جستجوی استاد راهنمای دانشگاهی

Pengfei Wang is an Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). He holds a Ph.D. in Geotechnical Engineering and an M.S. in Statistics from UCLA, alongside a B.S. in Transportation Engineering from Tongji University. Prior to ODU, he conducted postdoctoral research at UCLA. His expertise focuses on Geotechnical Engineering , Engineering Seismology , and Applied Statistics , with emphasis on regional geo-hazard modeling, multi-hazards risk assessment, and statistical learning applications. Key research interests include seismic site response analysis, liquefaction susceptibility, and probabilistic risk frameworks for infrastructure resilience. Dr. Wang’s work integrates geospatial analysis and statistical methodologies to address challenges in earthquake engineering. He has developed frameworks for regional landslide and liquefaction risk assessments, particularly in vulnerable regions like California’s Sacramento-San Joaquin Delta. His contributions include advancing HVSR (Horizontal-to-Vertical Spectral Ratio) methodologies and ergodic site response modeling. He maintains active collaborations with institutions globally and contributes to open-source databases for seismic data, promoting transparency and reproducibility in geotechnical research. His educational background in transportation engineering enriches interdisciplinary approaches to civil infrastructure resilience.

• Geotechnical Engineering
• Engineering Seismology
• Applied Statistics
• +۴ مورد دیگر

Babak Moaveni is a Professor in the Department of Civil and Environmental Engineering at Tufts University, serving as the Associate Chair since September 2024. He also holds a joint appointment as a Professor in Electrical and Computer Engineering. His research focuses on structural health monitoring, Bayesian inference, earthquake engineering, and offshore wind energy systems. Moaveni earned his Ph.D. in Structural Engineering from the University of California San Diego (2007), following an M.S. (2001) and B.S. (1999) from Sharif University of Technology in Tehran, Iran. His research interests span probabilistic system identification, signal processing, uncertainty quantification, and verification/validation of computational models. Notable grants include leadership in the PIRE project on offshore wind energy digital twins and the Coastal Virginia Offshore Wind Pilot Project. He has supervised multiple Ph.D. and M.S. students, with current advisees including Mehdi Akhlaghi and Nasim Partovi-Mehr. Moaveni has received the Best Presentation Award at the 2022 EDGE Symposium and serves on editorial boards for journals like Structural Health Monitoring and Frontiers in Built Environment . His lab, the Structural Health Monitoring Lab, specializes in infrastructure management and offshore wind energy systems. Key professional activities include membership in the American Society of Civil Engineers (ASCE) and roles on Tufts' Tenure and Promotion Committee. His teaching includes courses on structural health monitoring, numerical methods, and structural reliability.

• Structural Health Monitoring
• Bayesian Inference
• Earthquake Engineering
• +۷ مورد دیگر

Associate Professor Chengguo Zhang is a researcher at the University of New South Wales (UNSW Sydney) specializing in Mining Engineering and Geomechanics . His work focuses on improving mining safety and sustainability through fundamental and applied research on dynamic rock mass failures , groundwater-mining interactions , and data-driven visualization technologies . He currently serves as the Postgraduate Research Coordinator for the School of Mining Engineering. PhD in Mining Engineering from UNSW Sydney (2015) Coordinates postgraduate research programs Recipient of multiple teaching and research awards Research Interests: Zhang's work addresses critical mining industry challenges through: Quantification of energy sources and dissipation in rock masses for rockburst management Integration of AI data analytics and 3D visualization for geotechnical risk assessment Mine subsidence and coupled hydro-mechanical behavior of rock discontinuities Development of digital ground control management systems Article Trends: His recent publications demonstrate expertise in: Numerical modeling of rock fracturing mechanisms Nonlinear fluid flow analysis in fractured rock masses Shotcrete and ground support system evaluation Hydro-mechanical coupling during shear processes Energy-based coal burst risk classification Scientific Awards: Tim Shaw Award for Innovation in Teaching (2024) International Outstanding Young Scholar Award (2023) UNSW Education Excellence Award (2021) UNSW Research Excellence Award (2018) Research Supervision: Supervises 12 active PhD students (9 as primary/joint supervisor) and has guided 11 PhD completions (7 as primary/joint supervisor), including 3 Dean's Award recipients. Focuses on numerical modeling, data visualization, and machine learning applications in mining geomechanics.

• Mining Engineering
• Geomechanics
• Numerical Analysis
• +۶ مورد دیگر

Dr Donya Hajializadeh is an Associate Professor of Structural Engineering at the University of Surrey's School of Sustainability, Civil and Environmental Engineering. She holds multiple professional qualifications including Chartered Engineer (CEng) and European Engineer (EUR ING), and is a Fellow of the Higher Education Academy (FHEA). Her roles include Director of Employability (since 2020), Deputy Coordinator of the Surrey/ICE Scholarship (since 2019), and IStructE Liaison Officer (since 2021). She is also affiliated with the Surrey Institute for People-Centred Artificial Intelligence (PAI). Her education includes a BEng (Hons), MEng, and PhD in relevant fields. Research focuses on structural health monitoring (SHM), machine learning applications in asset management, and deep learning for damage identification. Key areas include railway bridge dynamics, vibration analysis, and resilience assessment under seismic and environmental hazards. Current PhD students include Chia Sadik (Transport Infrastructure Failure Assessment) and Michael Millgate (Dynamic Characterisation of Tall RC Buildings). Teaching responsibilities include ENG1073 Fluid Mechanics and ENGM054 Earthquake Engineering. Research aligns with sustainable development goals, emphasizing infrastructure sustainability and carbon reduction strategies. Notable projects include rail bridge innovation recognized by the Chief Scientific Adviser Award and presentations on damage identification techniques to government officials. She contributes actively to interdisciplinary initiatives, integrating AI with civil engineering for smarter infrastructure solutions.

• Structural Health Monitoring
• Machine Learning
• Asset Management Systems
• +۴ مورد دیگر

Katrina M. Groth is a Professor and Director of the Reliability Engineering Program at the University of Maryland, affiliated with the A. James Clark School of Engineering. She also serves as Associate Director for Research at the Center for Risk and Reliability and is part of the Maryland Energy Innovation Institute. Her expertise spans risk analysis, hydrogen safety, and nuclear safety, with notable contributions to probabilistic risk assessment (PRA) and human reliability analysis (HRA). Groth holds a Ph.D., M.S., and B.S. in Reliability Engineering from the University of Maryland (2009, 2008, 2004). Her research focuses on advancing safety practices for energy systems, including hydrogen technologies, nuclear power plants, and pipelines. Key innovations include the HyRAM toolkit for hydrogen risk assessment and the HyCReD database for reliability data. Groth has published over 175 papers and secured funding from DOE, NRC, and industry partners. She advocates for educational equity, mentoring women and first-generation engineering students. Award highlights include the NSF CAREER Award, USM Board of Regents Faculty Award, and ASME Rising Star of Mechanical Engineering. She leads initiatives such as the SyRRA Lab and serves on editorial boards for journals like Reliability Engineering & System Safety . Groth teaches graduate and undergraduate courses on reliability engineering and risk analysis, emphasizing practical applications in infrastructure and energy systems. Education: Ph.D., Reliability Engineering, University of Maryland, 2009 M.S., Reliability Engineering, University of Maryland, 2008 B.S., Engineering, University of Maryland, 2004 Professional Service: Board of Trustees, National Museum of Nuclear Science & History Associate Editor, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Her work integrates Bayesian methods, deep learning, and causal reasoning to address complex system safety challenges, with global impact on engineering standards and practices.

• Risk Analysis
• Quantitative Risk Assessment
• Hydrogen Safety
• +۷ مورد دیگر

Ronald D. Haynes is a Full Professor and Chair of Scientific Computing Graduate Programs in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He leads research in numerical methods for PDEs and industrial-scale optimization problems. His work develops advanced domain decomposition techniques, adaptive mesh methods, and parallel computing approaches for solving complex physical systems. Applications include modeling pitting corrosion of materials, predicting rock strength for drilling optimization, and simulating multiphase fluid flows in porous media. Recent publications demonstrate innovations in mesh adaptation, parallel algorithms, and machine learning applications for industrial problems. Collaborative projects have addressed reservoir simulation, drill bit analysis, and corrosion prediction through integrated computational approaches. Professor Haynes has received the President's Award for Outstanding Research (2018) and Dean of Science Distinguished Teaching Award (2017). He serves as Co-editor-in-chief of the CAIMS Mathematics in Science and Industry Journal and was President-Elect of the Canadian Applied and Industrial Mathematics Society (2023-2025). He maintains active doctoral supervision with current research groups focusing on domain decomposition methods, closest point algorithms, and optimization techniques. Industry partnerships include projects with ExxonMobil and Global Maritime addressing drilling optimization and mooring design challenges.

• Numerical Analysis
• Partial Differential Equations
• Optimization Algorithms
• +۲ مورد دیگر

Domniki Asimaki is a Professor of Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. Her research focuses on geotechnical engineering, computational mechanics, and structural dynamics, with an emphasis on understanding ground motion effects on natural and engineered systems such as dams, tunnels, and urban infrastructure. She holds a Dipl. from the National Technical University of Athens (1998), an M.S. (2000) and Ph.D. (2004) from MIT, joining Caltech in 2014. Key research interests include soil dynamics, wave propagation, regional ground deformation, and soil-foundation-structure interaction. She has pioneered data-driven approaches to integrate numerical simulations with field observations for resilient infrastructure design. Notable achievements include developing the open-source Seismo-VLAB software for seismic analysis and receiving prestigious awards like the Bodossaki Award of Scientific Excellence and the Geotechnical Earthquake Engineering Award. Her work addresses seismic hazards at urban and regional scales, with recent studies on the 2023 Türkiye earthquake, the 2019 Ridgecrest earthquake, and Kathmandu Basin dynamics. She leads initiatives to enhance ground motion prediction, landslide hazard assessment, and infrastructure resilience through advanced modeling and AI-driven methods. Education: Dipl., National Technical University of Athens, 1998 M.S., Massachusetts Institute of Technology, 2000 Ph.D., Massachusetts Institute of Technology, 2004 Awards: Bodossaki Award of Scientific Excellence Geotechnical Earthquake Engineering Award Labs/Teams: Leads research groups focusing on seismic hazard modeling, open-source software development, and geotechnical data assimilation techniques.

• Geotechnical Engineering
• Seismology
• Structural Dynamics
• +۶ مورد دیگر

Ethan Williams is an Assistant Professor at the University of California, Santa Cruz, Department of Earth & Planetary Sciences. His research focuses on distributed acoustic sensing (DAS) applications in seismology, oceanography, and environmental monitoring, particularly studying wave dynamics, subduction geohazards, and ocean-solid Earth interactions. Ph.D. in Geophysics, California Institute of Technology (2023) M.S. in Geophysics, California Institute of Technology (2019) B.S. in Geophysics and B.A. in Music, Stanford University (2017) Research Interests: Williams specializes in DAS technology, subduction zone geohazards, surface wave dynamics, and ocean-solid Earth coupling. His work bridges seismic monitoring, marine geophysics, and environmental sensing. Publication Trends: His recent articles emphasize DAS innovation for offshore seismic monitoring, wave propagation analysis, and integrating fiber-optic data with conventional measurements. Themes include earthquake detection, climate change impacts, and multi-scale ocean dynamics. Scientific Awards: 2022 SSA Student Presentation Award for 'Continuous seismic monitoring of a building over 20 years' Advising & Collaboration: Williams collaborates with institutions like the University of Washington and Caltech, working on NSF-funded projects such as the Ocean Observatories Initiative. He invites prospective students/postdocs to contact him via email.

• Seismology
• Oceanography
• Acoustic Monitoring
• +۳ مورد دیگر

Prof. Hansjörg Kutterer is a Professor and Dean at the KIT-Department of Civil Engineering, Geo and Environmental Sciences at Karlsruhe Institute of Technology (KIT). His primary affiliation is with KIT's Department of Civil Engineering, Geo and Environmental Sciences. He leads geodetic research initiatives focusing on Earth observation systems, atmospheric modeling, and geophysical data analysis. His research emphasizes advanced applications of GNSS, InSAR, and satellite gravimetry for monitoring climate-related phenomena such as water vapor dynamics, terrestrial water storage changes, and ground motion patterns. Key projects include developing machine learning-enhanced models for tropospheric delay corrections and integrated water vapor estimation in the Upper Rhine Graben region. Prof. Kutterer actively contributes to international geodetic frameworks like the Global Geodetic Observing System (GGOS), particularly through DA-CH regional collaborations. His work bridges geodetic methodologies with interdisciplinary challenges in climate science and environmental engineering. He oversees departmental operations as Dean, fostering innovation in geospatial education and infrastructure. His technical expertise spans geodetic deformation analysis, statistical robust estimation, and the integration of geophysical models with observational data.

• Geodesy
• Geophysics
• Environmental Science
• +۳ مورد دیگر

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +۷ مورد دیگر

Aaron Maxwell is an Associate Professor in the Department of Geology and Geography at West Virginia University (WVU). He serves as the principal investigator of West Virginia View, a member of AmericaView, and director of the WV GIS Technical Center. His research focuses on spatial predictive modeling, geohazard mapping, machine/deep learning applications in remote sensing, and thematic map accuracy assessment. He holds degrees from Alderson Broaddus University (B.S. in Biology, Chemistry, Environmental Science) and WVU (M.S. and Ph.D. in Geology), with a GIS Professional (GISP) certification. Education: Bachelor of Science in Biology, Chemistry, and Environmental Science – Alderson Broaddus University Master of Science in Geology – West Virginia University Doctor of Philosophy in Geology – West Virginia University Research Interests: Dr. Maxwell’s work emphasizes computational methods to extract insights from geospatial data. Key areas include: Deep learning for geomorphic feature extraction (e.g., LIDAR-based semantic segmentation) Machine learning applications in forest fuel load estimation and slope failure modeling Best practices for assessing deep learning outputs in remote sensing Synthetic data generation for predictive modeling Community flood resiliency and participatory GIS Publications: His recent work highlights advancements in geospatial deep learning (e.g., the geodl R package), accuracy assessment metrics for imbalanced datasets, and modeling post-mining landscape evolution. Articles often blend theoretical frameworks with applied case studies across environmental and geotechnical domains. Grants & Funding: Supported by NSF (CAREER Award) and AmericaView, his work trains students and develops open-source geospatial tools. Current projects include synthetic forest stand modeling and flood resiliency initiatives. Labs & Teams: Leads WV View, fostering remote sensing education and data sharing. Collaborates on geospatial workforce development and open-source software initiatives (e.g., GIScience courses, ArcGIS Pro labs).

• Geospatial Science
• Remote Sensing
• Machine Learning
• +۶ مورد دیگر

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.

• Advanced Sensing Technologies
• Artificial Intelligence
• Computational Mechanics
• +۵ مورد دیگر

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.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +۹ مورد دیگر

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.

• Artificial Intelligence
• Robotics
• Biology-Inspired Computing
• +۶ مورد دیگر

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.

• Geotechnical Earthquake Engineering
• Seismic Hazard Mapping
• Natural Hazards
• +۳ مورد دیگر