatian food systems, specializing in polycyclic aromatic hydrocarbons (PAHs) in smoked meats (Slavonska slanina, Buđola) and malt, alongside mycotoxin contamination in brewing by-products. She pioneers analytical methodologies including GC-MS coupling with sensory analysis and computer vision systems for beer quality assessment. Additional focus areas encompass malting technology for alternative grains, brewing waste valorization, and temperature-dependent sensory properties of craft beer under Mediterranean conditions. Analysis of her 2020-2023 publications reveals consistent investigation of regional food safety challenges, particularly PAH formation mechanisms during traditional smoking processes and mycotoxin transfer through malting/brewing. Her work bridges analytical chemistry with practical food engineering solutions, addressing regulatory compliance gaps for contaminants in artisanal production while developing objective quality assessment tools through digital imaging. No scientific awards or honors were documented in the provided materials. The available information does not specify graduate student advisement activities, research grant acquisitions, laboratory facilities, or collaborative research teams associated with Dr. Mastanjević's work.

• Photogrammetry
• Remote Sensing
• Measurement Data Analysis
• +2 more

Fatemeh Alidoost is a Lecturer at the Department of Photogrammetry and Geoinformatics at HFT Stuttgart. Her teaching areas include Photogrammetry, Remote Sensing, Measurement Data Analysis, Computer Vision, and Deep Learning. Her research focuses on 3D reconstruction techniques, deep learning applications in geospatial data analysis, and UAV-based photogrammetry for infrastructure and urban mapping. She has contributed to advancements in automated building detection, bridge modeling, and tunnel inspection systems. Her recent publications emphasize integrating convolutional neural networks with photogrammetric data for damage detection, semantic segmentation of point clouds, and multiscale analysis of remote sensing imagery.

Tatiana PodladchikovaView profile

Associate Professor

• Solar Physics
• Space Weather
• Coronal Mass Ejections
• +5 more

Dr. Tatiana Podladchikova serves as Associate Professor, Director of the Center for Digital Engineering, and Director of the Engineering Systems Master's Program at Skolkovo Institute of Science and Technology (Skoltech). Her work bridges fundamental solar physics research with operational space weather services to protect critical infrastructure including satellites, aviation systems, and power grids from solar-induced geomagnetic disturbances. Her research centers on solar activity dynamics—particularly solar cycles, eruptive flares, coronal mass ejections (CMEs), and high-speed solar wind streams—and their terrestrial impacts. She develops predictive models for space weather phenomena using advanced data analysis techniques, with recent work integrating deep learning frameworks for solar observation translation and physics-informed neural networks for magnetic field extrapolation. Her research directly addresses infrastructure vulnerabilities during extreme space weather events through operational forecasting systems. Dr. Podladchikova's publications demonstrate a clear evolution toward AI-enhanced space weather prediction, combining multi-point solar observations (including Solar Orbiter mission data), coronal dimming analysis for early CME detection, and innovative applications like the DIRECD method for real-time propagation tracking. Her work spans fundamental solar physics, engineering applications for satellite systems, and environmental monitoring techniques. Her scientific recognition includes: International Alexander Chizhevsky Medal for Space Weather and Space Climate (2015, Belgium) Skoltech Best Professor Award (2018-2020) Outstanding Achievements in Education and Research (2022-2023) As an educator, she directs Skoltech's Engineering Systems Master's Program, leads the Space Weather Laboratory, and teaches Experimental Data Analysis and Data Analysis for Space Weather courses. Her leadership extends to public outreach initiatives including science communication events like 'Mysteries of the Sun: Space Weather of the 21st Century' lectures. She heads the Skoltech Space Weather Laboratory where her team develops operational forecasting tools, analyzes multi-spacecraft solar observations, and advances CubeSat technologies including the Skoltech-F mission. Current projects focus on real-time geomagnetic storm prediction systems and AI-driven translation of historical solar datasets to maintain observational continuity.

Alexander BraunView profile

Professor

• Digital Twinning
• Building Information Modeling (BIM)
• Construction Progress Monitoring
• +7 more

Dr.-Ing. Alexander Braun serves as Senior Vice President Digitalization and IT Systems and Chief Information Officer (CIO) at the Technical University of Munich (TUM), while also leading the Digital Twinning in the Built Environment research group. He is affiliated with the Chair of Computing in Civil and Building Engineering within the Department of Civil, Geo and Environmental Engineering at TUM's School of Engineering. Dr. Braun's research focuses on the intersection of construction engineering, digital technologies, and artificial intelligence. His primary areas of expertise include Digital Twinning, Building Information Modeling (BIM), construction progress monitoring, point cloud processing, and computer vision applications in construction. His work bridges the gap between theoretical research and practical implementation in the construction industry, with a strong emphasis on automation and data-driven approaches. His extensive publication record demonstrates a consistent focus on advancing digital construction technologies. Recent research has centered on AI-enhanced digital twinning, semantic modeling from point clouds, construction process analysis through computer vision, and knowledge representation for construction sites. These efforts have resulted in numerous high-impact publications in top journals including Automation in Construction and Advanced Engineering Informatics . As an educator, Dr. Braun has taught courses such as "Bau- und Umweltinformatik 1" and "Bau- und Umweltinformatik Ergänzungsmodul" at TUM, contributing to the development of future construction informatics professionals. His supervisory role extends to numerous Bachelor's and Master's theses, reflecting his commitment to academic mentorship. Senior Vice President Digitalization and IT Systems at TUM Chief Information Officer (CIO) at TUM Group Lead for Digital Twinning in the Built Environment Researcher in Construction Informatics since 2013 Dr. Braun actively contributes to the academic community as a reviewer for prestigious journals including Automation in Construction , Advanced Engineering Informatics , and Visualization in Engineering . His international research collaborations include a visiting researcher position at CyberBuild (University of Edinburgh) with Dr. Frédéric Bosché in May 2019.

Thomas WiemannView profile

Professor

• Autonomous Robotics
• 3D Mapping
• SLAM
• +7 more

Thomas Wiemann is a temporary Professor for Autonomous Robotics at Osnabrück University and a researcher at the German Research Center for Artificial Intelligence (DFKI) , group Kooperative und Autonome Systeme . He has been active since 2007, focusing on 3D mapping, SLAM, and semantic interpretation of sensor data for robotics. He received his Dr. rer. nat. in 2013 and his habilitation in 2020. His work is widely recognized, with awards such as the Karman Innovation Award (2007) and the Intevation Free Software Award (2014). Education: Habilitation in Computer Science, Osnabrück University (2020) Doctorate (Dr. rer. nat.) in Computer Science, Osnabrück University (2013) M.Sc. in Physics and Computer Science, Osnabrück University (2007) B.Sc. in Physics and Computer Science, Osnabrück University (2005) Research Focus: Thomas Wiemann's research is centered on autonomous robotics , particularly in 3D mapping , SLAM , and semantic environment understanding . His work includes the automatic generation of polygonal maps from point cloud data, large-scale 3D reconstruction, hyperspectral data integration, and hardware-accelerated SLAM systems using FPGAs and GPUs. He has developed open-source tools like the Las Vegas Reconstruction Toolkit (LVR) and contributed to ROS packages for 3D mapping and navigation. Scientific Awards: Karman Innovation Award (2007) for his master’s thesis Intevation Free Software Award (2014) for his contributions to open-source software Advising & Grants: He has supervised over 60 bachelor’s and master’s theses, primarily in the areas of 3D mapping, SLAM, and robotics systems. His projects include SOILAssist (2019–2021), focusing on sustainable agriculture using robotics, and 3DinOS (2016–2017) for 3D documentation of historical buildings. He has also worked on DFG-funded projects like RoboRithmics . Labs & Teams: He is part of the Knowledge Based Systems Group at Osnabrück University and collaborates with the DFKI Robotics Innovation Center . His lab focuses on advanced 3D perception, semantic mapping, and energy-efficient robotic systems.

Dinesh Acharya UView profile

Professor

• Machine Learning
• Data Mining
• Image Processing
• +4 more

Dinesh Acharya U serves as a Professor in the Department of Computer Science and Engineering at Manipal Institute of Technology, Manipal University. His academic leadership spans both foundational computer engineering and interdisciplinary medical applications, with research output consistently growing since 2006. Current affiliations include active roles at the School of Computer Engineering with verified ORCID profile (0000-0002-0304-4725) and institutional webpage. Research interests prominently feature Machine Learning (68% fingerprint weight), Data Mining (40%), and Medical Informatics applications. His work bridges computer science with healthcare challenges, particularly in neonatal sepsis detection, diabetic complications, and low-resource language processing. The research fingerprint shows strong emphasis on prediction (52%), algorithms (53%), and India -specific healthcare contexts (40%). Publication trends reveal accelerating output since 2018, with 12 papers in 2022 and continued productivity through 2025. Recent work demonstrates interdisciplinary convergence, particularly in Medical AI (neonatal sepsis, diabetic kidney disease) Natural language processing for low-resource languages Transformer-based architectures across domains Notable patterns include increasing clinical collaborations and emphasis on practical implementation tools. Professional recognition includes an h-index of 13 with 596 citations across 71 research outputs. Key distinctions: Scopus profile verification ORCID registration Multi-institutional collaborations evident in co-authorship Academic supervision and grant activity cannot be confirmed from available data, though 15+ recent publications suggest active research teams. Current projects appear focused on Medical diagnostic tool development Low-resource language technology Clinical decision support systems with evident laboratory infrastructure supporting computational healthcare research.

• Optical Imaging
• Biomedical Imaging
• Artificial Intelligence
• +5 more

Dr. Jungeun (Jenny) Won serves as Assistant Professor of Research in the Department of Biomedical Engineering at the University at Buffalo's School of Engineering and Applied Sciences, with an affiliated appointment in Ophthalmology at the Jacobs School of Medicine & Biomedical Sciences. She directs the Translational Biophotonics Laboratory, developing optical imaging technologies for clinical translation in otolaryngology and ophthalmology. Postdoctoral associate, Massachusetts Institute of Technology (2024) PhD, Bioengineering, University of Illinois Urbana-Champaign (2021) MS, Bioengineering, University of Illinois Urbana-Champaign (2017) BS, Biomedical Engineering and Minor in Optics, University of Rochester (2015) Her research pioneers translational optical imaging with core expertise in optical coherence tomography (OCT) , biomedical instrumentation , and AI-driven image analysis . She develops clinically feasible devices for middle ear infection diagnosis (otitis media), retinal disease monitoring (AMD, diabetic retinopathy), and bacterial biofilm characterization , integrating computational methods to advance disease diagnostics and treatment monitoring. Recent publications (2021-2025) reveal dominant trends in handheld OCT device development for point-of-care use, machine learning integration for automated image classification, and multimodal approaches combining OCT with Raman spectroscopy. Her work bridges engineering innovation with clinical applications across ophthalmology and otolaryngology, emphasizing real-world translation. National Alliance for Eye and Vision Research Emerging Vision Scientist Program (2024) Bob Bilger Graduate Award for Hearing Research (2020) McGinnis Medical Innovation Graduate Fellowship (2020) Baxter Young Investigator Award (2019) Biannual Symposium Travel Scholarship Award (2019) Nadine Barrie Smith Memorial Fellowship (2017) Walt and Bobbi Makous Prize for Vision Research (2015) Xerox Engineering Research Fellowship (2014) As Principal Investigator, Dr. Won secures translational research funding including: UB CTSI Translational Pilot Studies Program ($49,700, 2025) for pediatric otitis media imaging UB Research Programs ($10,460 + $9,750, 2024-2025) for AI-assisted retinal imaging and handheld probe prototyping Her laboratory mentors students in biomedical imaging, device development, and computational analysis while advancing clinical partnerships for technology validation. The Translational Biophotonics Laboratory unites engineers, clinicians, and scientists to develop light-based imaging solutions addressing unmet clinical needs in ear and eye diseases, with active projects spanning middle ear biofilm characterization, retinal blood flow quantification, and AI-enhanced diagnostic platforms.

Jonathan M. AitkenView profile

Senior Lecturer

• Autonomous robotics
• Computer vision
• Spatial awareness in robotic systems
• +4 more

Dr. Jonathan M. Aitken is a Senior Lecturer in Robotics at the University of Sheffield's School of Electrical and Electronic Engineering. Previously a Research Fellow at the Autonomous Control Laboratory (ACSE), his work focuses on autonomous robotic systems with emphasis on quadcopters, computer vision, spatial awareness, and multi-robot collaboration. Expert in safe autonomous drone deployment Specializes in collaborative robotics (cobots) Qualified UK commercial drone pilot Research spans: Autonomous reconfiguration of robotic systems Dynamic risk assessment for systems-of-systems Visual SLAM for feature-sparse environments Formal verification of UAS systems Human-robot co-working interfaces Recent publications examine: Robust localization in sewer pipes Visual saliency algorithms for mobile robots Cobotic safety controller synthesis Modular digital twinning frameworks Spatial awareness for multi-robot teams Major grants include EPSRC Programme Grants for buried pipe sensing and Lloyds Registry Foundation funding for cobot safety integration.

• Medical Image Processing
• Computer Vision
• Biomedical Engineering
• +3 more

Yinghua Fu, PhD, serves as a Research Fellow in the Department of Diagnostic Radiology and Nuclear Medicine, focusing on advanced medical image analysis through computational methods. Her work bridges clinical medicine and artificial intelligence to develop practical diagnostic tools. Her research centers on deep learning applications for medical imaging, particularly in diabetic retinopathy analysis, Alzheimer's disease classification, and retinal image change detection. Key innovations include DAGU-Net for segmentation efficiency, TSCA-Net for attention-based spatial-channel processing, and lightweight architectures like MirageNet that minimize computational demands while maintaining accuracy. She specializes in adapting computer vision techniques to clinical constraints, emphasizing model interpretability and real-world deployability. Analysis of her 15 most recent publications (2023-2026) reveals three dominant trends: (1) Multi-scale feature extraction for medical image segmentation, (2) Lightweight network design targeting edge deployment in clinical settings, and (3) Cross-modal fusion techniques integrating structural and functional imaging data. Her work consistently addresses diabetic retinopathy and neurodegenerative disorders through retinal and brain imaging biomarkers. Scientific awards: No awards were documented in the provided materials. Advising and grants: The source text contained no information regarding student supervision, research funding, or collaborative grants.

Florence TupinView profile

Professor

• Synthetic Aperture Radar (SAR)
• Image Processing
• Deep Learning
• +3 more

Florence Tupin is a Professor at Télécom Paris (Institut Polytechnique de Paris) and head of the Image, Data, Signal department. She leads the IMAGES research team at LTCI Laboratory, focusing on advanced SAR image processing, deep learning applications in remote sensing, and 3D reconstruction for earth observation. Her work bridges theoretical models and large-scale geospatial data exploitation. Her research integrates statistical modeling , non-local filtering , and deep neural networks to address challenges in SAR despeckling, tomographic imaging, and multi-temporal analysis. She directs projects like ANR ASTRAL (2022-2025) and ALYS (2015-2021), developing open-source tools such as MuLoG and MERLIN for the remote sensing community. Publication analysis reveals dominant themes: deep learning-driven SAR restoration (45%), 3D reconstruction (25%), multi-temporal change detection (20%), and polarimetric analysis (10%). Her group consistently pioneers self-supervised methods for resource-efficient remote sensing. Awards & Recognition: IEEE Geoscience and Remote Sensing Society Distinguished Achievement (2025) Advising & Grants: Directs 9 PhD students and 4 postdocs; secured €2M+ funding (ANR, CNES, CEA). Recent PhDs focus on deep learning for SAR tomography, multi-modal fusion, and Arctic monitoring. Leads Futur & Rupture initiatives on forest biomass estimation. Labs & Teams: Heads the IMAGES team (20 researchers) at LTCI Laboratory. Collaborates with CNES, CEA, and international partners (Wuhan University, Arctic University of Norway) on satellite data exploitation.

• Sensor Technology
• Quantum Physics
• Optics
• +3 more

Prof. Dr.-Ing. Barbara Hippauf is a faculty member at Saarland University of Applied Sciences, where she serves as Head of the Technical Physics Laboratory within the Faculty of Engineering. She studied physics at the University of Karlsruhe (TH) in 1987, specializing in sensor technology, quantum physics, and optics. Her academic career includes roles at the University of the Federal Armed Forces Munich, Siemens AG, and the University of Karlsruhe (TH), where she completed her dissertation in 2000. She has been teaching Experimental Physics and Technical Physics since 2009. Born in Rotenburg an der Fulda, raised in Lima, Peru PhD in Electrical Engineering (Materials) from University of Karlsruhe Extensive industrial experience at Siemens AG and Alpha Engineering GmbH Current research focuses on precision engineering, solar thermal systems, and ceramic sensors Her research spans sensor technology , optical modeling , and solid-state materials , particularly in ceramic oxygen sensors and solar thermal systems. She has developed ray tracing simulations for light distribution analysis and contributed to statistical precision measurement in complex systems. At htw saar, she leads the Technical Physics Laboratory and contributes to academic governance as deputy chair of examination committees and member of master's admission commissions. She also serves as Bafög representative and equality officer for engineering students. Her publications and patents focus on: Self-heated porous oxygen sensors Noise compensation in acoustic systems Optical inspection methods for industrial automation Kinetic modeling of ceramic materials

David NasoView profile

Professor

• Control Systems
• Mechatronics
• Smart Materials
• +8 more

David Naso is a Full Professor at the Department of Electrical and Information Engineering (DEI) of the Polytechnic University of Bari, Italy. He teaches courses in feedback control, optimization, and system identification, and serves as the head of the DEI Robotics Laboratory and coordinator of the Master's Degree in Automation Engineering. PhD in Automatic Control from Polytechnic University of Bari (1998) Guest researcher at Technical University of Aachen (1997) Visiting professor at Saarland University (2013) His research focuses on robust control of aeronautical engines, control of high-speed electrical machines, mechatronic devices based on smart materials, distributed automation, and fault prevention with sensor networks. He has coauthored over 250 publications cited more than 3,000 times (h-index 29). Recent publications highlight expertise in control systems for dielectric elastomer actuators, energy management in hybrid-electric aircraft, and fault-tolerant microgrids. His work spans aerospace, robotics, and renewable energy systems. He leads the "Control Systems Team" at Energy Factory Bari (a GE Avio-Polytechnic partnership), and collaborates with AROL S.p.A for food industry robotics and Casillo Group for windmill monitoring systems. He sits on multiple Politecnico subsidiary committees. He has coordinated research projects with €2M+ funding and consulting contracts worth €400k. Currently serves as technical editor for IEEE/ASME Transactions on Mechatronics.