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Moataz MohamedView profile

Associate Professor

• Transportation Engineering
• Sustainable Transportation
• Electrification of Transit Systems
• +5 more

Dr. Moataz Mohamed is an Associate Professor of Civil Engineering at McMaster University and Director of the McMaster Institute for Transportation & Logistics (MITL). His research focuses on systemic evaluation of transportation networks to achieve sustainable and resilient systems, emphasizing electric and autonomous technologies. He holds the Joseph Ip Distinguished Engineering Fellowship. Education: B.Eng. (Assiut University, Egypt), M.A.Sc. (University of Rome), Ph.D. (University of Ulster). Awarded prestigious honors including Runner-Up Smeed Prize (2011), IEEE ITEC Best Paper (2020), and Ron Rice Award (2021). Research Interests: Electric transit systems, UAV logistics, energy-grid interactions, multimodal accessibility, and policy impact. Grants & Projects: SSHRC-funded transit infrastructure study, NRCan-funded E-Bus Energy Simulator (ZEVAI project), and collaboration with University of Liverpool on decarbonizing port cities. Teaches courses like CIV ENG 6T04 (Transit Modelling) and CIV ENG 745 (Multivariate Data Methods). Leads initiatives to accelerate ZEV adoption in trucking and public transit sectors.

Sanjay ChaudhuriView profile

Associate Professor

• Empirical Likelihood
• Analysis of Complex Survey Data
• Statistical Data Integration
• +7 more

Sanjay Chaudhuri is an Associate Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL). He previously held positions at the National University of Singapore (2005–2023) and earned his Ph.D. from the University of Washington (2005), under Professors Michael D. Perlman and Thomas S. Richardson. His research focuses on empirical likelihood, Bayesian methods, high-dimensional data analysis, and applications in demography, network data, and machine learning. Education: B.Stat and M.Stat from the Indian Statistical Institute (1998–2000), Ph.D. in Statistics from the University of Washington (2005). Research Interests: Empirical likelihood, statistical data integration, Bayesian empirical likelihood, graphical Markov models, covariance estimation, causality, approximate Bayesian computation (ABC), and applications in natural, engineering, and marketing sciences. Teaching: Advanced Topics in Applied Statistics, Statistical Models, Applied Regression Analysis, Design of Experiments, and courses on graphical models, data science, and linear models. Software Contributions: Developer of R packages glmc (combining sample/population data), ES (edge selection), elhmc (Hamiltonian Monte Carlo sampling), and Anhysnuc (analyzing nucleic acid thermal curves). Grants & Collaborations: Active in interdisciplinary projects involving statistical methods for complex data, including pandemic dynamics, thermal curve analysis, and environmental statistics. Labs/Teams: Leads the development of Anhysnuc , a web-based tool for analyzing irreversible thermal curves from nucleic acid hybridization experiments.

Tehran DavisView profile

Assistant Professor

• Perceptual-Motor Processes
• Multi-Agent Coordination
• Goal-Directed Action
• +3 more

Tehran Davis is an Assistant Professor of Psychology at the University of Cincinnati (since 2017) and previously held the same position at the University of Connecticut (2012–2017). His research focuses on perceptual and motor processes underlying goal-directed action, with a specific emphasis on multi-agent coordination, ranging from dyadic interactions to large-scale team behaviors. He holds a Ph.D., MA, and BA in Psychology from the University of Cincinnati (2012, 2009, 2005). His research interests include the coordination of interpersonal actions, postural control in motor tasks, and the application of dynamical systems theory to understand behavioral transitions. He has explored topics like affordance perception, joint action dynamics, and the role of attention in motor tasks, often employing advanced analytical methods like cross-recurrence quantification analysis. Dr. Davis has secured internal grants totaling over $16,000: a 2019–2020 collaborative grant ($10,000) for advanced human performance studies and a 2020–2021 grant as Principal Investigator ($6,000) for multi-level analysis of multi-agent interactions. His work bridges cognitive, motor, and social psychology, with applications to rehabilitation, sports science, and developmental disabilities.

• Statistics
• Probability
• Multivariate Geostatistical Modeling
• +6 more

Marco Minozzo is an Associate Professor of Statistics at the University of Verona , affiliated with the Department of Economics within the School of Economics and Management. Previously, he served as a Researcher in Statistics at the University of Perugia. His academic credentials include a degree in Statistics and Economics from the University of Padova and a Ph.D. in Statistics from University College London. Research Focus : Modeling multivariate geostatistical data, Monte Carlo estimation algorithms, spatial and temporal statistical models, and applications in economics, finance, and environmental science. Teaching : Statistics and Probability at undergraduate, graduate, and postgraduate levels, including courses on data analysis with R, Python, MATLAB, and SAS. Third Mission : Active in public engagement and continuous education, with teaching assignments in professional master’s programs and specialized courses. Research Projects : FAIR-PLAY (marketing 4.0), Management Journal Relevance, Monte Carlo methods for geostatistics, and high-dimensional dependency models.

• Business and Management
• Applied Mathematics
• Statistics
• +3 more

Dr. Nuo Xu is an Associate Professor at the Collat School of Business, University of Alabama at Birmingham (UAB), within the Department of Management, Information Systems and Quantitative Methods. He holds a PhD (2007) and MS (2003) in Industrial Engineering from the University of Cincinnati, and a BS in Material Engineering from Shanghai Jiao-Tong University (1997). His research interests span Business Analytics, Applied Mathematics, Statistics, and Manufacturing Engineering. Notable contributions include methodologies in gene expression analysis, feature selection bias reduction, and robust voting machine allocation models. Professional activities include editorial roles for journals like International Journal of Data Science and service on institutional committees such as the IS Search Committee. His academic career at UAB began in 2010, initially in the School of Engineering before transitioning to his current role in the Collat School. He advises students in business honors programs and actively participates in institutional service roles.

Andrea PastoreView profile

Associate Professor

• Environmental Statistics
• Biostatistics
• Econometrics
• +3 more

Andrea Pastore is an Associate Professor at the Department of Economics of Ca' Foscari University of Venice. His research focuses on statistical methodologies applied to environmental science, public health, and econometrics. He collaborates on projects like SELECT (Studio sulla Longevità) and has expertise in multivariate data analysis, health workforce forecasting, and risk analysis. Teaching responsibilities include courses on statistics for undergraduate and graduate programs, such as 'Statistica - 1/2' and 'R Coding and Finance Analytics'. He supervises theses on statistical topics and contributes to research funded by institutions like Regione del Veneto and PRIN. Key research areas include epidemiological forecasting, environmental data modeling, and statistical methods for healthcare policy. His work integrates quantitative approaches to address challenges in public health systems, industrial sectors, and non-profit governance.

Robert JungView profile

Professor

• Financial Econometrics
• Discrete-Valued Time Series
• Computational Statistics
• +2 more

Robert Jung is a Professor at the University of Hohenheim , affiliated with the Department of Econometrics and Economic Statistics under the Faculty of Business, Economics and Social Sciences . He joined the university in 2012 after holding professorships at the University of Erfurt and completing his postdoctoral degree at the University of Tuebingen. His current role includes serving as Vice-Dean for Research since 2014. Universities: University of Hohenheim, University of Erfurt, University of Tuebingen Departments: Econometrics and Economic Statistics, Computational Science Lab Research Interests span financial econometrics, discrete-valued time series, computational statistics, health econometrics, and spatial econometrics. His work focuses on modeling integer time series, volatility spillovers, and market interdependencies. Publication Trends include contributions to financial market analysis (2017-2006), discrete time series modeling (2015-2003), and cross-market dynamics (2014-1996). Key topics involve futures speculation, count data diagnostics, and stochastic volatility frameworks. Leadership Roles : Head of Department of Econometrics and Economic Statistics Speaker of Computational Science Lab Member of Faculty Board of Economics and Social Sciences

William W.S. WeiView profile

Professor

• Time Series Analysis
• Forecasting Methods
• Statistical Modeling
• +5 more

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
• +5 more

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 PreraView profile

Associate Professor

• Environmental and Natural Resources Economics
• International Development Economics
• Public Policy Analysis
• +3 more

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 PaquolaView profile

Researcher

• Neurodevelopment
• Neuroimaging
• Computational Neuroscience
• +3 more

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
• +3 more

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
• +7 more

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
• +7 more

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)
• +3 more

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.