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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.

Fei HanView profile

Assistant Professor

• Advanced Sensing Technologies
• Artificial Intelligence
• Computational Mechanics
• +5 more

Fei Han is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire, affiliated with the College of Engineering and Physical Sciences. His research focuses on advanced sensing technologies, artificial intelligence, computational mechanics, decision-making systems, deep learning, optimization, plasticity, and soil mechanics & foundations. He teaches courses including Soil Mechanics, Geo-Environmental Engineering, and Foundation Design. His work integrates machine learning with geotechnical engineering to address challenges in infrastructure resilience, sustainable development, and disaster mitigation. Education : Ph.D., Purdue University M.S.C.E., Purdue University B.S., Huazhong University of Science Key Research Trends : Recent publications emphasize data-driven modeling for infrastructure systems (e.g., transfer learning for monopile foundations, deep reinforcement learning for equitable bridge maintenance), optimization algorithms (multi-objective genetic algorithms), and marine geology advancements. His work bridges computational methods with practical geotechnical challenges, addressing scour management, soil-structure interaction, and sustainable engineering solutions. Grants & Advising : No specific grants or student advisees are listed, though his courses suggest involvement in mentoring senior theses (e.g., CEE 799H). His research collaborations involve institutions like INDOT and international co-authors. Labs & Teams : Active in UNH’s geotechnical and environmental engineering research groups, focusing on sensor fusion, deep learning applications, and sustainable infrastructure systems.

Rebecca DziedzicView profile

Assistant Professor

• Asset Management
• Water System Sustainability
• Infrastructure Resilience
• +5 more

Rebecca Dziedzic is an Assistant Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University. Her research focuses on asset management, water system sustainability, and infrastructure resilience. She holds a PhD in Civil and Environmental Engineering from the University of Toronto. Dr. Dziedzic's work integrates machine learning, data science, and policy analysis to address challenges in urban infrastructure systems. Her research explores topics such as water distribution network optimization, climate change adaptation in infrastructure, and circular economy strategies for construction. Recent projects include predicting water main breaks using multivariate models, developing frameworks for energy-efficient pump operation, and assessing carbon footprints in industrial facilities. Dr. Dziedzic supervises graduate students in Civil Engineering (MASc/PhD) and maintains an active research group through the UrbanLinks initiative. Her work has been published in over 30 peer-reviewed articles, with a strong focus on smart city technologies, disaster risk reduction, and sustainable infrastructure design.

• Offshore Geotechnical Engineering
• Soil-Structure Interaction
• Offshore Wind Energy
• +3 more

Professor Byron Byrne holds the Ørsted/Royal Academy of Engineering Research Chair in Advanced Geotechnical Design at the University of Oxford. He serves as Professor of Engineering Science (Civil Engineering), Fellow of St Catherine's College, and Co-Director of the EPSRC Supergen Offshore Renewable Energy Hub. His career spans academic leadership, industry collaboration, and innovative research in offshore geotechnical engineering. Education: BE(Hons) in Civil Engineering, University of Western Australia BCom in Commerce, University of Western Australia DPhil in Engineering, University of Oxford (Rhodes Scholar) Byron’s research focuses on soil-structure interaction for offshore foundations, particularly monopiles, suction caissons, and jacket piles supporting offshore wind turbines. His work combines experimental field/laboratory testing with theoretical modeling to develop practical design guidance. Key projects include PISA (industry-funded monopile research), ALPACA (chalk foundation studies), and collaborations with Ørsted and TechnipFMC. Current emphasis includes cyclic loading behavior, scour protection, and real-time monitoring applications. His scientific awards include the Crampton Prize (2023), BGA Medal (2022/2020), Fleming Award (2017), and multiple teaching accolades. His supervision spans 13 CDT students and 18+ graduate researchers, with completed projects on topics like pipeline buckling, soil liquefaction, and suction caisson installation. Byron also leads consultancy activities through Oxford University Consulting, working with industry leaders like Ørsted and Arup.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +9 more

Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.

Jim TørresenView profile

Professor

• Artificial Intelligence
• Robotics
• Biology-Inspired Computing
• +6 more

Jim Tørresen is a Professor of Computer Science at the Department of Informatics, University of Oslo, where he has been employed since 1999 (Associate Professor 1999-2005, Professor since 2006). He serves as group leader for the Robotics and Intelligent Systems (ROBIN) research group and is also a Principal Investigator at the Centre for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). His academic career includes visiting positions at Cornell University's Creative Machines Lab (2010-2011) and Kyoto University in Japan (1993-1994). His educational background includes a Dr.ing. (Ph.D.) in Computer Architecture from the Norwegian University of Science and Technology (1996) and an M.Sc. in Computer Architecture from the same institution (1991). Before his academic career, he worked in industry at Navia Aviation (1998-1999) and NERA Telecommunications (1996-1998). Tørresen's research spans artificial intelligence, robotics, and bio-inspired computing. His work focuses on biology-inspired algorithms, programmable logic (FPGA), robotics (simulation, prototyping, control), and human-robot interaction. He has made significant contributions to areas including evolutionary computing, reconfigurable hardware, and adaptive systems. His research often bridges theoretical computer science with practical applications in healthcare, music, and industrial settings. His recent publications demonstrate a strong focus on human-robot interaction, particularly in healthcare contexts for elderly care, as well as applications in sports science, musical robotics, and geological engineering. His work shows a consistent pattern of interdisciplinary research that combines machine learning techniques with domain-specific challenges. Tørresen has also authored a popular science book on artificial intelligence in the "what is" series by Universitetsforlaget, which discusses fundamental concepts, methods, future perspectives, and ethical aspects of AI. He has been active in academic leadership, serving as General Chair for the 22nd International Conference on Field Programmable Logic and Applications (FPL) in 2012 and the 9th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics in 2019. As group leader of ROBIN, he oversees research on intelligent systems that operate in dynamic environments requiring runtime adaptation. The group works at both fundamental and applied levels, using evolutionary algorithms for robot learning and machine learning techniques for classification and recognition tasks in various application domains.

• Hydraulic Engineering
• Coastal Morphology
• Flood Risk Management
• +3 more

Prof. Suzanne J.M.H. Hulscher is a Full Professor in Water Systems at the University of Twente, specializing in fluvial and coastal morphodynamics. Her research focuses on flood risk management, sediment dynamics, and climate adaptation. She has contributed to over 845 publications and supervised 55 research projects, including Vera van Bergeijk's PhD work on overtopping flows. Key achievements include the Simon Stevin Meester award (2016) and multiple best paper awards. Her work addresses UN Sustainable Development Goals through studies on saltwater intrusion, dike breach modeling, and nature-based solutions. Research highlights include hydraulic model calibration, estuarine sand wave dynamics, and vegetation effects on hydrodynamics. She actively contributes to editorial roles (CivilEng Journal) and policy advisory bodies (Wetenschappelijke Raad). Her interdisciplinary efforts bridge engineering, ecology, and climate science. Research areas: Coastal morphology, river dynamics, environmental hydraulics Key collaborations: 4TU.Centre for Research Data, IAHR, Netherlands Academy of Engineering Grants: Not explicitly listed, but extensive publications imply significant funding Her lab focuses on combining field data with numerical modeling for real-world applications like flood dashboard development and machine learning-based prediction systems. Current projects explore climate change impacts on engineered estuaries and distributive justice in climate policy.

• Rock Mechanics
• Civil Engineering
• Mining Geotechnics
• +4 more

Professor Anna Giacomini is a leading academic in Rock Mechanics and Civil Engineering at the University of Newcastle. She holds a PhD from the University of Parma, Italy, and has been at the University of Newcastle since 2005. Her roles include Director of the Priority Research Centre for Geotechnical Science and Engineering and Deputy President of the Academic Senate (Research). She specializes in rockfall hazard analysis, mine geotechnics, and numerical modeling of geomechanical systems. Her research focuses on improving safety in mining and civil environments, with over $7.5M in funding and 140+ publications. Key areas include rockfall trajectory analysis, energy absorption in safety barriers, and drapery systems. She has led 20 major projects through ACARP and pioneered low-cost photogrammetric monitoring systems for rock slopes. Professor Giacomini is also a co-founder of HunterWiSE, promoting women in STEM. She has received prestigious awards such as the 2022 NSW Premier’s Engineering Prize and the 2019 John Booker Medal. Her administrative roles include membership in the ARC College of Experts and leadership in gender equity initiatives. Her technical contributions span experimental and numerical rock mechanics, including advancements in discrete element modeling (DEM) and stochastic approaches for discontinuity shear strength prediction. She collaborates internationally with institutions like the Colorado School of Mines and the University of Bologna.

Laurie Gaskins BaiseView profile

Professor

• Geotechnical Earthquake Engineering
• Seismic Hazard Mapping
• Natural Hazards
• +3 more

Laurie Gaskins Baise is a Professor and Chair of the Department of Civil and Environmental Engineering at Tufts School of Engineering, Tufts University. She leads the Geohazards Research Lab and directs the BSCE degree program. Her expertise lies in geotechnical earthquake engineering, seismic hazard mapping, and natural hazards mitigation. Education: Ph.D., University of California, Berkeley (2000) M.S., University of California, Berkeley (2000 and 1997) B.S.E., Princeton University (1995) Research Interests: Dr. Baise focuses on integrating predictive models with observational data to address geohazards like earthquakes and extreme wind events. Her work emphasizes regional seismic hazard mapping, liquefaction risk assessment, and rapid damage detection via remote sensing. She develops machine learning frameworks for post-disaster damage mapping and geospatial models for site response analysis. Labs/Teams: She directs the Geohazards Lab at Tufts, which collaborates on projects involving satellite imagery, geostatistical methods, and deep learning for hazard response. Recent work includes liquefaction modeling in Türkiye and Ukraine, wildfire spread forecasting, and ensemble-based seismic hazard assessments. Grants/Advising: Her research is supported by national and international grants. She mentors students in developing predictive models and remote sensing techniques for disaster resilience. Notable collaborations include work with the U.S. Geological Survey and global seismic networks.

Minchen LiView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Robotics
• +4 more

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.

• Geotechnical Engineering
• Environmental Monitoring
• Fiber Optic Sensing
• +2 more

Dante Fratta is a Professor in the Department of Civil & Environmental Engineering at the University of Wisconsin-Madison, where he has been actively involved in research and teaching since 2000. His work focuses on geotechnical engineering, environmental monitoring, and fiber optic sensing technologies. Education: PhD (1999) – Georgia Institute of Technology M.A.Sc. (1995) – University of Waterloo Diploma (1993) – Universidad Nacional de Córdoba Research Interests: Geomaterial process evaluation using elastic and electromagnetic waves, fundamental physical behavior of soils and rocks, geophysical assessment of near-surface environments, and distributed fiber optic sensing methods. Recent Publication Trends: He has pioneered applications of Distributed Acoustic Sensing (DAS) and fiber optic technologies for geotechnical, environmental, and energy infrastructure monitoring, including wind turbines, geothermal systems, and mining operations. Scientific Awards: Benjamin Smith Reynolds Award for Excellence in Teaching (2012) Chi Epsilon Excellence in Teaching Award (2008) Best Paper Award, GeoCongress Sensing Methods and Devices Track (2006) Distinguished Alum, Universidad Nacional de Córdoba (2013) Multiple keynote and invited speaking engagements Teaching: Fratta teaches graduate and undergraduate courses in geotechnical engineering, including Foundations, Applied Geophysics, and Pre-Dissertator Research, with active involvement in Spring 2025 classes.

Yuting ZhangView profile

Research Fellow

• Probabilistic Geotechnics
• Reliability-Based Design
• Data-Driven Site Characterisation
• +3 more

Dr Yuting Zhang serves as a Research Fellow within the Department of Civil, Maritime, and Environmental Engineering at the University of Southampton's Faculty of Engineering and Physical Sciences. He is an integral member of the Royal Academy of Engineering Chair Centre of Excellence for Intelligent & Resilient Ocean Engineering (IROE), focusing on machine learning applications for geotechnical site characterization. His educational background includes a Bachelor's degree and MPhil in Geotechnical Engineering from Wuhan University, China, followed by a 2024 PhD from the University of Newcastle, Australia, specializing in probabilistic calibration of resistance factors for piling designs. Zhang's research centers on probabilistic geotechnics and reliability-based design methodologies, with particular emphasis on data-driven site characterization techniques. His work bridges machine learning algorithms with geotechnical and geophysical data analysis to enhance foundation engineering practices, especially in offshore and marine environments. Current projects investigate spatial soil variability effects on pile group reliability, optimization of resistance factors, and innovative data augmentation approaches for rock fracture prediction. His publication record demonstrates consistent output in high-impact journals since 2022, with recent 2025 publications indicating active research momentum. The articles reveal strong thematic focus on probabilistic methods for pile design, integration of diverse geotechnical data sources, and machine learning applications in subsurface characterization. As part of the Infrastructure Group and Southampton Marine and Maritime Institute, Zhang contributes to ocean energy research initiatives while maintaining active collaborations with international researchers including Jinsong Huang, Jiawei Xie, and Anna Giacomini. His work supports the development of more resilient offshore infrastructure through advanced geotechnical reliability frameworks.

Iason PapaioannouView profile

Adjunct Professor

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +5 more

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

• Geotechnics
• Numerical Simulations
• Bayesian Inference
• +4 more

Stéphane Commend is an Associate HES Professor at the Fribourg School of Engineering and Architecture (HEIA-FR) under HES-SO Valais-Wallis. He also holds a lecturer role at the School of Engineering and Management of the Canton of Vaud. His primary research focuses on geotechnics, numerical simulations, and probabilistic modeling applied to infrastructure projects like tunneling and deep excavations. Education and affiliations include roles across multiple HES-SO institutions, with a strong emphasis on integrating advanced computational methods into geotechnical engineering. Notable projects include the Grand Paris Express tunnel project, Bayesian inference for wood constitutive modeling, and probabilistic risk analysis for urban construction. Research interests span soil-structure interaction, finite element modeling, and uncertainty quantification. Recent work emphasizes Bayesian methods for parameter calibration, machine learning in excavation design, and natural hazard vulnerability assessment. Key contributions include frameworks linking ZSOIL and UQLab for reliability analysis, and prototypes like SLIDE-PM for mudflow impact modeling. Current projects (e.g., iBAG and OptiSoil) focus on optimizing construction methods using AI and data-driven approaches. He leads collaborative teams across HES-SO institutes and academic partners like EPFL and CETU. Key Projects: iBAG Project (2022–2025): Bayesian methods in geotechnics OptiSoil (2019–2025): Machine learning for excavation design TULIP Project: TBM-pile interaction probabilistic analysis

• Applied Geophysics
• Glaciology
• Geophysical Data Integration
• +3 more

Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.