جست‌وجوی استاد و پژوهشگر

Xiaohui Qiمشاهده پروفایل

استادیار

• Geotechnical Engineering
• Risk and Reliability Analysis
• Probabilistic Site Investigations
• +۳ مورد دیگر

Dr. Xiaohui Qi is an Assistant Professor in the Department of Mechanical and Construction Engineering at Northumbria University since 2020. His research focuses on reliability analysis of geotechnical structures, probabilistic site investigations, and data-driven methods for predicting geotechnical and geological properties. Prior affiliations include postdoctoral roles at Nanyang Technological University (2017-2020) and the University of Macau (2015-2017), alongside a PhD from Wuhan University (2015) and an exchange at the National University of Singapore (2012-2015). His work addresses uncertainty in geotechnical engineering through Bayesian back-analysis, spatial variability characterization, and AI-driven predictive modeling. Key interests include slope stability, braced excavations, and soil parameter estimation using limited data. Recent research trends emphasize integrating machine learning (e.g., Bayesian methods, generalized additive models) with geostatistical techniques for 3D site reconstruction and geological interface prediction. His studies often compare methodologies (e.g., coupled Markov chains vs. stochastic simulations) to optimize spatial prediction accuracy. Collaborations span institutions in Singapore, China, and elsewhere, with a focus on applications like subway construction and foundation design uncertainties. No scientific awards are listed, but his 1625 citations highlight impactful contributions to geotechnical reliability and data analytics.

Paul Santiمشاهده پروفایل

استاد

• Geologic Hazards
• Landslide Dynamics
• Debris Flow Mitigation
• +۳ مورد دیگر

Paul Santi is a Professor of Geology and Geological Engineering at the Colorado School of Mines. He specializes in geologic hazards research, with a focus on landslides, debris flows, and risk mitigation strategies. His academic career includes roles in education, research, and applied geotechnical studies. Education: Ph.D. in Geological Engineering from Colorado School of Mines M.S. in Geology from Texas A&M University B.S. in Geology and Physics with honors from Duke University Research interests include landslide mechanics, post-wildfire debris flow prediction, climate change impacts on geomorphology, and international hazard mitigation partnerships in Peru. His work integrates field observations, remote sensing, numerical modeling, and policy development. Publications emphasize interdisciplinary approaches to hazard assessment and community resilience, particularly in data-scarce environments. Notable contributions include frameworks for landslide runout scoring, debris flow avulsion analysis, and innovations in landslide susceptibility modeling. Scientific awards include multiple AEG Outstanding Student Professional Papers (2011, 2010) and recognition for landslide drain design research (2008). His work often bridges academic research with real-world applications, including disaster preparedness initiatives in Peru and post-wildfire hazard mapping in Colorado. Labs/Teams: Collaborations with international research groups in Peru, engagement with local communities for hazard awareness programs.

• Earthquake Engineering
• Structural Engineering
• Seismic Reliability
• +۷ مورد دیگر

Paolo Castaldo is a Full Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as Deputy Director of the Department and Scientific Director of several national research projects. He is actively involved in teaching at both the undergraduate and PhD levels and is a member of the University Open Access Commission. His research interests include: Earthquake Engineering Structural Safety and Reliability Seismic Isolation using Friction Pendulum Systems (FPS) Reinforced Concrete Structures Bridges and Viaducts Numerical Modelling and Simulation Castaldo's recent publications focus on seismic performance, safety assessment, and reliability of isolated and reinforced concrete structures, often involving nonlinear finite element analysis and probabilistic evaluation. His work frequently addresses practical applications in bridge engineering, structural robustness, and retrofitting of existing infrastructure. Among his scientific contributions are leadership roles in PRIN-funded research projects such as 'Life-long optimized structural assessment and proactive maintenance with pervasive sensing techniques' and 'Data Fusion Based Digital Twins for Structural Safety Assessment'. He also leads commercial and departmental agreements involving structural safety assessments for public infrastructure. He supervises several PhD students, including Gabriele Neri, Guglielmo Amendola, Gaspar Andres Auad Alvarez, and Elena Miceli, guiding research on seismic reliability, impact mitigation in isolated structures, and robustness of concrete frames. His research is applied in real-world contexts, including collaborations with the ReLUIS Interuniversity Consortium and the Department of Civil Protection, as well as with the Archaeological Park of Paestum and Velia for structural assessment of historical sites.

Alvaro Nosedal Sanchezمشاهده پروفایل

دانشیار

• Nonparametric Regression
• Gaussian Markov Random Fields
• Linear Models
• +۲ مورد دیگر

Alvaro Nosedal Sanchez is an Associate Professor in the Teaching Stream and Associate Chair (STATS) at the Department of Mathematical and Computational Sciences , University of Toronto Mississauga. His research integrates statistical theory with real-world applications in climate science, transportation, and disaster management. Research Interests Nonparametric Regression Gaussian Markov Random Fields Linear Models Bayesian Statistics Application of Statistical Methods Publication Trends His work spans 2012–2019 , focusing on statistical methodologies for transportation systems, climate modeling, and disaster response. Key themes include spatial statistics, algorithm development, and probabilistic modeling. Recent publications emphasize logistic regression in utility coordination and Gaussian Markov Random Fields in climate analysis. Teaching STA 215: Introduction to Applied Statistics STA 218: Statistics for Management and Economics STA 256/258/260: Core statistics and probability courses STA 302: Regression Analysis STA 313: Topics in Statistics STA 437: Applied Multivariate Statistics STA 457: Applied Time Series Analysis

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +۷ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

Olivier DECKمشاهده پروفایل

استاد

• Geotechnical Engineering
• Civil Engineering
• Risk Assessment
• +۳ مورد دیگر

Olivier DECK is a Professor at the University of Lorraine , affiliated with the Department of Geosciences and Civil Engineering . He serves as Head of the department and is attached to the GeoResources Laboratory and the GEOSYSTEMES research team. His professional address is at ARTEM-Office R220, and he has been a member of the University Association of Civil Engineering's scientific committee since 2015. Research interests include: Soil-structure interaction Vulnerability of structures to natural hazards Risk management in civil engineering Uncertainty quantification in geological hazards His work focuses on mining subsidence, ground movement, and building damage assessment using analytical and numerical methods. He has supervised numerous PhD theses on these topics and contributed to decision-support methodologies for sustainable risk management. Teaching responsibilities include leading the M2 "Risques" track in Civil Engineering at University of Lorraine, lecturing in structural mechanics, geotechnics, and numerical modeling, and serving on the Civil Engineering aggregation jury since 2015. Scientific responsibilities include: Leading the ANR-INCERDD project (2011-2014) on uncertainty quantification Organizing committees for National Geotechnics Days (2016) and other scientific events Peer-reviewer for journals like Computers and Geotechnics and Natural Hazards

• Risk Assessment
• Ecosystem Services
• Environmental Exposure
• +۷ مورد دیگر

Katherine Ellen von Stackelberg is a Senior Research Scientist in the Department of Environmental Health at Harvard T.H. Chan School of Public Health. She holds leadership roles as Team Leader for the Biogeochemistry of Global Contaminants Group (Sunderland Lab) and Director of Research Translation for the Harvard Superfund Research Program (MEMCARE). Her interdisciplinary work bridges environmental science, risk assessment, and policy development. Education: AB, cum laude in General Studies from Harvard College (1988) ScM in Environmental Health and Health Policy from Harvard School of Public Health (1998) ScD in Environmental Science and Risk Management from Harvard School of Public Health (2006) Her research focuses on the intersection of environmental exposures, ecosystem services, and human health. Key areas include: Risk assessment frameworks for environmental contaminants (PFAS, metals, PCBs) Valuation of natural capital and biodiversity through ecosystem integrity indices Development of probabilistic bioaccumulation models for aquatic systems Socio-ecological approaches to planetary health and regenerative futures She teaches courses on socio-ecological systems that analyze economic drivers of environmental degradation. Her scholarly publications demonstrate consistent focus on environmental risk assessment methodologies, with recent expansion into global contaminant distribution, ecosystem service valuation, and health impacts in vulnerable populations. Research frequently incorporates machine learning, spatial modeling, and decision-analytic frameworks. Dr. von Stackelberg has served on the US EPA's Board of Scientific Counselors and National Academies panels, and regularly reviews for the European Commission Horizon program. She leads multiple projects on risk assessment frameworks and contaminant bioaccumulation modeling.

• Seismology
• Earthquake Engineering
• Ground Motion Prediction
• +۳ مورد دیگر

Dr. Jaleena Sunny is a Research Fellow at the Swiss Seismological Service (SED) based at ETH Zürich. Her work focuses on advancing ground motion prediction methodologies, induced seismicity analysis, and uncertainty quantification in seismic hazard assessment. She specializes in developing stochastic models for earthquake ground motion and validating their performance through novel metrics. Her research interests include seismology, earthquake engineering, and regional seismic risk analysis with applications to both natural and induced seismic events. Dr. Sunny's recent work emphasizes improving ground motion prediction equations for unconventional seismic sources like hydraulic fracturing-induced tremors and optimizing computational algorithms for model validation. Her publications (2018-2025) demonstrate contributions to stochastic ground motion modeling, induced seismicity characterization, and the development of metrics for comparing seismic models. Notable projects include extending the ERM-CH23 model for Swiss shallow induced seismicity and creating soil amplification maps for spectral acceleration periods. Awards: None explicitly mentioned Grants/Advising: No grants or advisees listed She is affiliated with the SED lab at ETH Zürich, contributing to Switzerland's earthquake monitoring infrastructure and hazard mitigation strategies.

Anne Kiremidjianمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Health Monitoring
• Risk Assessment
• +۳ مورد دیگر

Anne Kiremidjian is the C.L. Peck, Class of 1906 Professor in Stanford University's School of Engineering, specializing in Civil and Environmental Engineering. She holds a BA in Physics from Queens College (1972), BS in Civil Engineering from Columbia University (1972), and MS/PhD in Structural Engineering from Stanford (1973/1977). Her research focuses on earthquake hazard modeling, structural resilience, and wireless sensor networks for infrastructure monitoring. Key roles include Co-Director (1987-1995) and Director (1995-2002) of the John A. Blume Earthquake Engineering Research Center. She co-founded K2 Technologies and Sensametrics, Inc., and has served on numerous national and international advisory boards, including the National Academy of Engineering and the John and Jene Blume Foundation. Her research emphasizes data-driven methods for post-earthquake damage assessment, integrating machine learning and sensor networks. Notable contributions include algorithms for structural health monitoring and frameworks for prioritizing healthcare system recovery post-disaster. Over 130 publications and 15+ honors highlight her impact, including the 2023 Thomas Egleston Medal and National Academy of Engineering membership (2021). Teaching spans decision analysis, structural engineering, and research mentorship. Her work bridges academia and industry, addressing global challenges in disaster resilience and sustainable infrastructure.

Irene Loمشاهده پروفایل

استادیار

• Market Design
• Operations Research
• Socially Responsible Operations Research
• +۵ مورد دیگر

Irene Lo is an Assistant Professor in the Department of Management Science and Engineering at Stanford University. She is affiliated with multiple institutes including the Stanford Institute for Human-Centered AI, the Stanford King Center for Global Development, and the Stanford Woods Institute for the Environment. Her research focuses on designing fair and efficient market mechanisms for education, environmental, and global development contexts. Education: Ph.D. in Operations Research, Columbia University (201?) B.A. in Mathematics, Princeton University Postdoctoral Fellow, Stanford Economics Department (supervised by Al Roth) Research Interests: Market design for public sector applications Mechanism design for social good Graph theory and combinatorial optimization Socially responsible operations research Awards: William T. Grant Scholar (2025) Co-founder of the ACM Conference on Equity and Access in Algorithms Recent Activities: Launched the Stanford Impact Lab on Equitable Access to Education Co-organized Rising Stars Workshops in Industrial Engineering Her work spans academic advising across multiple institutions, including co-advising Ph.D. students Andrei Graur and Anushka Murthy. She teaches courses such as Market Design and Resource Allocation in Non-Profit Settings and Introduction to Game Theory.

• Maritime Safety
• Naval Architecture
• Risk Assessment
• +۷ مورد دیگر

Kostas Spyrou is a Visiting Professor in the Department of Naval Architecture, Ocean and Marine Engineering at the University of Strathclyde, Faculty of Engineering. His academic work centers on maritime safety, risk assessment, and ship behavior under extreme conditions. His research interests include: Probabilistic modeling for fire safety onboard ships Risk-based design frameworks in marine environments Ship maneuverability and survivability in extreme astern seas Safety improvement strategies for bulk carriers and other vessels Interplay between design, operation, and environmental conditions The published articles highlight a consistent focus on enhancing maritime safety through quantitative risk analysis and system-level design improvements. His work spans both theoretical modeling and practical application in naval architecture. Key themes include fire safety protocols, emergency response systems, and dynamic vessel performance under adverse weather. He served as Principal Investigator on the ONR-NICOP Research Grant (2013–2017), which advanced knowledge in onboard safety systems. This project underscores his leadership in externally funded research initiatives. He is affiliated with the following research environment: Naval Architecture, Ocean and Marine Engineering Research Group, University of Strathclyde

• Seismic Hazard Assessment
• Earthquake Risk Modeling
• Natural Disaster Risk Analysis
• +۵ مورد دیگر

L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.

• Sustainable Architecture
• Life Cycle Assessment (LCA)
• Life Cycle Costing (LCC)
• +۱۲ مورد دیگر

Elena Fregonara is a professor in the Department of Architecture and Design at Politecnico di Torino, where she conducts research at the intersection of sustainable architecture, real estate economics, and environmental policy. Her work emphasizes life cycle thinking, circular economy principles, and the integration of environmental costs into financial decision-making in the construction sector. Institution: Politecnico di Torino Department: Department of Architecture and Design Research Focus: Sustainable Architecture, Life Cycle Assessment, Real Estate Appraisal, Circular Economy Her research interests revolve around sustainable urban development, with a strong emphasis on the economic evaluation of building sustainability. She investigates how embodied energy, carbon emissions, daylight, and green attributes influence real estate values and investment decisions. Her work bridges theoretical economics with practical applications in public procurement, urban regeneration, and building retrofitting. The trends in her recent publications show a consistent focus on integrating environmental externalities into traditional economic models. She employs life cycle costing (LCC), life cycle assessment (LCA), structural equation modeling, and net present value (NPV) frameworks to evaluate sustainability across scales—from materials to urban systems. Her research increasingly addresses policy implications, especially in public procurement and urban governance. Elena Fregonara has contributed to significant research initiatives such as the “Circular Design for Natural Fibers” (CD4NF) project under the PNRR, reflecting her engagement with national sustainability strategies. She has no listed scientific awards in the provided data. She has collaborated extensively with researchers such as Diego G. Ferrando, Alice Barreca, and Irene Rubino on topics including building upcycling, discount rate modeling, and sustainable public procurement. While her mentorship of students is not explicitly detailed, her collaborative work suggests active engagement in research teams and academic networks. She is involved in developing methodological tools like the “Smart SPP LCC-CO2 Tool” for sustainable decision-making. Her research lab or team is not explicitly named, but her involvement in EU and PNRR-funded projects indicates leadership or key roles in interdisciplinary research groups focused on sustainable construction and urban development.

Florian Steinkeمشاهده پروفایل

استاد

• Energy Management Systems
• Machine Learning Applications in Energy
• Distributed Control Systems
• +۵ مورد دیگر

Prof. Dr. Florian Steinke is a Professor and Head of the Energy Information Networks and Systems Department at Technische Universität Darmstadt. His academic career spans roles at Siemens Corporate Technology (2009–2016) and a PhD in machine learning at the Max Planck Institute for Intelligent Systems (2006–2008). His research focuses on algorithmic energy management, distributed control systems, machine learning applications in energy grids, and resilient smart grid design. Education: PhD in Machine Learning (Max Planck Institute for Intelligent Systems, 2006–2008) Diplom in Computational Physics (University of Tübingen & University of Washington, 1999–2005) Research Interests: Development of cyber-physical systems for energy grids Optimization of thermal-electric systems using game theory and stochastic control Integration of social media data for demand forecasting Cybersecurity measures against adversarial attacks on grids Recent work emphasizes probabilistic grid modeling, resilient energy market design, and AI-driven control strategies for Fourth Generation district heating grids. His platform ecosystem research aims to support the energy transition through data-driven solutions. Labs/Teams: Leads the Energy Information Networks and Systems research group, focusing on interdisciplinary projects combining automation, data science, and energy systems engineering.

Shanghong Xieمشاهده پروفایل

استادیار

• Machine Learning
• Network Analysis
• Precision Medicine
• +۱۲ مورد دیگر

Shanghong Xie is an Assistant Professor in the Department of Statistics at the University of South Carolina, College of Arts and Sciences. She is also a member of the Carolina Autism and Neurodevelopment (CAN) Research Center and the Institute for Mind and Brain (IMB), reflecting her interdisciplinary focus on statistical methods for neuroscience and public health. Dr. Xie earned her PhD in Biostatistics from Columbia University in 2019 and completed her postdoctoral training there. Her research centers on developing machine learning and generative models to tackle complex challenges in functional data analysis, network modeling, causal inference, and precision medicine. She applies these methods to neuroimaging (fMRI, DTI, EEG), genetic biomarkers, and public health data, particularly in mental health and neurological disorders. Her recent publications demonstrate a strong trajectory in methodological innovation with high-impact applications, including COVID-19 forecasting models adopted by the CDC, mediation analysis with neuroimaging, and personalized treatment regime estimation. The research trends highlight expertise in high-dimensional data, graphical models, and integrative learning across multi-modal biomarkers. American Statistical Association (ASA) Statistics in Imaging Section First Prize Student Paper Award American Statistical Association (ASA) Mental Health Statistics Section Best Student Paper Award International Conference on Health Policy Statistics (ICHPS) Student Travel Award Dr. Xie advises several graduate students and co-founded the Functional and Complex Data Analysis (FUNCODA) Working Group, fostering collaboration on advanced data modeling. She has been involved in significant grant-supported work, notably contributing to the CDC’s ensemble forecasting effort during the pandemic through her survival-convolution model. Her lab emphasizes computational statistics and open science, with code available on GitHub.