جست‌وجوی استاد و پژوهشگر

William W.S. Weiمشاهده پروفایل

استاد

• Time Series Analysis
• Forecasting Methods
• Statistical Modeling
• +۵ مورد دیگر

William W.S. Wei is a Professor of Statistics at Temple University’s Fox School of Business and Management, where he has served since 1974. Previously, he chaired the Department of Statistics (1982–1987) and directed graduate programs in Statistics. He holds a B.A. in Economics (National Taiwan University, 1966), a B.A. in Mathematics (University of Oregon, 1969), and an M.S. (1972) and Ph.D. (1974) in Statistics from the University of Wisconsin, Madison. His research focuses on time series analysis, forecasting, and multivariate statistical modeling, with contributions to aggregation effects, outlier detection, and high-dimensional dimension reduction. He authored seminal books like Time Series Analysis (1990, 2006) and Multivariate Time Series Analysis and Applications (2019). His work emphasizes practical applications across economics, finance, and environmental science. Dr. Wei is a Fellow of the American Statistical Association and Royal Statistical Society, and served as 2002 ICSA President. He has received Temple’s 2016 Musser Award and two Lifetime Achievement Awards (2014, 2024). He has advised numerous Ph.D. students now in academia and industry. Beyond academia, he contributes to community education and leadership roles in Taiwanese Hakka organizations.

• Bayesian methods
• Biostatistics
• Data science
• +۵ مورد دیگر

Saman Muthukumarana is a Professor and Department Head in the Department of Statistics at the University of Manitoba's Faculty of Science. His research focuses on Bayesian methods, biostatistics, data science, environmental statistics, multivariate analysis, and statistical machine learning. He actively publishes in top-tier journals and conferences, addressing challenges in imbalanced data classification, anomaly detection, and computational statistics. Recent work includes advancing SMOTE techniques for high-dimensional data, Bayesian network analysis for environmental monitoring, and applications of machine learning to healthcare and sports analytics. He is a sought-after collaborator, contributing to interdisciplinary projects in ecology, engineering, and public health. His academic profile highlights over 60 peer-reviewed publications since 2008, reflecting a strong commitment to methodological innovation and real-world impact. Muthukumarana is also involved in supervising graduate students and mentoring early-career researchers. His departmental leadership positions him at the forefront of statistical education and research strategy within the Faculty of Science.

Alejandro Preraمشاهده پروفایل

دانشیار

• Environmental and Natural Resources Economics
• International Development Economics
• Public Policy Analysis
• +۳ مورد دیگر

Alejandro Prera is an Associate Professor at the School of Economic Sciences, Washington State University (WSU). His research focuses on environmental and natural resources economics, international development economics, and public policy analysis. He holds a PhD in Economics from the University of New Mexico (2014), an MA in Economics (2008), and a BS in Business Administration from the University of Nebraska-Lincoln (2000). His research explores topics such as water resource management in Uzbekistan, impacts of federal land policies on recreation demand in the Southwest U.S., and the effects of economic integration on social infrastructure in Central America. He employs experimental economics, statistical analysis, and policy modeling in his work. His publications address both theoretical and applied issues in environmental economics, behavioral economics, and development studies. Prera’s work often bridges academic theory with practical policy applications, particularly in natural resource management and international development contexts. He contributes to interdisciplinary collaborations involving environmental scientists, policymakers, and stakeholders to address complex socio-economic challenges.

Casey Paquolaمشاهده پروفایل

پژوهشگر

• Neurodevelopment
• Neuroimaging
• Computational Neuroscience
• +۳ مورد دیگر

Dr. Casey Paquola leads the Multiscale Neurodevelopment Team at the Institute of Neuroscience and Medicine (INM-7), Research Center Jülich. His research focuses on understanding neurodevelopmental processes through multiscale frameworks that integrate postmortem histology, transcriptomics, and in vivo neuroimaging (e.g., MRI, EEG). The team studies cytoarchitecture maturation, connectivity, and functional dynamics across early life to elucidate mechanisms of neurodevelopmental disorders. Their work emphasizes microstructure as a bridge between postmortem and in vivo data, identifying principles like large-scale cortical gradients. Research methods include microscopy, neuroimaging, and computational modeling to construct brain models. Key areas of study include the default mode network, epilepsy-related brain changes, and the interplay between genetics and brain structure. The team develops tools like BrainSpace and Micapipe for analyzing structural-functional relationships in neuroimaging datasets. Led initiatives: ENIGMA-Toolbox for transdiagnostic analysis, BrainStat for statistical neuroimaging analysis. Focus areas: Cortical development gradients, functional resilience, and translational neuroimaging. Collaborations: Multidisciplinary teams combining computational, clinical, and experimental neuroscience. Future work aims to advance precision medicine through personalized brain mapping and understanding neurodevelopmental disorders' multiscale origins.

• Statistical theory and methods
• Extreme value theory
• Environmental statistics
• +۳ مورد دیگر

Anthony Davison is a Professor Emeritus at EPFL's School of Basic Sciences, affiliated with the Department of Mathematical Sciences (SMA-ENS) and the Institute of Mathematics (PH-SB). He holds an Honorary Professor title at EPFL. Education: BA in Mathematics (Oxford, 1980), MSc and PhD in Statistics (Imperial College London, 1981–1984). Research: Focuses on statistical theory/methods, extreme value theory, environmental/biological applications, and risk modeling. His work includes analyzing climate extremes, insurance claims, and spatial-temporal phenomena. Recent articles address extreme thunderstorms, hail damage modeling, and longevity limits. Teaching: Supervised over 20 PhD students, including Mario Krali, Ophélia Miralles, and Raphaël Huser. His advising spans topics like Bayesian inference, environmental statistics, and extreme value analysis. Grants & Labs: Collaborates on projects involving climate science, statistical methodology, and data-driven risk assessment. Active in interdisciplinary teams at EPFL and international institutions.

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

• Precision Agriculture
• Remote Sensing
• Soil Fertility Management
• +۷ مورد دیگر

Abdul Munnaf is an Assistant Professor in Agricultural Biosystems Engineering at Wageningen University & Research, affiliated with the Plant Research International (PE&RC) institute. His work focuses on integrating advanced sensor technologies, machine learning, and geospatial analysis to improve precision agriculture practices. Key research areas include proximal sensors, soil fertility management, and crop growth modeling. He has developed novel sensing systems for real-time soil property measurement and mapping, with applications in nitrogen fertilization optimization and site-specific seeding strategies. Munnaf’s projects emphasize sustainability, such as improving bio-based fertilizer efficacy and reducing environmental impacts through data-driven approaches. His research outputs (29 articles) span topics like visible-near-infrared spectroscopy for soil analysis, robotic systems for agricultural automation, and machine learning models to predict soil organic carbon. He co-supervises PhD candidates in sensor technology and biofertilizer characterization. Collaboration networks include Belgium and China, reflecting his global research impact. Munnaf’s work bridges engineering and agronomy, addressing challenges in soil variability, crop productivity, and digital agriculture. Ongoing projects include developing biodegradable sensing technologies and evaluating spectral fusion techniques for soil attribute estimation.

• Remote Sensing
• Environmental Science
• Geographic Information Systems (GIS)
• +۳ مورد دیگر

dainius masiliunas is a researcher at the Laboratory of Geo-information Science and Remote Sensing at Wageningen University. He holds a PhD in remote sensing and focuses on land cover dynamics, time series analysis, and environmental monitoring using satellite data. His work includes developing algorithms like DRMAT and BFAST for detecting breakpoints in multispectral time series and global land cover change detection. Education: PhD in Geo-information Science and Remote Sensing, Wageningen University (2024) His research interests revolve around integrating advanced time-series techniques with remote sensing data to monitor environmental changes. He has contributed to projects like WorldCover (CGLOPS) and developed tools for land cover fraction mapping using Markov chains. Recent activities include delivering tutorials on time-series processing and presenting at international conferences like IEEE IGARSS. He co-created the CY-Bench dataset for crop yield forecasting and collaborated on the Diurnal UAV-based sun-induced chlorophyll fluorescence dataset. His current work emphasizes global-scale monitoring and improving algorithms for detecting land cover disturbances. dainius has led projects as principal investigator and contributed to initiatives like automated global land cover disturbance monitoring using BFAST Lite. His research bridges computational methods with ecological applications, aiming to support climate change and land management studies.

Yi Shenمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Processes
• Extreme Value Theory
• +۳ مورد دیگر

Yi Shen is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada. He holds affiliations with the Institute of Quantum Computing. His research focuses on applied probability, stochastic processes, and their symmetries, including stationarity, self-similarity, and exchangeability. He explores applications in extreme value theory, financial mathematics, quantum information, and statistical physics. Education: PhD in Operations Research (Cornell University, 2013) under Gennady Samorodnitsky; Ingenieur in Quantitative Economics and Finance (École Polytechnique, 2008); BS in Mathematics and Physics (Tsinghua University, 2005). Research emphasizes random locations of stochastic processes (e.g., supremum locations) and their connections to probabilistic symmetries. Recent work bridges probability with quantum mechanics, econometrics, and machine learning. His publications address topics like regression discontinuity designs, operator-scaling Gaussian fields, and quantum system Hamiltonian inference. Key contributions include analyzing self-similar processes, ergodic theory applications, and separability-entanglement classification in quantum systems. His work spans theoretical probability to applied domains like finance and quantum computing.

• Neural Data Science
• Systems Neuroscience
• Computational Neuroscience
• +۳ مورد دیگر

Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at NTNU's Department of Mathematical Sciences. Formerly, he held positions as a PhD researcher and postdoc at NTNU's Kavli Institute for Systems Neuroscience, with prior education including an MS in Computational Engineering from Purdue University and a BS in Applied Mathematics from the University of Connecticut. His career also includes industrial experience as a Computational Methods Engineer at Pratt & Whitney, UTC. His research focuses on integrating mathematical and computational approaches to understand neural systems, particularly spatial cognition, grid cells, and topological data analysis in neuroscience. Key contributions include work on toroidal representations in grid cells, posture coding in neocortex, and cohomological feature extraction from neural data. Publications emphasize multidisciplinary methods bridging algebraic topology, statistical physics, and neuroscience. Teaching includes courses like TMA4255 (Applied Statistics) and TMA4285 (Time Series). Outreach activities highlight engagement in scientific communication, including media interviews about posture-coding neurons and grid cell research.

Wujun Siمشاهده پروفایل

دانشیار

• Reliability Engineering
• Degradation Modeling
• Statistical Monitoring
• +۲ مورد دیگر

Dr. Wujun Si is an Associate Professor in the Industrial, Systems and Manufacturing Engineering department at Wichita State University's College of Engineering. He holds a Ph.D. from Wayne State University and specializes in reliability engineering and predictive maintenance. His research integrates statistical modeling with industrial applications, focusing on degradation analysis, life prediction, and maintenance optimization using advanced techniques like deep reinforcement learning. Recent work explores applications in renewable energy systems and multi-component industrial systems. Dr. Si's publications demonstrate consistent focus on developing sophisticated statistical and machine learning approaches to solve complex reliability challenges across manufacturing, energy, and materials domains.

Laurent Decreusefondمشاهده پروفایل

استاد پژوهشی

• Stochastic Geometry
• Probability Theory
• Wireless Network Modeling
• +۳ مورد دیگر

Laurent Decreusefond is a Research Professor in Probability at the Department of Computer Science and Networks (INFRES), Networks, Mobility and Services (RMS) at Telecom Paris (part of Institut Polytechnique de Paris). He is affiliated with the Information Processing and Communication Laboratory (LTCI) and the Data, Intelligence and Graphs (DIG) team. His work bridges probability theory, stochastic geometry, and wireless network modeling. Research areas: Stochastic Geometry, Malliavin Calculus, Stein's Method, Wireless Sensor Networks, Determinantal Processes, and Extreme Value Theory. Recent articles focus on Stein's method applications, functional limit theorems, and wireless network optimization using stochastic models.

Brindha Karthikeyanمشاهده پروفایل

مدرس

• Groundwater Management
• Environmental Geosciences
• Water Resources Management
• +۶ مورد دیگر

Brindha Karthikeyan is an environmental geoscientist affiliated with the IHE Delft Institute for Water Education. She holds a PhD in Environmental Geosciences from Anna University, India, and completed her Habilitation in Hydrogeology at Freie Universität Berlin, Germany. Her research focuses on integrated water resources management, managed aquifer recharge (MAR), and groundwater quality assessment, with a strong emphasis on applied solutions for real-world environmental challenges. Post-doctoral fellowships at IWMI (Lao PDR) and the National University of Singapore. Experience in over 12 countries across Asia, Africa, and the Americas. Recipient of the Linnaeus Palme Fellowship and Associate Editor of the Hydrogeology Journal. Her research interests span hydrogeochemical processes, climate change impacts on water resources, and sustainable groundwater management. She has pioneered novel methods for assessing karst spring dynamics and fluoride contamination mitigation. Over 150+ publications reflect her expertise in interdisciplinary approaches combining GIS, machine learning, and field-based studies. Key contributions include floodwater harvesting strategies in the Ganges Basin and vulnerability assessments in coastal aquifers. Her work integrates socio-technical aspects of water governance, emphasizing capacity-building in Global South nations.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Statistics
• Kernel Smoothing Methods
• Density Estimation
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics in the Department of Mathematics at the University of Extremadura since 2022. Previously, he held roles at the University of Valladolid and other positions within Extremadura. His research focuses on nonparametric statistics, kernel smoothing methods for density estimation, and their applications in clustering. He is actively involved in initiatives like the NICDA Workshop and Statistics Reading Club. Education history details are not explicitly provided in the text. His research interests emphasize advanced statistical methodologies including density derivative estimation, nonparametric clustering, and applications in epidemiology and ecology. Notable contributions include work on Bayesian taut splines, mode-based symmetry estimation, and statistical modeling for animal home ranges. Recent publications (2020–2024) explore cutting-edge topics such as geodesic normal distributions, cross-validation techniques, and SIR modeling for pandemic data. His work bridges theoretical advancements with practical applications in health, ecology, and machine learning. Awards and grants are not explicitly mentioned. Chacón has advised no listed students but collaborates on interdisciplinary projects, including the special issue on Data Science for COVID-19. His research lab focuses on multivariate kernel smoothing and nonparametric methods.

• Approximation Theory
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Feng Dai is affiliated with Xidian University's National Laboratory of Radar Signal Processing in China. His research spans approximation theory, machine learning, computer vision, and optimization algorithms. He has collaborated extensively with institutions like the National Laboratory and co-authored over 140 publications since 2002. Key research interests include polynomial approximation on multivariate domains, deep learning for computer vision tasks (e.g., object detection, semantic segmentation), and optimization techniques for engineering systems. His work bridges mathematical theory with practical applications in signal processing and imaging systems. Recent publications focus on advancing polynomial mesh theory, developing algorithms for panoramic imaging, and improving federated learning frameworks for IoT applications. Notable contributions include work on universal discretization methods and boundary handling in oriented object detection. His interdisciplinary approach integrates computational mathematics with modern AI techniques, addressing challenges in both theoretical and applied domains such as autonomous systems and medical imaging.