جست‌وجوی استاد و پژوهشگر

• 4D Seismic Analysis
• Reservoir Engineering
• Carbon Capture & Storage
• +۳ مورد دیگر

Gustavo Corte is a Research Associate at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, affiliated with the Institute for GeoEnergy Engineering. His research focuses on 4D seismic inversion, reservoir engineering, and carbon capture applications. He has contributed to projects involving Bayesian inversion techniques, machine learning applications in seismic data analysis, and CO2 dissolution modeling in North Sea reservoirs. Research interests include dynamic reservoir monitoring through seismic data, probabilistic estimation of subsurface parameters, and integration of geophysical data with reservoir simulation tools. His work emphasizes practical applications in hydrocarbon fields and carbon storage validation. Key collaborations include studies on the Catcher Fields and West of Shetlands regions. Corte's publications span peer-reviewed journals like Geophysical Prospecting and conference proceedings, with a focus on advancing seismic data interpretation methods. His work has been cited 36 times and accessed widely in academic platforms.

Xuefei LUمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +۹ مورد دیگر

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

Thierry Kleinمشاهده پروفایل

استاد

• Probability Theory
• Statistics
• Uncertainty Quantification
• +۴ مورد دیگر

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.

Barbaros Yetمشاهده پروفایل

دانشیار

• Decision-Making Under Uncertainty
• Bayesian Approaches
• Causal and Probabilistic Graphical Models
• +۵ مورد دیگر

Barbaros Yet is an Associate Professor in the Department of Cognitive Science at the Graduate School of Informatics, Middle East Technical University in Ankara, Turkey. His research focuses on decision-making under uncertainty, integrating human judgment, published evidence, and uncertain data into Bayesian approaches and probabilistic graphical models. Applications span healthcare (clinical decision support, trauma care modeling), agriculture (climate resilience policy), and operational systems (fleet reliability prediction). Research interests include Bayesian networks, causal reasoning, knowledge representation, and decision support systems. He has developed tools for shared clinical decision-making, trauma outcome prediction, and agricultural policy prioritization. His work emphasizes interpretable models that align with expert reasoning processes. Notable contributions include Bayesian network-based frameworks for spinal pathology assessment, aircraft reliability modeling, and trauma care prediction systems. His articles frequently bridge theoretical probabilistic modeling with practical applications in medicine and engineering. Active in interdisciplinary collaborations, he contributes to global health initiatives like the UKRI Action Against Stunting Hub. Professional links include ORCID (0000-0003-4058-2677), ResearchGate (https://www.researchgate.net/profile/Barbaros-Yet), and a Twitter presence (@barbarosyet). Contact via byet@metu.edu.tr.

Zenon Medina-Cetinaمشاهده پروفایل

دانشیار

• Stochastic Geomechanics
• Risk and Reliability Analysis
• Bayesian Networks
• +۷ مورد دیگر

Zenon Medina-Cetina is an Associate Professor in the Zachry Department of Civil & Environmental Engineering at Texas A&M University (TAMU), with affiliations to the Mosbacher Institute for Trade, Economics, and Public Policy at the Bush School of Government and Public Service. He holds courtesy joint appointments in Petroleum Engineering, Ocean Engineering, Multidisciplinary Engineering, and Geography (College of Arts and Sciences). Dr. Medina-Cetina specializes in probabilistic risk assessment of geo-systems, stochastic geomechanics, and integrated site characterization. His work bridges engineering, geoscience, and computational methods to address complex challenges in infrastructure resilience, offshore geotechnics, and environmental systems. Education: Ph.D. (2006), M.Sc. (2005) in Civil Engineering from Johns Hopkins University; M.E. (1996) in Geostatistics from Universidad Nacional Autónoma de México (UNAM); B.Sc. (1994) in Soil Dynamics from Universidad Autónoma de Yucatán (UADY). He completed postdoctoral research at SIMULA and the Norwegian Geotechnical Institute (NGI). Research Interests: Stochastic processes and spatio-temporal simulation (3D-T) Risk, reliability, and sensitivity analysis Uncertainty quantification and inverse problems Bayesian networks and machine learning applications Optimal modeling of geomechanical systems Arctic and Gulf of Mexico geohazard mitigation Awards & Recognition: 2024: Mosbacher Research Fellow, College of Engineering Excellence Award 2022: Zachry Career Development Professorship II, Excellence in Teaching Award 2012: Fellow, Society for Underwater Technology 2001: Fulbright Scholarship Grants & Projects: Lead investigator of $7.4M+ in research funding from federal agencies (e.g., DHS, NSF) and industry Director of the Stochastic Geomechanics Laboratory (SGL) Principal on initiatives like the Arctic X-Grant and Yucatán Initiative Co-lead of the Network-to-Network for the Gulf of Mexico Teaching & Mentoring: Developed TAMU’s Introduction to Research Abroad Program (IRAP) Advised 14 doctoral and 13 master’s students, plus postdoctoral researchers Course instructor in Bayesian Risk Networks, Stochastic Mechanics, and Geomechanics Labs & Teams: Stochastic Geomechanics Laboratory (SGL): Focuses on data-driven risk modeling and computational geomechanics International collaborations with institutions in Mexico, Norway, and the Arctic region

Michael Colaresiمشاهده پروفایل

استاد

• International Relations
• Political Methodology
• Computational Social Science
• +۳ مورد دیگر

Michael Colaresi is the Associate Vice Provost for Data Science and William S. Dietrich II Professor of Political Science at the University of Pittsburgh, affiliated with the Dietrich School of Arts and Sciences. He holds roles in the Center for Research Computing and Data, Computational Social Science program, and the Intelligent Systems Program at the School of Computing and Information. His research focuses on computational social science, leveraging machine learning and text analysis to study national security secrecy, political violence, and human rights dynamics. Colaresi co-directs the Responsible Data Science, Complexity, and AI for Peace (RedSCAIP) Lab, emphasizing interdisciplinary collaboration to address societal challenges. Education: PhD in Political Science from Indiana University (2002). He founded the Social Science Data Analytics initiative at Michigan State University and now leads efforts in Pitt’s Institute for Cyber Law, Policy, and Security. His work has been funded by NSF grants and foundations like Hillman and RK Mellon. Research Interests: International Relations, computational methods, conflict forecasting, and the intersection of technology with democratic governance. His 2014 book *Democracy Declassified* explores secrecy dilemmas in liberal democracies and was shortlisted for the Conflict Research Society Prize. Recent projects include the ViEWS political violence early-warning system and HR-VEAW human rights analytics platform. Awards: Co-winner of the 2017 Journal of Peace Research Best Visualization Award and 2006 Gosnell Prize for Political Methodology. He serves as co-editor of *International Interactions* and collaborates with institutions like the Peace Research Institute Oslo (PRIO) and Uppsala University’s Violence Early Warning Project. Teaching: Courses include Computational Social Science, Bayesian Statistics, and Text-as-Data methods. His pedagogical focus emphasizes bridging social science theory with modern computational tools.

• Porous Media
• Computational Mechanics
• Bayesian Inference
• +۳ مورد دیگر

Mina Karimi is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at California Institute of Technology (Caltech). She is part of the Bhattacharya group, advised by Professor Kaushik Bhattacharya. Her research focuses on computational mechanics, poromechanics, and Bayesian inference applied to porous media systems. Key areas include reactive flow modeling, multiscale simulations, and data-driven approaches for subsurface engineering challenges. Her work integrates advanced computational methods with geomechanical and materials science problems, emphasizing energy systems and environmental applications. Recent studies explore carbon sequestration mechanisms, chemo-poro-mechanical coupling, and high-dimensional parameter estimation using Bayesian frameworks. She also investigates crack-healing phenomena in shape memory alloy composites and develops accelerated micromechanical models for solute transport. Publications span topics from machine learning-enhanced groundwater modeling to Hessian-informed sampling techniques for high-dimensional inverse problems. Her research bridges theoretical developments with practical applications in subsurface energy storage, geological carbon sequestration, and material behavior under extreme conditions. Advising is conducted under the mentorship of Prof. Bhattacharya, with affiliations to the Resnick Sustainability Institute at Caltech. Current efforts emphasize computational tools for poromechanics and uncertainty quantification in complex multiphase systems.

Hoshin V. Guptaمشاهده پروفایل

استاد

• Surface Water Hydrology
• Climate Modeling
• Hydrologic Interactions with Climate
• +۹ مورد دیگر

Hoshin V. Gupta is a Regents' Professor at the University of Arizona, holding joint appointments in the Department of Hydrology and Atmospheric Sciences, Technology-Public Policy/Markets, Global Change GIDP, and Remote Sensing and Spatial Analysis GIDP. He specializes in interdisciplinary research at the intersection of hydrology, climate science, and decision-making frameworks. Education: PhD in Systems Engineering from Case Western Reserve University (1984). Research focuses on surface water hydrology, climate modeling, and the integration of hydrologic processes with policy analysis. Techniques include computational modeling, machine learning, remote sensing data assimilation, and Bayesian methods. His work addresses uncertainty quantification, decision support systems, and the human-water nexus. Professional roles include membership in the Promotion and Tenure Committee and leadership in interdisciplinary initiatives through Global Change and Remote Sensing GIDPs.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +۴ مورد دیگر

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

Mojtaba Dirbazمشاهده پروفایل

استاد مدعو

• Structural Engineering
• Structural Health Monitoring
• Bayesian Methods in Engineering
• +۳ مورد دیگر

Mojtaba Dirbaz is an Adjunct Professor at the Department of Civil, Architectural, and Environmental Engineering within the Armour College of Engineering at Illinois Institute of Technology. His doctoral degree in Civil Engineering (Structural Engineering) was awarded by Illinois Institute of Technology in 2013. His research focuses on advancing structural health monitoring and infrastructure assessment through Bayesian methodologies. Key interests include damage detection, modal analysis, and uncertainty quantification in civil engineering systems. His work applies probabilistic frameworks to bridge condition assessment and structural integrity evaluation using limited or uncertain field data. Recent publications emphasize Bayesian updating techniques for infrastructure diagnostics, integrating visual inspection and modal data to enhance reliability in structural condition assessment. No scientific awards are explicitly listed. Advising roles and grants are not mentioned in available records. No affiliated labs or teams are noted in the provided information.

Benjamin Burkhardمشاهده پروفایل

استاد

• Ecosystem Services
• Landscape Ecology
• Physical Geography
• +۳ مورد دیگر

Prof. Dr. Benjamin Burkhard is a Professor at the Institute of Earth System Sciences within the Faculty of Natural Sciences at Leibniz University Hannover. He serves as Deputy Head of the Examining Board for Landscape Sciences (MSc) and holds management responsibilities in the Physical Geography and Landscape Ecology Section. Additionally, he represents professors on the Faculty Council and the Examination Board for Geography (BSc), demonstrating his significant institutional involvement. His research spans three primary domains: physical geography and landscape ecology (focusing on mapping and analysis of landscape structures, processes and functions across different spatio-temporal scales), ecosystem services (with emphasis on modeling, quantification and mapping), and human-environmental relations (particularly indication, modeling and land use assessments). This interdisciplinary approach positions him at the forefront of environmental systems research. Prof. Burkhard's scholarly output reveals a strong focus on ecosystem services assessment methodologies, with recent publications addressing standardized ecosystem condition assessments, cultural ecosystem services valuation, and marine ecosystem services mapping. His work demonstrates a clear progression from theoretical frameworks toward practical applications for environmental management and policy-making, with increasing emphasis on participatory approaches and standardized assessment protocols. He leads numerous significant research projects including 'SMILES: Enhancing Small-Medium Islands resilience by securing the sustainability of Ecosystem Services' (2022-2026), 'SELINA: Science for Evidence-based and sustainable decisions about natural capital' (2022-2027), and long-term erosion monitoring projects in Lower Saxony. These projects reflect his commitment to addressing pressing environmental challenges through rigorous scientific investigation. Prof. Burkhard actively contributes to developing methodological frameworks for ecosystem services assessment, participating in European research networks and initiatives that bridge the gap between scientific research and practical environmental management applications. His work has established him as a key contributor to the evolving field of ecosystem services science and its application in policy contexts.

Karl Haapalaمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Life Cycle Engineering
• Environmental Performance Modeling
• +۴ مورد دیگر

Karl Haapala is a Professor in the Department of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University (OSU), where he directs the Industrial Sustainability Lab and the OSU Energy Efficiency Center. He holds a Ph.D. from Michigan Technological University (2008) and has over $38M in research funding from agencies like DOE, NSF, and industry partners. His expertise spans sustainable manufacturing, life cycle engineering, and process modeling for environmental performance improvement. He has published over 150 peer-reviewed works and received awards such as the 2014 SME Outstanding Young Manufacturing Engineer Award and the 2019 Fulbright Scholar Award to Tampere University, Finland. Education: Ph.D., Mechanical Engineering-Engineering Mechanics, Michigan Technological University (2008) M.S., Mechanical Engineering, Michigan Technological University (2003) B.S., Mechanical Engineering, Michigan Technological University (2001) Research Interests: Dr. Haapala focuses on sustainable design and manufacturing, including life cycle assessment (LCA), process modeling for environmental efficiency, additive manufacturing sustainability, and education in sustainable engineering. His work bridges theoretical frameworks with industrial applications, emphasizing collaboration with SMEs and global partners. Grants and Partnerships: He leads the DOE Industrial Assessment Center at OSU and collaborates with the CESMII Western Smart Manufacturing Innovation Center. His industry partnerships include Boeing, Caterpillar, HP Inc., and Tillamook. Awards: 2023 SME Distinguished Faculty Advisor 2019 Fulbright Scholar Award 2015 ASME DFMLC Best Paper 2012 CIRP LCE Best Paper Labs and Teams: Directs the Industrial Sustainability Lab, focusing on smart manufacturing, energy efficiency, and circular economy solutions. Engages in international collaborations, including a 2019-2020 Fulbright appointment at Tampere University to advance additive manufacturing sustainability.

Mohammad Qamarul Islamمشاهده پروفایل

استاد

• Statistical Inference
• Robust Statistical Methods
• Nonparametric Methods
• +۴ مورد دیگر

Prof. Mohammad Qamarul Islam is a Professor in the Statistics Department at Middle East Technical University (METU), Turkey, a position held since 2016. Previously, he served as Professor (2010-2016), Associate Professor (2005-2010), and Assistant Professor (1999-2005) in the Economics Department at Cankaya University. His academic career spans over four decades with prior appointments at METU (1983-1999) and Pakistani institutions including Gomal University and Karachi University. Education: Ph.D. in Statistics (1989), Middle East Technical University, Turkey M.Sc. in Statistics (1972), University of Karachi, Pakistan Research Focus: Prof. Islam specializes in advanced statistical methodologies with emphasis on Statistical Inference and Robust Methods . His work bridges theoretical statistics and practical applications through Nonparametric Techniques , Multivariate Analysis , and Econometric Modeling . Current investigations focus on regression under non-standard error distributions and experimental design optimization, contributing significantly to statistical theory validation in real-world scenarios. Publication Trends: His 2012-2016 publications reveal concentrated expertise in multivariate regression under non-ideal conditions (non-normal/elliptical errors), demonstrating methodological innovations for robust parameter estimation. Recent work extends to model uncertainty quantification in time series, reflecting his evolving focus on practical statistical challenges in economic and scientific data analysis. Academic Service: Information regarding student advising, research grants, laboratory affiliations, or scientific awards is not documented in the provided materials.

Taufiquar Khanمشاهده پروفایل

استاد

• Applied Mathematics
• Inverse Problems
• Computational Mathematics
• +۳ مورد دیگر

Taufiquar Khan is a Professor and Chair of the Department of Mathematics and Statistics at the University of North Carolina at Charlotte (UNC Charlotte). Previously, he held professorial positions at Clemson University between 2000 and 2020, advancing from Assistant Professor to Professor of Mathematical Sciences. His academic leadership roles include Chair at UNC Charlotte since 2020 and various administrative roles at Clemson, including Associate Director for Graduate Studies and Director of Global Engagement Initiatives. He holds a Ph.D. in Applied Mathematics from the University of Southern California (2000) and degrees in Mathematics, Physics, and Aerospace Engineering from USC and Occidental College. His research focuses on inverse problems, computational mathematics, and their applications in biomedical imaging, engineering systems, and environmental science. Notable contributions include advancements in electrical impedance tomography, machine learning for inverse problems, and mathematical modeling of traffic systems. He has authored over 50 peer-reviewed publications and supervised multiple graduate students. Dr. Khan’s awards include a Humboldt Research Fellowship (2007) and Summer Faculty Fellowships at Brooks Air Force Laboratory. He has led international collaborations, including roles at Khalifa University and the University of Bremen. His work bridges theoretical mathematics with practical applications in healthcare, energy systems, and environmental sustainability.

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.