Machine learning-based tools for structural and earthquake engineering Professors and Supervisors | ApplySharif

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Yangkang ChenView profile

Academic

University of Texas at AustinUnited States

Yangkang Chen is a Research Assistant Professor at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. He leads AI/ML research at the Texas Seismological Network (TexNet). His expertise includes seismic signal analysis, deep learning, and high-performance computing. Chen holds a Ph.D. in geophysics from UT Austin (2015) and was a postdoc at Oak Ridge National Laboratory (2016-2018). Research Focus: AI-driven earthquake monitoring, seismic modeling, and reproducible research. Key Contributions: Developed EQCCT, TXED datasets, and open-source tools like pyseistr and DRR. Leadership: Editor of JGR: Machine Learning and Computation, and AGU fellowships. Education: Ph.D. in Geophysics, UT Austin, 2015 Postdoctoral Research at Oak Ridge National Lab (2016-2018) Research Interests span AI applications in seismology, seismic inversion, and reproducible methodologies. His work bridges computational methods with real-world seismic challenges, such as induced seismicity detection and earthquake cataloging. Awards include First Prize in GeoHackathon 2024, Clarivate Highly Cited Researcher (2020), and multiple IEEE recognitions. He advises over 20 graduate students/postdocs, focusing on ML-driven geoscience problems. Chen's lab develops tools for seismic data analysis, DAS processing, and machine learning frameworks.

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Michail PapoutsidakisView profile

Professor

University of West AtticaGreece

Michail Papoutsidakis is a Professor and Deputy Head of the Department of Industrial Design and Production Engineering at the School of Engineering, University of West Attica. He also directs the Postgraduate Program in New Technologies in Shipping and Transport (co-founded with the University of Aegean) and the 'Unmanned Autonomous and Remote-Control Systems' program. His academic background includes a PhD from the Bristol Robotics Laboratory (UK) focusing on control systems and robotics, along with a postdoctoral research fellowship at the University of Thessaly. Education: BSc in Automation Engineering (2000, Technological Institute of Piraeus), MPhil in Control Systems (2004, University of the West of England), and PhD in Robotics and Artificial Intelligence (2004, BRL). Research interests span robotics, mechatronics, Industry 4.0, additive manufacturing, and smart infrastructure. He leads the Research Lab 'Industrial Systems and Mechatronics Applications' and has authored over 150 publications. Notable contributions include a patented 'Self-propelled polymorphic test base' for robotics education and pioneering work in predictive maintenance using machine learning. His academic contributions extend to educational innovation, including open-source robotics platforms (DuBot) and STEM-focused curricula emphasizing sustainability. He has directed numerous projects funded by national and international bodies, with a focus on bridging theoretical research and practical industrial applications. Professional roles include leadership in postgraduate program development, research lab management, and industry-academia collaboration through applied research initiatives in shipping, logistics, and smart manufacturing systems.

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Himmet KaramanView profile

Professor

Istanbul Technical UniversityTurkey

Prof. Himmet Karaman is a Professor at Istanbul Technical University's Department of Geomatics Engineering, part of the Faculty of Civil Engineering. His research focuses on GIS applications, earthquake damage estimation, urban resilience, and disaster management. He holds a PhD in Geomatics Engineering from Istanbul Technical University (2008). Educational Background: PhD in Geomatics Engineering, Istanbul Technical University (2003–2008) MSc in Geomatics Engineering, Istanbul Technical University (2001–2003) BSc in Geodesy and Photogrammetry Engineering, Istanbul Technical University (1997–2001) Research Interests: Prof. Karaman’s work integrates geospatial technologies with disaster risk reduction, including earthquake vulnerability analysis, decision support systems, and urban immunity strategies. He has developed tools like HAZTURK for seismic risk assessment and contributed to projects like Antarctica GNSS monitoring. His studies often combine GIS, machine learning, and multi-criteria decision analysis. Awards: Most Successful Thesis Award, Istanbul Technical University (2021) Grants & Projects: He led projects such as the ‘Turkey National Geographic Information System Infrastructure Project’ and ‘Risk Assessment for Base Station Towers in Istanbul.’ His work includes collaborations on nuclear accident vulnerability frameworks and cellular network path loss prediction using GeoAI. Professional Roles: Member of AFAD Advisory Board, The Resilience Advisors Network, and International Association for Bridge and Structural Engineering. He has served as Department Vice President and Board Member at Istanbul Technical University.

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Gian Paolo CimellaroView profile

Professor

Polytechnic University of TurinItaly

Gian Paolo Cimellaro is a Full Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, Italy. He is a leading researcher in disaster resilience, structural health monitoring, and seismic engineering, with extensive contributions to infrastructure interdependencies and urban resilience. He is affiliated with the SISCON Interdepartmental Center for Safety of Infrastructures and Constructions and leads the Disaster Resilience Simulation Laboratory. Research Interests: Disaster and Seismic Resilience Structural Health Monitoring (SHM) Earthquake Engineering and Structural Control Infrastructure and Network Interdependencies Smart Cities and Resilience Technologies Machine Learning Applications in Civil Engineering His recent publications focus on probabilistic resilience frameworks, Bayesian network modeling, infrastructure interdependencies, and post-disaster recovery, with applications to hospitals, transportation networks, and urban water systems. He has pioneered tools like the PEOPLES resilience framework and software such as ParkAdvisor and OpenSignal for urban resilience and damage assessment. Scientific Awards: Seed Grant Award, Siebel Energy Institute (2015) for work on power network vulnerability He has supervised numerous PhD students and secured significant research funding from EU programs including ERC (IDEal reSCUE, IDEAL SENSOR, IDEAL DRONE) and MSCA projects (FOURIER, ReCharged). His research integrates experimental work, computational modeling, and real-world applications to improve the safety and sustainability of civil infrastructure. Laboratories and Research Groups: Disaster Resilience Simulation Laboratory (DISEG) Research on structural control, SHM, and smart sensor networks Development of software platforms: EDAM, OpenSignal, ParkAdvisor

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Masoud AkbarzadehView profile

Associate Professor

University of PennsylvaniaUnited States

Masoud Akbarzadeh is an Associate Professor of Architecture at the University of Pennsylvania's Stuart Weitzman School of Design, directing the Polyhedral Structures Laboratory (PSL) where he pioneers geometric approaches to structural design and digital fabrication. His work integrates architectural innovation with engineering precision to develop sustainable, high-performance building systems. His academic credentials include: D.Sc. from ETH Zurich's Institute of Technology in Architecture Master of Science in Architecture Studies (Computation) and M.Arch from MIT Degree in Earthquake Engineering from Iran University of Science and Technology B.S. in Civil and Environmental Engineering Dr. Akbarzadeh's research centers on Three-Dimensional Graphical Statics , revolutionizing structural form finding through polyhedral reciprocal diagrams. Key focus areas include computational geometry for spatial structures, digital fabrication of concrete and glass systems, and geometric optimization for environmental performance. His laboratory develops cutting-edge methods for translating theoretical geometry into buildable structures through advanced computational tools. Recent publications reveal accelerating innovation in carbon-capturing concrete using triply periodic minimum surfaces, ultra-thin glass bridges, and machine learning-enhanced structural design. His work consistently bridges theoretical geometric principles with practical applications in sustainable construction and renewable energy infrastructure, demonstrating increasing interdisciplinary impact. Major recognitions include: National Science Foundation CAREER Award (2020) Top 100 Innovators & Entrepreneurs Magazine (2025) SOM award for MIT thesis As principal investigator, he leads significant research initiatives including the NSF CAREER project on 3D graphic statics and collaborative EAGER grants for offshore renewable energy structures. Though specific students aren't listed, he mentors through PSL and teaches graduate courses like Structures II and Technology Lab, fostering next-generation designers in computational structural engineering. CAREER: 3D/Polyhedral Graphic Statics for Education, Design, and Optimization (2020) Collaborative EAGER: Harnessing Ocean Wave Energy with 3D Printed Structures (2024) The Polyhedral Structures Laboratory drives experimental research in geometric structural systems, producing breakthroughs like the Tortuca glass bridge and Terrene 2.0 biomaterial composites. Through partnerships with industry and academic institutions, PSL advances the frontiers of structural design, fabrication technology, and sustainable material applications in architecture.

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Giovanni FABBROCINOView profile

Professor

University of MoliseItaly

Prof. Giovanni FABBROCINO is a Full Professor at the Department of Biosciences and Territory of the University of Molise . His research focuses on structural engineering, heritage conservation, and structural health monitoring (SHM). He specializes in seismic analysis of historical buildings, vibration-based diagnostics, and the integration of digital technologies like BIM and virtual reality for infrastructure assessment. His work bridges civil engineering with cultural heritage preservation, emphasizing interdisciplinary methodologies. Key research areas include: Seismic retrofitting of historical structures Non-destructive testing of masonry and RC structures Operational modal analysis (OMA) for dynamic characterization Remote sensing applications in structural monitoring Digital twin development for heritage conservation He leads projects involving advanced materials testing (e.g., weathering steel analysis) and crowd-based technologies for cultural heritage documentation. His methodologies often combine experimental measurements with computational models to enhance infrastructure safety in seismic-prone regions. Current research trends include AI-driven permission models for Android systems and probabilistic frameworks for healthcare facility safety networks. Prof. FABBROCINO collaborates with institutions like the StreGa Laboratory , advancing multidisciplinary approaches to sustainable urban regeneration and disaster resilience. His work contributes to both academic discourse and practical solutions for safeguarding historical centers and strategic infrastructure.

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Shah Rukh HumayounView profile

Assistant Professor

San Francisco State UniversityUnited States

Shah Rukh Humayoun is an Assistant Professor in the Department of Computer Science at San Francisco State University, part of the College of Science and Engineering. His research focuses on visual analytics, software engineering, human-computer interaction, and mobile applications. He specializes in developing interactive visualization tools for data exploration, system analysis, and engineering education. Notable projects include MECpace for embedding space comparison, WebGraphViz for retail analytics, and VREE for earthquake damage training in VR. His work integrates cross-device interaction, virtual reality applications, and user-centered design principles. Recent projects address challenges in multi-classification models, emotion analysis in social media, and disaster preparedness through immersive simulations. He has contributed to over 50 publications since 2013, spanning topics from gesture authentication to embedded system visualization. Dr. Humayoun's research also explores usability heuristics for mobile apps, agile project management, and IoT scenario formalization. His interactive tools like Layman and ESSAVis++ enhance failure analysis in embedded systems. Despite extensive contributions, no scientific awards are explicitly mentioned in available records. He advises no listed students but collaborates on grants related to visualization systems and emergency response technologies. His lab focuses on bridging software engineering practices with human-centered design innovations.

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Ha BuiView profile

Professor

Monash UniversityAustralia

Professor Ha Bui is the Head of the Department of Civil and Environmental Engineering at Monash University and an ARC Future Fellow. He holds a PhD in Computational Geomechanics from Ritsumeikan University, Japan, and has been with Monash since 2012. His research focuses on computational mechanics, material modeling, and geomechanics, with a particular emphasis on failure mechanisms of geomaterials and developing predictive tools for geo-disasters. He leads the development of GeoXPM, a particle-based computational software for geotechnical applications. Bui has received numerous awards, including the ALERT Research Medal (2021) and the ARC Future Fellowship (2020). He serves as Editor of Computers & Geotechnics and Associate Editor of multiple journals, contributing significantly to the field through editorial and review work. His research spans numerical methods like SPH and DEM, addressing challenges in geohazard prediction, material behavior under extreme conditions, and infrastructure resilience.

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Sinan AcikgozView profile

Associate Professor

University of OxfordUnited Kingdom

Sinan Acikgoz is an Associate Professor of Engineering Science at the University of Oxford, affiliated with Mansfield College. His work focuses on structural and geotechnical engineering, integrating computational modeling with experimental techniques. PhD in Engineering from Trinity College, University of Cambridge (2014) 1851 Brunel Research Fellow at Clare Hall College (2014–2020) Postdoctoral Research Associate at Cambridge's Centre for Smart Infrastructure and Construction Research Interests: Masonry structures , Structural health monitoring , Soil-structure interaction , and Rocking structures . His work addresses challenges in underground construction , earthquake resilience , and vision-based sensing to minimize structural damage risks. Recent publications emphasize 1D-CNNs for damage detection, Virtual Fields Method for material characterization, and dynamic modeling of rocking systems. These studies often involve interdisciplinary collaboration and advanced computational tools. Scientific Awards: 1851 Brunel Research Fellowship He actively seeks graduate students for interdisciplinary structural engineering research, particularly those with computational and experimental skills.

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James CrutchfieldView profile

Professor

University of California, DavisUnited States

Dr. James Crutchfield is a Distinguished Professor of Physics at the University of California, Davis. His research focuses on nonlinear dynamics, condensed matter physics, and the physics of computation. He explores interdisciplinary topics such as evolutionary dynamics, pattern discovery, and distributed robotics, often integrating principles from statistical mechanics and information theory. His work frequently addresses thermodynamic computing, quantum systems, and the interplay between structure and randomness in complex systems. Recent publications highlight his contributions to stochastic thermodynamics, quantum information processing, and the theoretical foundations of predictive systems. He has pioneered methodologies for analyzing trajectory fluctuations, entropy production, and the efficiency of information processing in both classical and quantum contexts. Dr. Crutchfield’s research bridges theoretical frameworks with practical applications, such as earthquake prediction via machine learning and the design of energy-efficient superconducting circuits. His lab’s work often emphasizes emergent organization in complex systems, leveraging tools from nonlinear dynamics and computational physics. While no specific awards are listed, his prolific publication record underscores his influence in these fields. Collaborations span physics, computer science, and engineering, reflecting his commitment to interdisciplinary innovation.

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Gerardo GrelleView profile

Researcher

Sapienza University of RomeItaly

Gerardo Grelle is a Type B Researcher at the Department of Civil, Building and Environmental Engineering, Sapienza University of Rome. His research focuses on seismic response modeling, landslide dynamics, geophysical surveys, and GIS applications in geohazards. He has developed software tools like SiSeRHMap and contributed to patents related to landslide monitoring and seismic hazard assessment. Collaborations include institutions such as the University of Bristol and Chengdu University of Technology. His work integrates computational methods, physical modeling, and field data to address challenges in earthquake engineering, hydrogeology, and environmental geotechnics. Research interests include viscoplastic behavior of soils, seismic amplification, karst spring characterization, and the development of hybrid models for hazard prediction. He has authored/co-authored over 50 publications, with recent contributions on machine learning in landslide hazard mapping and topographic effects during seismic events. His patents include systems for slope monitoring and seismic response mapping.

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Kaveri ThakoorView profile

Assistant Professor

Columbia UniversityUnited States

Kaveri Thakoor, Ph.D., is an Assistant Professor of Ophthalmic Science (in Ophthalmology) at Columbia University Irving Medical Center. She directs the Artificial Intelligence for Vision Science (AI4VS) Laboratory, specializing in developing robust and interpretable deep learning systems for ophthalmology applications. Her research addresses key challenges in AI translation to clinical practice, including model generalization across imaging modalities and institutions. Education BS, Chemistry (Honors) – Stanford University (2010) MS, Computer Science – University of Southern California (2015) PhD, Biomedical Engineering – Columbia University (2022) Research Focus Dr. Thakoor's research integrates AI with ophthalmology to enhance disease diagnosis. Key areas include: glaucoma detection using OCT scans, multimodal analysis of age-related macular degeneration, generative adversarial networks for portable OCT image enhancement, and eye-tracking-informed AI systems. Her lab aims to develop clinically deployable tools that improve accessibility and diagnostic accuracy across diverse populations. Publication Trends Dr. Thakoor's recent publications demonstrate a strong focus on deep learning applications in medical imaging, particularly glaucoma and AMD detection. Her work evolves from foundational computer vision research to clinically oriented AI systems, emphasizing multimodal data fusion, model interpretability, and real-world robustness. Earlier publications reflect interdisciplinary contributions to seismology and assistive technologies. Awards and Honors Biomedical Engineering Young Scientist Award, NEBEC (2022) Morton B. Friedman Memorial Prize for Excellence (2022) Three Minute Thesis Finalist (2022) NIH Director’s Early Independence Award Candidate (2021) NSF Graduate Research Fellowship (2018) 2nd Place Best Student Paper Award (2015) Best Student Paper Award (2013) National Merit Commended Scholar (2006) Girl Scout Gold Award (2005) Laboratory The AI4VS Laboratory develops AI systems as clinical collaborators for ophthalmologists. Current projects include constraint-based AI using expert eye-tracking patterns, portable OCT integration, and cross-institutional validation of diagnostic algorithms.

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Iris TienView profile

Associate Professor

Georgia Institute of TechnologyUnited States

Iris Tien is an Associate Professor at the Georgia Institute of Technology in the School of Civil and Environmental Engineering , part of the College of Engineering. She joined Georgia Tech in 2014 after earning her Ph.D. in Civil Systems Engineering from UC Berkeley. M.S. in Civil and Environmental Engineering (2010) B.S. in Civil and Environmental Engineering with Minor in English (2008) Her research focuses on probabilistic methods for infrastructure reliability , integrating data analytics, machine learning, and risk analysis to improve civil systems under extreme events. She develops frameworks for seismic vulnerability assessment and community resilience through projects funded by NSF, DHS, and NIST. Key publication trends include: Applications of Bayesian networks and convolutional neural networks in infrastructure risk modeling Multi-hazard resilience strategies (earthquakes, floods, corrosion) Interdisciplinary work bridging engineering with environmental health and urban equity Integration of 5G technology impacts into infrastructure risk analysis Scientific recognition: Received the IASSAR Early Achievement Research Prize Twice awarded 1st Place Paper Awards in resilient infrastructure Editor’s Choice selections in leading ASCE journals Selected for National Academy of Engineering Frontiers of Engineering Symposia Her work addresses smart cities and interdependent systems through grants from the National Science Foundation , Department of Homeland Security , and U.S. Department of Transportation . She has developed tools for coastal flood sensor networks and corrosion detection in bridges .

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Sam DillavouView profile

Visiting Professor

University of PennsylvaniaUnited States

Sam Dillavou is a Visiting Professor in the Department of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences. His research focuses on interdisciplinary applications of machine learning to physical systems, granular materials dynamics, and nonlinear physics-driven learning architectures. He explores emergent behaviors in analog circuits and stochastic processes in granular flow clogging, bridging computational methods with experimental physics. His work spans theoretical and applied domains, including energy-efficient 'smart instruments' and seismic stress analysis. Key areas of investigation include self-training circuits, interpretable machine learning for material science, and the statistical mechanics of granular systems. Dillavou's recent studies emphasize physical learning networks that operate without traditional processors, leveraging analog systems for power-efficient solutions. Notable contributions include analyzing clog formation in granular hoppers using ML techniques, developing Bellybutton—a customizable image segmentation tool—and studying fault mechanics through seismological stress drop analysis. His research integrates computational modeling with experimental validation, addressing both fundamental physics questions and real-world engineering challenges. Awards: None explicitly listed. Advising & Grants: No student advisees listed; active in collaborative projects within the Physics department's experimental and computational research groups. Involved in lab-based studies of granular dynamics and analog computing systems. Labs/Teams: Affiliated with the Department of Physics and Astronomy's labs focused on nonlinear systems, statistical mechanics, and interdisciplinary computational physics.

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Luigi LombardoView profile

Associate Professor

University of TwenteNetherlands

Luigi Lombardo is an Associate Professor in the Department of Applied Earth Sciences and affiliated with the Digital Society Institute. His research focuses on geohazards, particularly landslides, wildfires, and cascading disasters. He applies advanced geospatial analysis, machine learning, and remote sensing techniques to model environmental risks and improve disaster management. Key research interests include landslide susceptibility modeling, earthquake-induced hazards, climate change impacts, and spatial-temporal data science. He develops innovative tools like the SZ-plugin for QGIS, enhancing geohazard analysis accessibility. Recent work addresses post-seismic recovery dynamics, Arctic thaw slumps, and probabilistic landslide assessments across global regions (e.g., Colombia, Turkey, Italy). His studies emphasize interdisciplinary approaches to bridge geoscience and societal needs, contributing to UN Sustainable Development Goals related to climate action and disaster resilience. Publications highlight methodological advancements such as physics-informed neural networks, functional regression for landslide prediction, and ensemble modeling for multi-hazard scenarios. He collaborates internationally to build open datasets like the Arctic Retrogressive Thaw Slump (ARTS) inventory. Research trends show a focus on integrating AI with geophysical principles, improving early warning systems, and quantifying cascading risks from coupled natural-human systems. His work aims to inform policy decisions and enhance community resilience through actionable scientific insights.

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