جست‌وجوی استاد و پژوهشگر

Jingru Muمشاهده پروفایل

استادیار

• Nonparametric Statistics
• Spatio-temporal Analysis
• Large-scale Data Mining
• +۲ مورد دیگر

Dr. Jingru Mu is an Assistant Professor of Statistics at Kansas State University. Her methodological research develops statistical tools for complex datasets, with applications in geospatial analysis and computational imaging. Research specialties: Semi-parametric modeling of spatio-temporal dependencies High-dimensional variable selection techniques Statistical computing for large-scale datasets Image feature extraction algorithms Holds Ph.D. in Statistics from Iowa State University (2019). Teaches mathematical statistics, statistical inference, and time series analysis. Research bridges theoretical statistics with applications in environmental monitoring and computer vision.

Joshua D. Patrickمشاهده پروفایل

مدرس ارشد

• Statistical Modeling
• Environmental Statistics
• Applied Statistics
• +۲ مورد دیگر

Dr. Joshua D. Patrick is a Senior Lecturer in the Department of Statistical Science at Baylor University. He holds a Ph.D. in Statistics from Baylor University (2013), an M.S. in Mathematical Sciences from the University of West Florida (2009), and a B.S. in Computational and Applied Mathematics from the same institution (2007). His research focuses on spatio-temporal statistical modeling, particularly in environmental applications. He completed a postdoctoral fellowship at the University of California, Davis, and returned to Baylor to teach. His work emphasizes applying statistical methods to real-world problems, such as solar irradiance analysis. Education: Ph.D., Statistics, Baylor University, 2013 M.S., Mathematical Sciences, University of West Florida, 2009 B.S., Computational and Applied Mathematics, University of West Florida, 2007 Research Interests: Spatio-temporal modeling, environmental statistics, and statistical methodologies for renewable energy data. His 2016 publication in Renewable Energy highlights his expertise in applying advanced statistical techniques to solar irradiance analysis. Dr. Patrick’s academic journey reflects a deep passion for mathematics and its practical applications. He and his wife, Becky, are both Baylor alumni and actively involved in the Baylor community.

Joon Jin Songمشاهده پروفایل

استاد

• Statistical Science
• Bayesian Analysis
• Spatial Statistics
• +۴ مورد دیگر

Dr. Joon Jin Song is a Professor of Statistical Science at Baylor University. His work focuses on Bayesian methods, spatial statistics, and their applications in environmental science, biostatistics, and public health. He holds a Ph.D. from Texas A&M University (2004), an M.S. from Kyungpook National University (1999), and a B.S. from Yeungnam University (1997). His research integrates advanced statistical techniques to address challenges in spatial data analysis, including rainfall estimation, disease mapping, and environmental monitoring. Notable projects include Bayesian correction for misclassification in count data and radar-rain gauge merging for high-resolution precipitation estimation. He has also contributed to studies on crime fear reduction through spatio-temporal modeling and public health surveillance during the COVID-19 pandemic. Dr. Song's publications span computational statistics, environmental modeling, and health informatics. His methodologies emphasize robustness to measurement error, high-dimensional data, and spatial dependencies. Collaborations include interdisciplinary work in meteorology, epidemiology, and bioinformatics.

Yasemin Irvin-Ericksonمشاهده پروفایل

استادیار

• Violence Against Women
• Domestic Violence
• Identity Fraud Victimization
• +۶ مورد دیگر

Dr. Yasemin Irvin-Erickson is an Assistant Professor in the Department of Criminology, Law and Society at George Mason University. Her research focuses on victimization, technology, and economic empowerment of vulnerable populations, with particular emphasis on domestic violence, identity fraud, and gender-based violence. She has managed over $3 million in research funding, including NSF and BJA grants, and led innovative projects such as a mobile survey application to combat transit violence. Education: Ph.D. in Criminal Justice (Rutgers University, 2014), M.A. in Criminal Justice (Rutgers, 2010), M.A. in Forensic Science (Istanbul University, 2009), B.A. in Sociology (Middle East Technical University, 2004). Research Interests: Violence Against Women Domestic Violence Patterns Identity Fraud Victimization Public Transit Safety Economic Impacts of Crime Victim Reporting Barriers Grants & Projects: Key roles in over $10 million of externally funded projects, including co-PI for a $1.48M BJA project on policing mental health, and PI for a $100K World Bank innovation grant addressing transit violence. Awards: Development Marketplace Innovation Award (2017), National Institute of Justice Fellowship (2012–2014), and multiple academic fellowships. Labs/Teams: Active in the Center for Victim Research (OVC-funded) and collaborates with institutions like the Urban Institute and John Jay College.

Nicolas Gutehrléمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Information Extraction
• Information Retrieval
• +۴ مورد دیگر

Nicolas Gutehrlé is a Post-doctoral researcher at the ANR InSciM project (2024–Present), focusing on modeling uncertainty in scientific articles across disciplines. He also serves as a Teaching Assistant at Université de Bourgogne and Université de Franche-Comté, instructing courses in Computational Linguistics, Natural Language Processing, and related fields. His research interests include Digital Humanities, Information Extraction, and Knowledge Management, with a focus on semantic annotation and archival document analysis. **Research Projects** ANR InSciM : Developing linguistic models to identify uncertainty expressions in scientific papers (SSH/STM disciplines). EMONTAL Project (PhD): Automated processing of heterogeneous archival collections for heritage enhancement via NLP and discourse analysis. **Teaching**: Delivers courses in Computational Linguistics, Phonetic Modeling, Cognitive Methods, and Software Engineering for NLP across master’s programs. Guides workshops on LaTeX and scientific writing. **Popularization**: Actively participates in events like European Researchers’ Night and Science Festival, promoting understanding of historical document analysis and semantic technologies through articles and public engagement.

Timothée Poisotمشاهده پروفایل

استاد

• Computational Ecology
• Biotic Interactions
• Interaction Networks
• +۱ مورد دیگر

Timothée Poisot is Professor in the Department of Biological Sciences at Université de Montréal and Training Director of BIOS². His research examines spatio-temporal variation in biotic interactions, developing computational methods to predict species interaction networks for conservation and infectious disease management. Research integrates community ecology, coevolution, and biostatistics to model ecosystem properties. Focus areas include predicting interaction structures where empirical data is limited, and applying ecological network analysis to conservation challenges including emerging infectious risks.

David Hitchcockمشاهده پروفایل

استاد

• Functional Data Analysis
• Cluster Analysis
• Environmental Data Analysis
• +۵ مورد دیگر

David Hitchcock is a Professor of Statistics at the University of South Carolina, serving as Director of the Graduate Program in the Department of Statistics within the McCausland College of Arts and Sciences. He earned his B.S. from the University of Georgia, M.S. from Clemson University, and Ph.D. from the University of Florida. His research focuses on functional data analysis, cluster analysis, and environmental applications, including spatio-temporal modeling of precipitation, medical skill assessment, and wildfire data analysis. Education: Bachelor’s Degree: University of Georgia Master’s Degree: Clemson University (Mathematical Sciences) Ph.D.: University of Florida (Statistics) Research Interests: Hitchcock’s work spans functional data analysis, cluster analysis, and environmental statistics. Recent projects include: Simultaneous registration and clustering of functional data in medical skill evaluation. Spatio-temporal models for flood dynamics and precipitation patterns. Applications in loggerhead turtle population estimation and mass spectrometry. Collaborations: He collaborates with researchers like John Grego (flooding data), Ian Dryden (proteomics), and S.Z. Samadi (environmental engineering). His team has developed influential methods in functional regression and influential curve detection. Teaching: Hitchcock teaches advanced statistics courses such as Bayesian Data Analysis, Multivariate Statistics, and Time Series Forecasting. Recent courses include STAT 542 (Computing for Data Science) and STAT 530 (Applied Multivariate Analysis). Student Advising: Guided PhD students in topics like functional clustering, stock price prediction, and music genre analysis. Notable advisees include Zizhen Wu (functional registration), Shan Zhong (deep learning ensembles), and Aimée Petitbon (multinomial time series).

Emmanuel Ifeachorمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Professor Emmanuel Ifeachor holds the position of Research Professor at the School of Engineering, Computing and Mathematics, University of Plymouth (Faculty of Science and Engineering). His work spans biomedical engineering, telecommunications, and artificial intelligence, contributing to UN Sustainable Development Goals (SDGs) related to health and well-being. His research focuses on machine learning applications in healthcare diagnostics (e.g., Alzheimer’s, Parkinson’s, autism), signal processing for biomedical signals, and Quality of Experience (QoE) in multimedia networks. Notable projects include LiveWell, promoting Parkinson’s patient wellness via ICT, and development of blood-based Alzheimer’s biomarkers. Recent work emphasizes explainable AI for medical diagnostics, stroke risk prediction using routine data, and AI-driven MRI analysis for neurodegenerative diseases. He has supervised over 20 PhD students in areas like network QoE optimization, EEG-based biomarkers, and medical AI systems. His contributions include pioneering frameworks for bioprofile analysis over grids, VoIP quality prediction, and SDN/NFV-based network management. Collaborative projects address clinical alarm systems in ICUs and crowdsourced video QoE evaluation methodologies.

Bin Yangمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Machine Learning
• +۹ مورد دیگر

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Hong Huangمشاهده پروفایل

دانشیار

• Information Science
• Bioinformatics
• Library and Information Technology
• +۴ مورد دیگر

Dr. Hong Huang is an Associate Professor in the School of Information at the University of South Florida (USF), part of the College of Arts and Sciences. He holds a Ph.D. in Information from Florida State University, complemented by M.S. degrees in Computer Science (FSU) and Plant Genetics (Florida A&M University), and a B.S. in Biochemistry from Sun Yatsen University. His research bridges information science, bioinformatics, and educational technology, focusing on data management, AI/ML applications, and health informatics. He has secured federal grants (e.g., USDA) and published over 140 peer-reviewed works. His research interests include genomic data curation, IT in libraries/education, and bio/health information systems. Notable contributions include developing the Vitigene database and MASTtreedist software. Huang serves on editorial boards for Library & Information Science Research and Journal of Information and Learning Sciences. His awards include the Lewis-Marxsen Scholarship and recognition for student presentations. He teaches courses like Data Analytics, Health IT, and Scholarly Communication. Grants include collaborative projects on data management and learning science. Advising focuses on interdisciplinary projects involving students in bioinformatics and information systems.

Genevieve Reidمشاهده پروفایل

استادیار

• Critical GIS
• Indigenous data sovereignty
• Geospatial ontologies
• +۳ مورد دیگر

Dr. Genevieve Reid is Assistant Professor of Geography in the Department of Global & Sociocultural Studies at Florida International University. Her research examines geospatial technologies through critical, feminist, and Indigenous frameworks. Research investigates geospatial ontologies, participatory GIS, Indigenous data sovereignty, and spatial stigma. She collaborates with Eastern Cree communities in Northern Quebec on land documentation, cultural preservation, and legal advocacy through community-based mapping. Publications critically analyze how mapping technologies reproduce power dynamics, proposing decolonial alternatives for spatial knowledge representation. Her work connects technical geospatial research with Indigenous self-determination movements. Dr. Reid teaches cartography, GIS applications, critical GIScience, and spatial research methods. She trains students in ethical geospatial practice and community-engaged research methodologies.

Kristen Schellمشاهده پروفایل

پژوهشگر

• Large scale optimization
• Applied statistics
• Machine learning
• +۶ مورد دیگر

Kristen Schell is a Research Scientist in the Industrial and Systems Engineering Department at Rensselaer Polytechnic Institute, part of the School of Engineering. Her work focuses on mathematical models for energy transition, combining wind energy forecasting, microgrid design, and spatio-temporal statistical methods. She holds dual doctoral degrees in Engineering and Public Policy from Carnegie Mellon University and the University of Porto, alongside a Master's in Environmental Engineering from Johns Hopkins University and a Bachelor's in Chemical Engineering from Carnegie Mellon University. Education: PhD in Engineering and Public Policy, Carnegie Mellon University PhD in Faculty of Engineering, University of Porto MS in Environmental Engineering, Johns Hopkins University BS in Chemical Engineering & Engineering and Public Policy, Carnegie Mellon University Her research interests span large-scale optimization, applied statistics, machine learning, renewable energy systems, and energy policy. She develops models informed by empirical data from utilities, weather predictions, and energy markets to address climate resilience and energy infrastructure challenges. Dr. Schell is actively mentoring PhD students in optimization modeling, statistical methods, and machine learning applications in energy systems. Her work bridges technical and policy dimensions of sustainable energy transitions.

Arcangela Ragoمشاهده پروفایل

استادیار

• Telecommunications
• Machine Learning
• 6G Systems
• +۱ مورد دیگر

Arcangela Rago is a Tenure Track Assistant Professor at the Department of Electrical and Information Engineering, Polytechnic University of Bari. She specializes in telecommunications engineering with a focus on 6G systems, non-terrestrial networks, and AI integration. Education: Obtained M.Sc. in Telecommunications Engineering (with honors) and Ph.D. in Electrical and Information Engineering from Polytechnic University of Bari. Research Interests: Her work explores the integration of terrestrial and non-terrestrial networks, 6G communication systems, and machine learning applications in telecommunications. Current projects focus on AI-driven network optimization, drone-assisted emergency systems, and sustainable network architectures. Publication Trends: Recent articles demonstrate strong focus on 6G network integration, AI applications in telecommunications, and hybrid terrestrial-satellite systems. Research shows increasing emphasis on practical implementations for smart agriculture, disaster management, and multimedia services. Awards: Best Workshop Paper at ICT-DM'23 (2023) Best Workshop Paper at ISCC: NGMSE2024 (2024)

Annunziata D'Aversaمشاهده پروفایل

پژوهشگر ارشد

• Time Series Forecasting
• Nowcasting
• Big Data Analytics
• +۱ مورد دیگر

Annunziata D'Aversa is a Research Fellow and PhD student in the Department of Computer Science at the University of Bari Aldo Moro, specializing in energy sector forecasting models. Her work focuses on developing incremental update methodologies for time-sensitive data streams in electricity networks and renewable energy systems. She earned her Master's degree in Data Science from the University of Bari Aldo Moro in July 2021 with full marks and honors, completing a thesis on spatial autocorrelation modeling for energy consumer forecasting in electricity networks. Her research interests include Time Series Forecasting, Nowcasting, Big Data Analytics, and Neural Networks, with emphasis on spatial-temporal pattern recognition for energy applications. She develops adaptive models that process real-time data streams for grid management and wind power optimization. Her 2022-2024 publications demonstrate consistent innovation in spatio-temporal modeling, applying model trees and linear architectures to multi-step forecasting challenges across smart grids and wind power plants. Key contributions include spatially-aware nowcasting techniques and autocorrelation leverage for consumption prediction.

Stefano Polimenaمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Stefano Polimena is a Research Fellow at the Department of Computer Science, University of Bari Aldo Moro. He earned his Bachelor's degree in Data Science with full marks and honors in July 2021. Research Interests: Computer Vision for agro-food quality control Deep Learning and Neural Networks Big Data Analytics and Data Mining Energy Forecasting in Smart Grids Publications Trends: Stefano's work focuses on applying machine learning to food quality assessment and energy consumption forecasting. His research integrates computer vision with regression models for agriculture, and leverages spatio-temporal data mining for smart grid analytics.