Anne Fischer, M.Sc., is a researcher at the Chair of Material Handling, Material Flow, and Logistics at the Technical University of Munich (TUM). Her work focuses on digital twins, construction automation, and resource scheduling in heavy civil engineering. She is based in Garching near Munich and collaborates with institutions like UC Berkeley and Stanford University. Research Interests: Digital Twin frameworks, simulation-based optimization, BIM integration, activity recognition in construction, and sustainable logistics systems. Collaboration: Serves as a contact person for international exchanges with U.S. institutions. Publication Trends: Her recent articles (2024–2021) address construction automation, digital twin applications, and variability management in civil engineering projects. Key Projects: Engaged in initiatives like Bauen 4.0 , MiProcess2Twin , and SiteRoute , which focus on digitalization and automation in construction. Location: Boltzmannstraße 15, Garching bei München (Room: 5505.EG.501).
Prof. Dr. Matti Schneider serves as Professor of Engineering Mathematics and Head of the Institute of Engineering Mathematics within the Faculty of Civil Engineering at the University of Duisburg-Essen. His academic leadership spans computational mechanics research and teaching core mathematics courses for civil engineering students. His educational background includes: Diploma in Applied Mathematics with distinction from TU Bergakademie Freiberg (2009) PhD (Dr. rer. nat.) from Leipzig University (2013) on "The Leray-Serre spectral sequence in Morse homology on Hilbert manifolds and in Floer homology on cotangent bundles" Professor Schneider's research focuses on advancing computational methods for solid mechanics through FFT-based homogenization techniques, microstructure modeling, and multi-scale material analysis. His work bridges applied mathematics and engineering to solve complex problems in heterogeneous material systems, with particular emphasis on numerical stability, boundary condition implementation, and efficient solver development for industrial applications. His methodologies enable accurate prediction of material behavior across scales from microscopic structures to macroscopic components. Analysis of his 15 most recent publications reveals dominant trends in FFT-based computational homogenization, with significant contributions to thermal problems, porous media, and fiber-reinforced composites. He pioneers the integration of machine learning (particularly deep material networks) with traditional numerical methods to model complex material behaviors like shear-thinning suspensions and 3D-printed materials. His work consistently addresses computational challenges in boundary condition implementation and convergence for stochastic microstructures. Professor Schneider leads the Institute of Engineering Mathematics and directs research within the ERC-funded BeyondRVE project, which focuses on extending representative volume element concepts for advanced material modeling. His collaborative network includes major German research institutions like Fraunhofer ITWM and international partners in materials science.
Dr. rer. nat. Xu Li is a researcher at the Institute of Semiconductor Technology within the TU Braunschweig (Technische Universität Braunschweig), Germany. Affiliated with the Faculty of Electrical Engineering, Information Technology, Physics , Xu Li contributes to interdisciplinary research spanning materials science, environmental science, and computer engineering. Fields of Interest : Materials Science, Environmental Science, Biotechnology, Sensor Technology, Computer Science, Civil Engineering, IoT Contact : xu.li@tu-braunschweig.de Xu Li's research focuses on: Materials Science : Developing magnetoelectric sensors with energy harvesting capabilities, exploring concrete shrinkage mechanisms using porous aggregates. Environmental Science : Investigating microplastic impacts on soil ecosystems and carbon dynamics in tea plantations. Biotechnology : Advancing PCR-based mutation detection for viral pathogens. Computer Science : Innovating face recognition algorithms and agricultural IoT systems.
Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion
Jorge Macedo is an Assistant Professor and Frederick L. Olmsted Early-Career Professor at the School of Civil and Environmental Engineering , Georgia Institute of Technology. He received his B.S. and M.S. in civil engineering and soil mechanics from the Peruvian National University of Engineering (2007-2011), followed by M.S. (2014) and Ph.D. (2017) in Geoengineering from UC Berkeley. Education: B.S. Civil Engineering (2007), Peruvian National University of Engineering M.S. Soil Mechanics (2011), Peruvian National University of Engineering M.S. Geoengineering (2014), UC Berkeley Ph.D. Geoengineering (2017), UC Berkeley His research focuses on geotechnical earthquake engineering , advanced numerical modeling (FEM, FDM, MPM), performance-based design , and mining geotechnics . He applies machine learning and reliability tools to assess seismic risks, particularly in liquefaction and residual drift modeling. Recent work examines nonergodic ground motion models, slope stability under subduction earthquakes, and mine tailings behavior. The 2025-2024 publications highlight trends in machine learning for hazard assessment , nonergodic ground motion modeling , and mine tailings analysis . Articles address slope systems, liquefaction effects, and physics-informed neural networks in seismic analysis. Scientific Awards: Young Researcher Award (2023), ISSMGE Technical Committee NSF CAREER Award (2022) Dr. Macedo's work bridges academic research with industry applications , including collaborations with Golder Associates and contributions to geotechnical asset management in Georgia. He actively participates in curriculum development, emphasizing data analytics and computational skills.
Dr Richard Collins is a Senior Lecturer in Water Engineering at the University of Sheffield , affiliated with the School of Mechanical, Aerospace and Civil Engineering. His research focuses on hydraulic transients , pipeline integrity , and smart water infrastructure . Graduated with an Aerospace Engineering degree (2005) and PhD in Materials and Mechanical Engineering (2009) Current research explores pressure transients , leak detection , and autonomous robotic systems for pipeline inspection Projects include fatigue analysis , biofilm mobilisation , and ultrasound-based pipe assessment His publications emphasize cast iron pipe fatigue , acoustic leak detection , and transient-induced contamination . Funded by RCUK and Datatecnics , his work bridges mechanical engineering and civil infrastructure challenges.
Dr James Shucksmith is a Senior Lecturer in Water Engineering at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. After completing his undergraduate degree and PhD at the same department, he joined the academic staff in 2010 following a KTP associate role with Yorkshire Water. His research focuses on urban flooding hydrodynamics, water quality modeling, and sustainable drainage systems. Co-director of EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience Current projects: Real Time Abstraction Management (with Severn Trent Water), Centaur FloodInteract Research interests include: Urban flood hydrodynamics and drainage-surface flow interactions Water quality forecasting tools for surface water abstraction Development of local real-time control systems for urban drainage Experimental validation of flood models using PIV measurements His publications (2010-2025) cover topics like contaminant transport in flooded sewer systems, longitudinal dispersion modeling, and real-time control optimization. Recent work focuses on data-driven approaches for Cryptosporidium prediction and E. coli forecasting.
Prof. Monika Sester is a distinguished Professor and Executive Director of the Institute of Cartography and Geoinformatics at Leibniz University Hannover, within the Faculty of Civil Engineering and Geodetic Science. She also serves as Spokesperson for the Leibniz Research Center FZ:GEO and holds multiple leadership roles including Faculty Information Officer (FIO) for the Faculty of Civil Engineering and Geodetic Science, Ombudsman for Good Scientific Practice, and Exchange Coordinator for Geodetic Science and Geoinformatics. Her research focuses on the intersection of geospatial information science, cartography, and urban mobility. Prof. Sester's work spans several key areas: Geospatial data processing and analysis Cartographic representation and visualization Urban mobility and transportation systems Spatial data uncertainty and quality Digital mapping technologies and applications Historical map analysis and interpretation Prof. Sester's recent publications demonstrate a strong focus on applying advanced computational techniques to geospatial problems. Her work shows increasing emphasis on machine learning applications for map analysis, urban mobility optimization, and 3D spatial modeling. She has been particularly active in researching applications of deep learning for historical map interpretation, urban mobility patterns, and spatial uncertainty visualization. Her contributions to the field have been recognized through leadership positions in major research initiatives: Executive Director, Institute of Cartography and Geoinformatics Spokesperson, Leibniz Research Center FZ:GEO Faculty Information Officer, Faculty of Civil Engineering and Geodetic Science Ombudsman for Good Scientific Practice Member of multiple academic committees including the Admissions and Examination Board Prof. Sester actively collaborates with students and researchers across multiple projects focused on geospatial information systems, urban mobility, and cartographic visualization. Her leadership extends to guiding research directions within the Leibniz Research Center FZ:GEO, which brings together interdisciplinary expertise to address complex spatial challenges.
Maarten Bassier is an Assistant Professor (tenure track) at KU Leuven, affiliated with the Department of Civil Engineering within the Faculty of Engineering Technology. He is based at the Geomatics unit operating at the Ghent and Aalst Campuses. His academic profile combines research, teaching, and institutional service, with significant contributions to the field of digital construction technologies. As senior academic staff, he serves on both the Council of the Faculty of Engineering Technology and the Civil Engineering Department Council, actively participating in institutional governance while maintaining a robust research program focused on Scan-to-BIM methodologies and geospatial applications in construction. Dr. Bassier's research centers on Scan-to-BIM methodologies, which involve converting 3D scans of existing buildings into Building Information Models. His work bridges geomatics, computer vision, and civil engineering, with applications in construction progress monitoring, infrastructure inspection, and heritage documentation. He applies machine learning techniques to automate aspects of the modeling process, particularly semantic segmentation of point clouds and integration of UAV (drone) data. His research increasingly incorporates deep learning approaches for object detection, segmentation, and completion in complex built environments, with practical applications spanning road construction, bridge inspections, and electrical substation modeling. His interdisciplinary approach connects civil engineering with computer science to solve practical construction challenges through digital innovation. Bassier's recent publication record demonstrates a strong focus on automating the Scan-to-BIM process through advanced computational techniques. His work spans multiple application domains while maintaining a consistent methodological thread of integrating sensing technologies with semantic understanding of construction environments. The trajectory of his research shows increasing sophistication in machine learning applications, moving from basic point cloud processing to complex semantic understanding and automated model generation. His publications appear in high-impact journals across civil engineering, remote sensing, and computer vision domains, reflecting the interdisciplinary nature of his work. SESAME - Semantic Segmentation of Electrical Substations and Derived Models for Engineering (2024-2026) - Promotor UAV-assisted bridge inspections (2022-2027) - Co-promotor XR-empowered dynamic reality modeling for AECO applications (2021-2026) - Co-promotor Digitization in road construction: automating as-built models (2020-2026) - Co-promotor SCAN-to-BIM Automation of as-built BIM production through digitization and machine learning (2020-2025) - Co-promotor As a member of the Division Digital and Sustainable Civil Engineering and the Subdivision Geomatics Ghent, Dr. Bassier contributes to KU Leuven's research ecosystem focused on digital transformation in civil engineering. His teaching portfolio includes courses on BIM, industrial measurements, Scan-to-BIM, 3D modeling, and geomatics, preparing the next generation of civil engineers for the digital construction landscape. His work represents the cutting edge of digital construction technologies, with practical applications that address real-world challenges in infrastructure development and maintenance.
Ahed Alboody is a Professor and Researcher at HESAM University Group, specifically affiliated with CESI and the Digital Innovation Laboratory for Businesses and Learning to Support Territorial Competitiveness (LINEACT) in Nice, France. He holds a specialized doctorate in computer science from the University of Toulouse 3 Paul Sabatier and has extensive experience in deep learning, computer vision, and remote sensing applications. His work bridges academic research with practical applications in environmental monitoring, human-computer interaction, and spatial reasoning systems. Education: Specialized Doctorate in Computer Science, University of Toulouse 3 Paul Sabatier (IRIT), 2011 Master 2 Research in Electronics, Automation and Systems Engineering, National Polytechnic Institute of Toulouse (INPT-ENSEEIHT), National School of Civil Aviation (ENAC), ISAE-SUPAERO, and University of Toulouse III, 2006 Engineering Diploma in Electronics and Telecommunications, University of Tishreen (Techrine), Lattakia, Syria, 2002-2003 Undergraduate studies in Electronics and Telecommunications, University of Tishreen (Techrine), Lattakia, Syria, 2002 Alboody's research focuses on advanced applications of deep learning and computer vision, particularly in the areas of 3D hand gesture recognition, hyperspectral and multispectral image processing, and semantic segmentation. His work combines theoretical advancements in mixture-of-experts architectures with practical applications in remote sensing and environmental monitoring. He has pioneered approaches in frugal learning and zero-shot learning for image segmentation tasks, with applications in digital twins and collaborative robot environments. His publication record demonstrates a clear evolution from foundational work in spatial reasoning systems (2008-2012) to current cutting-edge research in deep learning architectures for 3D gesture recognition and hyperspectral image analysis. Recent publications (2022-2024) show a strong focus on mixture-of-experts transformers, parallel architectures for efficient computation, and applications in environmental monitoring with drones and satellite imagery. Alboody actively supervises Master's level research projects (two M2 level projects mentioned) and serves as a reviewer for prestigious journals including IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Geoscience and Remote Sensing. He has also been a member of the Technical Program Committee for international conferences on databases and knowledge applications. His laboratory work centers around the Digital Innovation Laboratory for Businesses and Learning to Support Territorial Competitiveness (LINEACT), where he leads research in engineering and digital tools. Current projects include developing graph neural networks for 3D hand gesture recognition using depth and skeleton data, and implementing frugal learning approaches for semantic image segmentation in collaborative robot environments.
Jean-Daniel Penot is a Researcher at CESI's Research and Innovation Department , with expertise in additive manufacturing, materials science, and industrial integration. His work bridges advanced manufacturing technologies with environmental sustainability and educational innovation. Doctorate in Materials Physics (2010) Engineering Degree in Physics (2007) Research Master in Optoelectronics (2007) Penot's research spans Additive Manufacturing and its applications in automotive, nuclear, and construction sectors. He focuses on Laser-Material Interaction , Machine Learning for process optimization, and Sustainable Engineering through life cycle assessments and geopolymer applications. His recent publications emphasize BIM , AM Modular Plants , and Defect Analysis in 3D-printed metals. Penot leads France Additive initiatives and contributes to International Standards as a board member. Penot supervises PhD students including Maryam Houhou and Amal Khabouchi , with a focus on Industrial Security and Energy Transitions . His projects integrate Thermal Comfort , Ultrasonic Inspection , and Quality Assurance in additive manufacturing systems.
Julia K. Green is an Assistant Professor at the University of Arizona , specializing in environmental science. Her research focuses on biosphere-atmosphere interactions and linkages between carbon, water, and energy fluxes through vegetation activity. Ph.D. in Environmental Engineering, Columbia University M.Phil. in Earth and Environmental Engineering, Columbia University MS in Environmental Engineering, Columbia University BS in Civil Engineering, Tufts University Green employs big datasets from climate models, remote sensing, flux towers, and field measurements to study vegetation stress and global change impacts. Her work intersects climate change, drought impacts, and critical zone science. She can be contacted via email at juliakgreen@arizona.edu , and her lab's research emphasizes data-driven approaches using machine learning techniques.
Professor Vasilis Sarhosis is a Professor of Resilient Structures and Infrastructure at the School of Civil Engineering, University of Leeds, within the Faculty of Engineering and Physical Sciences. He also serves as a visiting Professor at Southeast University, Nanjing, China, and is a CDRI (Coalition for Disaster Resilience Infrastructure) Fellow. His academic credentials include multiple degrees from the University of Leeds: a Ph.D. in Computational modelling of low bond strength masonry (2012), an MSc in Sustainable Waste Management (2006), an MSc in Infrastructure Asset Maintenance and Management (2004), and a BEng in Civil Engineering (2003). Professor Sarhosis's research focuses on understanding the long-term behaviour of existing masonry infrastructure and historic structures. His work encompasses both 'blue skies' research and 'near-to-market' development, with a strong translational focus. His research group investigates inspection, monitoring and condition appraisal of masonry infrastructure; structural assessment; and maintenance, repair and rehabilitation of masonry infrastructure. His expertise spans machine learning, artificial intelligence, structural health monitoring, sensor technology, digital twins, cultural heritage preservation, and masonry structures. Analysis of his recent publications (2021-2023) reveals a strong focus on computational modeling of masonry structures, particularly using discrete element methods. His work integrates artificial intelligence and machine learning techniques for structural health monitoring and assessment. Key research areas include seismic performance of masonry structures, digital twin technology for infrastructure assessment, and the development of smart materials for structural monitoring and rehabilitation. His work frequently addresses heritage conservation challenges and disaster resilience for masonry infrastructure. Scientific Awards: International Partnership Award, University of Leeds (2021) Queen's Anniversary Prize for Higher and Further Education (2013) First price award for Commercialisation of Research: Project sDNA (2014) Professor Sarhosis has successfully secured significant research funding including EPSRC grants, UKRI HEIF funding, MSCA-IF projects, and British Academy GCRF funding. He serves as Subject Editor for Engineering Failure Analysis (Elsevier) and Associate Editor for the International Journal of Masonry Research and Innovation. He has examined numerous PhD theses at institutions worldwide including Heriot-Watt University, Newcastle University, Politecnico di Milano, and ETH-Zurich. His professional memberships include Fellow of the Institution of Civil Engineers (FICE), Fellow of the Institute of Mechanical Engineers (FIMEchE), and Chartered Engineer (CEng).
Chrysanthos Maraveas is an Assistant Professor at the Department of Technology and Natural Resources Management & Agricultural Engineering in the School of Environment and Agricultural Engineering at the Agricultural University of Athens since 2022. He earned his PhD in Structural Engineering from the University of Manchester and has held postdoctoral research positions at the University of Liège (EU-funded) and the University of Patras (Greek Ministry of Development-funded). Research Interests : Applications of AI and quantum computing in agriculture Sustainable materials and biodegradable polymers from agricultural waste Internet of Things (IoT) for greenhouse optimization Structural durability and corrosion resistance in agricultural environments Plastic waste management in agrifood systems 4D printing for sustainable agricultural plastics Scientific Recognition : Ranked in the top 2% of scientists worldwide by Stanford University (based on citations and h-index) Publications focus on: Cybersecurity in Agriculture 4.0/5.0 Smart sensors and edge computing for resource management Biopolymer innovations and nanotechnology Structural analysis of agricultural systems Sustainable construction materials from agro-waste Fire resistance in steel and composite structures
Rui Pedro Carvalho Lima de Sousa is a postdoctoral researcher at the Textile Science and Technology Center (2C2T) of the University of Minho. His career focuses on organic-inorganic hybrid materials for sensors and advanced textile applications, with expertise in sol-gel chemistry , electrospinning , and optical sensing . PhD in Applied Chemistry (University of Minho, 2023), thesis: "Sol-gel based optical sensors for monitoring of biocides and other analytes" MSc in Chemical Characterization and Analysis Techniques (2018), BSc in Biochemistry (2016) Research interests span: Development of smart textile materials for filtration and adsorption via electrospinning Biocide detection using quinoline and hydrazone derivatives Hybrid sol-gel matrices for corrosion protection and concrete durability monitoring Integration of machine learning in sensor design Recent publications highlight work in NIR fluorescent probes , ion chemosensors , and environmental monitoring applications. Key collaborations include projects with the Institute of Systems Engineering and Computers (INESC) and University of Trás-os-Montes . Scientific recognitions : Best Doctoral Thesis in Chemistry (PYCA Award) FlashChem Photography Contest (2024) Advising includes mentoring students like Oscar Martinez-Rico and Joana Rocha on electrospun membrane projects, with past contributions to organic-inorganic hybrid material research.
