Michael Hicks is a Professor at Delft University of Technology (TU Delft) in the Civil Engineering & Geosciences school, specializing in Geo-engineering . His research focuses on geotechnical risk assessment, soil variability, and advanced numerical methods like the Material Point Method (MPM) to model slope failures, earthquake simulations, and soil-structure interactions. Editorial board member for Géotechnique , Georisk , and Computers and Geotechnics (2019) Keynote speaker at workshops on slope failure modeling (2018) Active in public engagement about flood risk management (2017–2018) His work addresses challenges in soil heterogeneity, dynamic boundary conditions, and reliability-based design for infrastructure. Notable contributions include developing probabilistic MPM frameworks and machine learning tools for CPT data interpretation, with applications in earthquake resilience and radioactive waste repository safety. Recent publications highlight innovations in 3D slope stability analysis, nonlocal soil deformation modeling, and thermomechanical interface behavior under cyclic loading. Collaborations span academia and industry, focusing on improving numerical accuracy and understanding failure mechanisms in geotechnical systems.
Elyar Pourrahimian is a Postdoctoral Fellow at the University of Alberta's Faculty of Engineering, specifically within the Civil and Environmental Engineering Department. He teaches courses such as CIV E 303 - Project Management (Winter Term 2026) and CIV E 601 - Analytical Methods for Project Management (Fall Term 2025), focusing on project planning, scheduling, and control methodologies. His research interests span Construction Management Project Management Artificial Intelligence Applications in Engineering Chaos Theory in Project Planning Fuzzy Systems in Labour Productivity Bayesian Inference in Construction Simulation . Recent publications highlight trends in construction workspace optimization (2025), chaos and fuzzy systems for productivity analysis (2025-2022), and machine learning frameworks (2024) for construction monitoring. He also explores multidimensional project control (2024) and socio-technical lean management frameworks (2024).
Dr. Sue Ahn serves as a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, where she leads research in traffic flow theory, Intelligent Transportation Systems (ITS) applications, and traffic operational impacts on environment and safety. Her work bridges theoretical traffic dynamics with practical transportation solutions through quantitative analysis and ITS implementation. Her academic foundation includes: PhD in Civil and Environmental Engineering, University of California, Berkeley (2005) MS in Civil and Environmental Engineering, University of California, Berkeley (2001) BS in Civil Engineering, Ohio State University (2000) Dr. Ahn's research program focuses on three interconnected pillars: fundamental traffic flow understanding through observation and statistical methods; environmental and safety impact analysis of traffic phenomena; and development of traffic control strategies using ITS. Her expertise spans traffic flow modeling , adaptive traffic control systems , emissions quantification , and connected/automated vehicle integration , with recent work emphasizing machine learning applications in mixed-traffic environments. She translates theoretical insights into practical transportation management tools while maintaining rigorous scientific methodology. Analysis of her 2023-2024 publications reveals dominant themes in connected/automated vehicle control (70% of output), traffic flow theory advancements (20%), and environmental impact assessment (10%). Key methodological trends include deep learning integration (LSTM networks, graph neural networks), stochastic behavioral modeling, and multi-dimensional trajectory optimization – reflecting the field's shift toward data-driven solutions for heterogeneous traffic systems. Dr. Ahn's scientific recognition includes: 2023 Transportation Research Board Greenshields Prize (Best Paper Award) 2020 University of Wisconsin-Madison Vilas Associate award 2019 Transportation Research Board Best Paper in Traffic Flow Theory 2016 Transportation Research Board Cunard Award 2012 NSF CAREER Award 2008 Annual ITS Arizona Conference Best ITS Planning Project She actively mentors graduate researchers through CIV ENGR 890 (Pre-Dissertator's Research) and CIV ENGR 990 (Thesis) courses, with her NSF CAREER grant providing foundational support for traffic flow theory investigations. Current advising focuses on automated vehicle behavior modeling and environmental impact quantification, while future work appears directed toward heterogeneous traffic management frameworks and real-world ITS deployment validation.
Ulrik Dam Nielsen is an Associate Professor in the Section for Fluid Mechanics, Coastal and Maritime Engineering at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). He also held an external position as Associate Professor II at the Norwegian University of Science and Technology (NTNU) from 2014 to 2023, reflecting strong international collaboration. His work contributes to UN Sustainable Development Goals related to sustainable maritime operations and clean energy. His research focuses on naval architecture and ship motion dynamics , particularly in the context of sea state estimation , added resistance in waves , and real-time prediction of vessel responses . He integrates data analytics , estimation theory , and machine learning to develop methods for monitoring hydrodynamic performance and enhancing maritime safety and energy efficiency. A central theme of his work is using ships as mobile wave sensors—transforming operational vessels into 'sailing wave buoys' for environmental monitoring. His recent publications show a clear shift toward data-driven methodologies, especially machine learning applications in sea state estimation, added resistance modeling, and performance monitoring. These works span journals like Ship Technology Research and Journal of Offshore Mechanics and Arctic Engineering , and conferences such as IEEE MetroSea, highlighting interdisciplinary innovation at the intersection of classical marine engineering and modern AI. Best Paper Presented by a Young Researcher Award (First Classified), 2024 (jointly awarded) He actively supervises PhD students—such as R. E. G. Mounet, M. Mittendorf, J. P. Tomy, and A. Oikonomakis—on projects funded by DTU and collaborative initiatives. His leadership in projects like WEFOSWAB (Wave Estimation and Forecasting Using Ships as Buoys) and data-driven added resistance modeling underscores his role in advancing smart maritime technologies. He also contributes to open science through the public release of datasets such as NetSSE . He teaches core courses including Introduction to Ships and Floating Structures , Marine and Ocean Engineering , and Ship Operations , shaping the next generation of maritime engineers.
Dr. Ewa Zaborowska is an Associate Professor at the Department of Sanitary Engineering within the Faculty of Civil and Environmental Engineering at Gdańsk University of Technology. Her research focuses on environmental engineering with particular emphasis on wastewater treatment processes, greenhouse gas emissions, and sustainable energy recovery from wastewater treatment plants. Her research interests span across several critical areas in environmental engineering: Advanced machine learning applications for predicting nitrous oxide emissions in wastewater treatment Biogas production optimization through anaerobic digestion processes Carbon footprint analysis of wastewater treatment facilities Energy recovery pathways from wastewater treatment processes Mitigation strategies for achieving net-zero carbon conditions in wastewater treatment plants Dr. Zaborowska's recent publications (2022-2023) demonstrate a strong focus on sustainability challenges in wastewater management, with particular attention to greenhouse gas emissions (CH 4 and N 2 O) and carbon footprint reduction strategies. Her work often involves comparative studies between different countries (particularly Poland and Finland in the Baltic Sea region) and explores innovative approaches for energy recovery from wastewater treatment processes. Her scientific contributions include: Development of algorithms for selecting appropriate machine learning models for environmental prediction tasks Investigation of low-temperature pretreatment methods for enhancing biogas production Systematic reviews of net-zero carbon strategies for wastewater treatment plants Comprehensive carbon footprint analyses of wastewater treatment facilities Dr. Zaborowska actively collaborates with researchers from various institutions on projects related to sustainable wastewater management and greenhouse gas mitigation. Her work contributes significantly to the advancement of environmentally sustainable practices in the wastewater treatment sector.
Professor Luming Shen is a distinguished academic in the School of Civil Engineering at The University of Sydney. With over two decades of experience in mechanical behavior of materials research, he leads cutting-edge investigations at the intersection of civil engineering, materials science, and computational mechanics. His work spans multiple scales from nano to macro, focusing on fundamental understanding that can be applied to real-world engineering challenges in water purification, structural safety, and sustainable infrastructure. Professor Shen's educational background includes: Bachelor's degree in Building Engineering from Tongji University, China Master's degree in Structural Engineering from Tongji University, China PhD in Civil Engineering from the University of Missouri-Columbia, USA Professor Shen's research focuses on the mechanics and behaviors of materials across multiple scales. His primary interest lies in understanding both brittle materials (concrete, rock, glass) and ductile materials (aluminum, titanium, metals). Two major thrusts of his work include nano-mechanics and materials research, particularly developing carbon nanotube membranes for water purification, and studying novel composite materials under impact and extreme loading conditions for applications in blast-resistant structures and vehicle safety. He employs high-performance computing for molecular and macro-level analyses, complemented by physical laboratory testing. Professor Shen's extensive publication record demonstrates a consistent focus on multiscale modeling of materials behavior, with recent work emphasizing granular materials dynamics, carbon nanotube applications, 3D-printed concrete technology, and energy storage systems. His research shows a clear evolution toward increasingly complex multiphysics problems that integrate mechanical, thermal, and fluid dynamics phenomena at multiple scales. The interdisciplinary nature of his work bridges civil engineering, materials science, computational mechanics, and environmental engineering, with applications spanning from fundamental material science to practical civil infrastructure solutions. Professor Shen actively supervises multiple research students, including Yifang Cao working on 3D printing concrete, Jiangshuai Meng studying granular materials under impact loads, and Runda Wang applying machine learning to rock burst prediction. His research is supported by access to advanced computational resources and laboratory facilities at The University of Sydney, particularly through his membership in The University of Sydney Nano Institute. The university has provided specialized space and equipment necessary for conducting physical tests on materials under high-speed impact conditions. Professor Shen maintains active laboratory facilities for conducting physical tests on materials under various loading conditions, particularly high-speed impact testing. His work is supported by computational resources for molecular dynamics and multiscale modeling. As a member of The University of Sydney Nano Institute, he collaborates with interdisciplinary researchers working at the nanoscale, particularly in applications related to water purification technologies using carbon nanotube membranes.
Ramin Karim is a Professor and Head of Subject in the Department of Civil, Environmental and Natural Resources Engineering at Luleå University of Technology. His research focuses on operation and maintenance technology, with expertise in railway systems, industrial cybersecurity, structural health monitoring, and the application of advanced analytics in asset management. He leads the Operation, Maintenance and Acoustics division, emphasizing interdisciplinary approaches to solving complex engineering challenges. Key research areas include predictive maintenance strategies for railway infrastructure, cybersecurity frameworks for Industry 5.0, and the integration of metaverse technologies in industrial contexts. His work often involves data-driven methodologies such as point-cloud processing, game theory for cyber threat modeling, and digital twin concepts. Recent publications highlight his contributions to railway maintenance policy optimization, health monitoring of ground support systems in mining, and cybersecurity challenges in industrial systems. He has co-authored over 50 peer-reviewed articles, many appearing in high-impact journals like International Journal of Systems Assurance Engineering and Management and Frontiers in Virtual Reality . Ramin Karim’s research also explores emerging technologies like federated learning for digital twins, blockchain applications in railways, and human-centric predictive health management systems. His work aligns with initiatives such as the Reality Lab Digital Railway, aimed at advancing sustainable and digitally enabled transportation solutions.
Dr. Abi Nazari Geykli serves as Assistant Professor in the Applied Engineering & Technology Management Department at Indiana State University's Bailey College of Engineering & Technology since August 2024. Previously, he held postdoctoral positions at the University of Alabama at Birmingham (2024) and University of Memphis (2022). His academic credentials include: Ph.D. in Civil/Hydrotechnical Engineering, Azerbaijan University of Architecture and Construction (2014) M.S. in Civil/Water Resources Engineering, Sharif University of Technology (2003) B.S. in Agricultural Engineering (Water Resource Engineering), University of Tabriz (2000) Specializing in Water Resources Engineering , his research integrates hydrologic-hydraulic modeling with machine learning to address climate change impacts, urbanization effects, and flood risk. Key focus areas include groundwater recharge mechanisms , contaminant transport , and sustainable infrastructure resilience , leveraging AI/remote sensing for environmental solutions. His methodology bridges theoretical principles with real-world applications in watershed management. Analysis of his 2022-2025 publications reveals consistent emphasis on urban hydrology challenges, particularly flood modeling using multivariable ML approaches (2023), aquifer recharge under land-use changes (2023), and deep learning for streamflow prediction (2025). This demonstrates an evolving trajectory toward data-driven solutions for climate-urbanization interactions in water systems. Current research initiatives include: Principal Investigator: $7,500 project on Vigo County flood/pollutant transport (2025-2028) Principal Investigator: $4,154 Terre Haute wastewater management study using SWAT+ modeling (2025-2026) Co-PI: $4,708 nanocomposites materials research (2025-2026) He also serves as peer reviewer for ASCE Journal of Hydraulic Engineering, Ain Shams Engineering Journal, and other prominent publications.
Tamás Lovas serves as Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. He teaches advanced courses in Building Information Modeling, Laser Scanning, Remote Sensing, and Intelligent Transportation Systems while supervising diploma theses in Surveying and Geoinformatics Engineering. Education: 1994: High school graduation, Városmajor High School, Budapest 1999: Certified Surveyor and Geoinformatics Engineer, Budapest University of Technology, Faculty of Civil Engineering 2005: PhD (Earth Sciences), Budapest University of Technology and Economics, Faculty of Civil Engineering Research Interests: Dr. Lovas specializes in laser scanning technologies and geospatial data processing with emphasis on airborne and terrestrial point cloud analysis. His work bridges civil engineering applications and computational methods, particularly in infrastructure monitoring and digital representation. Processing, classification, and modeling of airborne laser scanned data Accuracy testing of terrestrial laser scanning Processing and modeling of terrestrial laser scanned data Comparative study of spatial data acquisition technologies His 2022-2025 publications demonstrate accelerating integration of artificial intelligence in point cloud processing, with significant contributions to road surface extraction, urban land cover classification, and BIM automation. Current research trends show strong focus on digital twin development for autonomous vehicles and infrastructure management. Scientific Awards: Republic Scholarship (1998-1999) Karlsruhe Chancellor's Scholarship (1999) ERASMUS scholarship (2000) Korányi Fellowship (2001-2002) János Bolyai Research Scholarship (2008-2011) OHV 1st place (2008) Dean's commendation for ERASMUS committee work (2014) For Students Award - Teaching Department (2017) Advising and Grants: Dr. Lovas mentors students through diploma theses and TDK research projects on topics including object survey with amateur sensors and hull modeling. His research funding includes the prestigious János Bolyai Research Scholarship and international fellowships supporting collaborations with institutions like The Ohio State University. Labs and Teams: As founding member and supervisory board member of the Hungarian BIM Association, he drives industry-academia collaboration. His leadership roles include Deputy Dean of Education at the Faculty of Civil Engineering and responsibility for English language training programs, facilitating international academic exchange.
Brian J. McPherson is a Professor in the Department of Civil & Environmental Engineering at the University of Utah. He specializes in geologic carbon storage, geomechanics, and subsurface fluid dynamics, with extensive research on CO₂ sequestration, enhanced oil recovery (EOR), and reservoir characterization. His work integrates experimental, numerical, and field studies to assess risks and optimize subsurface storage. McPherson's research focuses on: Geomechanical risk assessment for CO₂ storage Multiphase flow dynamics in porous media Machine learning applications in reservoir forecasting Impacts of CO₂ leakage on groundwater systems His publications emphasize carbon capture and storage (CCS) technologies, hydraulic fracturing, and subsurface monitoring methods. Recent articles frequently address uncertainty quantification and predictive modeling using Bayesian frameworks and AI. He leads major DOE-funded initiatives, including the San Juan Basin CarbonSAFE project and the Uinta Basin CarbonSAFE II feasibility study. These projects focus on ensuring safe long-term CO₂ storage in saline reservoirs. McPherson advises graduate students through thesis research courses and directs the SMART Machine Learning Initiative as an External Advisory Board member.
Tomonari Furukawa is a Professor and Zinn Faculty Scholar at the University of Virginia, leading the VICTOR Lab. He holds a B.Eng. in Mechanical Engineering from Waseda University (1990), an M.Eng. in Mechatronic Engineering from the University of Sydney (1993), and a Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo (1996). His research focuses on robotics, computational mechanics, autonomous systems, and advanced sensor technologies. He has published over 300 papers, contributed to editorial boards, and secured grants such as the U.S. DOD DURIP grants for advanced research infrastructure. His work spans topics like autonomous robotic mapping, sensor fusion, and real-time deformation measurement for automotive safety. He has developed systems for tire tread profiling, crash deformation analysis, and 3D road surface reconstruction. Furukawa’s methodologies often integrate Bayesian approaches, neural networks, and multi-sensor data fusion to solve complex engineering challenges. His contributions to the NSF I/UCRC Centre for Tire Research highlight his impact on applied mechanics. Recipient of multiple career and paper awards, Furukawa emphasizes translational research. His VICTOR Lab explores cutting-edge robotics, including compliant bipedal designs for disaster response (e.g., DARPA Robotics Challenge) and autonomous navigation systems. Current projects leverage AI and advanced vision systems for infrastructure monitoring and human-robot collaboration.
Gabriele Lobaccaro is a Professor at the Department of Civil and Environmental Engineering at NTNU. He specializes in sustainable urban planning, energy-efficient architecture, and solar energy integration in built environments. His research focuses on smart cities, solar potential assessment, and climate-resilient urban design. Education: MSc in Building Engineering and Architecture from Politecnico di Milano (2008), PhD in Building Engineering from Politecnico di Milano and UNSW Sydney (2013). Former roles include Postdoctoral Research Fellow (2013-2017) and Researcher at the Department of Architecture and Technology (2017-2020). Research highlights include participation in international projects like REINVENT, RAMSES, and EERA Joint Programme Smart Cities. He leads efforts in solar neighborhood planning, façade engineering, and digital twin technology for energy systems. Awards: Åsgård Research Program (2018/2020), ISSNAF/CNI Scholarship (2014), and COST Action TU0902 Scholarship (2014). Teaching: Courses in Building Physics for Architects, Climate and Built Forms, and Urban Studies. Active in NTNU’s Smart Sustainable Cities@NTNU initiative and IEA SHC Task 51/63.
Dr James Lawrence is a Professor in Geological Engineering at Imperial College London, within the Department of Civil and Environmental Engineering (Faculty of Engineering). His affiliations include the Earth Observation Network, Geotechnics Group, and the Grantham Institute. Previously, he held roles at the University of Brighton, University of Leeds, and Radioactive Waste Management Ltd (RWM). He leads research in geotechnics, Cretaceous geology, and geophysical methods, with a focus on InSAR technology for monitoring ground movements and radioactive waste disposal. Education: MSc in Mineral Resources from Cardiff University, followed by a PhD at the University of Brighton. Research interests include the geological evolution of chalk cliffs, infrastructure challenges posed by geological formations, and the safety of geological disposal facilities (GDF). Recent projects involve InSAR monitoring for rural UK deformation and nuclear decommissioning. He has authored over 30 journal articles and led 15+ research projects. Teaching includes Rock Engineering, Soils, and Geomorphology at Imperial College, with fieldwork in the UK and Greece. Management roles include coordinating the Civil Engineering (MEng) course. Professional memberships include the British Geotechnical Association (BGA), International Society for Rock Mechanics (ISRM), and editorial board of the Quarterly Journal of Engineering Geology and Hydrogeology.
Nicole Riemer is a Professor at the University of Illinois Urbana-Champaign, affiliated with the Department of Climate, Meteorology and Atmospheric Sciences, Civil and Environmental Engineering, and the National Center for Supercomputing Applications (NCSA). Her research focuses on aerosol particles' creation, transport, and transformation, with applications to climate, health, and pollution mitigation. She develops advanced simulations to study aerosol impacts on weather patterns, climate change, and human health. Education : Not explicitly stated in the provided texts. Affiliations : Multiple roles across climate, engineering, and supercomputing departments. Her work integrates observational data, satellite information, and computational models to address global challenges like air pollution and climate feedback loops. Key areas include aerosol mixing state, black carbon dynamics, and aerosol-cloud interactions. Dr. Riemer has received prestigious awards, including the Atmospheric Sciences Ascent Award (2021) and the NSF CAREER Award (2013). Her research outputs span aerosol chemistry, climate modeling, and computational methods. Notable datasets include work on particle-resolved modeling and machine learning applications in environmental science. She actively mentors graduate students and collaborates internationally on climate and atmospheric science projects.
Dr. Nidhal Jamia is a Lecturer in Aerospace Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a PhD in Mechanical Engineering and has experience as a Research Officer and Research Assistant at Swansea's Faculty of Science and Engineering (FSE). His expertise lies in Linear and Nonlinear Structural Dynamics, Turbomachinery Blade Vibrations, and Experimental Modal Analysis, with a focus on jointed structures and bolted interfaces. Dr. Jamia's research emphasizes predicting dynamic responses in complex systems, such as bolted joints and turbomachinery blades, with practical applications in vibration control and structural integrity. He has contributed to advancements in blade tip timing techniques, eddy current sensor modeling, and nonlinear system identification. His work bridges theoretical models with experimental validation, addressing challenges in aerospace and mechanical engineering. Selected research highlights include studies on mistuned bladed disks using wavelet transforms, sensor characteristics in blade tip timing, and the development of equivalent models for nonlinear joints. His recent efforts focus on stochastic updating of nonlinear systems, digital twin platforms, and the TRC benchmark system's experimental analysis. Education: PhD in Mechanical Engineering, Swansea University (2019–2022) MSc in Computational Mechanics, Polytechnical School of Tunisia (2015–2019) BEng in Civil Engineering, Engineering School of Gabes, Tunisia (2010–2014) Dr. Jamia is actively involved in teaching modules such as Engineering Mathematics, Experimental Studies for Mechanical and Aerospace Engineers, and Design and Laboratory Classes. He is available for postgraduate supervision and collaborates with industry on research projects involving structural dynamics and vibration analysis.
