Find Academic Supervisors

• Geomorphology
• Sediment Dynamics
• Mountainous Environments
• +3 more

François Clapuyt is a FNRS postdoctoral researcher specialized in geomorphic response quantification and sediment transfer dynamics in landslide-prone mountainous environments. His work combines advanced technologies like UAV-SfM photogrammetry and cosmogenic radionuclide dating to study spatio-temporal sediment patterns. Affiliation: Belgian National Fund for Scientific Research (FNRS) Research Focus: Clapuyt's work examines the interplay between stochastic mass movement processes and long-term denudation rates, with particular emphasis on sediment connectivity mechanisms in Alpine and Andean catchments. He develops methodologies using precision geospatial tools to track landscape evolution across multiple timescales. Recent Publications: His research output spans interdisciplinary topics including UAV-based topographic monitoring, cosmogenic nuclide applications, and sediment dynamics modeling across diverse mountainous regions. Key Themes: Sediment flux quantification 3D geomorphic reconstructions Catchment-scale denudation analysis Temporal scaling of erosion processes Landslide sediment cascades PPK-enhanced UAV mapping

• Responsible AI
• Computer Vision
• Computer Graphics
• +5 more

Prof. Hubert P. H. Shum is a Professor of Visual Computing and Director of Research in the Department of Computer Science at Durham University. He specializes in Responsible AI, Computer Vision, and AI in Healthcare. As a Co-Founder of the Durham University Space Research Centre and Fellow of the Wolfson Research Institute for Health and Wellbeing, he leads interdisciplinary research in healthcare, space technology, and autonomous systems. His work includes over 200 publications in top venues like CVPR, ICCV, and MICCAI, focusing on spatio-temporal data modeling. He has secured £10M+ in grants from EPSRC, MoD, and Innovate UK, supervising over 30 PhD students. Key projects include NortHFutures (digital health hub) and counter-drone research. Research interests span AI ethics, medical imaging, autonomous vehicles, and space applications. His lab develops technologies for surgical workflow anticipation, LiDAR segmentation, and human-AI interaction. Notable awards include Best Paper Awards in CVPR and exceptional teaching/supervision accolades from Durham University. Key Grants : EPSRC Impact Acceleration (£2.76M), EPSRC Digital Health Hub (£4.17M), Royal Society (£143k). Professional Activities : Conference chairs for Pacific Graphics, BMVC; editorial roles in Computer Graphics Forum and IJCV. Labs/Initiatives : Durham University Space Research Centre, Centre for Visual Arts and Culture.

Anh Vu VoView profile

Assistant Professor

• LiDAR
• Urban Science
• Spatial Data Management
• +4 more

Dr. Anh Vu Vo is an Assistant Professor at University College Dublin's School of Computer Science, specializing in high-scalability algorithms for LiDAR and urban spatial datasets. He holds a PhD from UCD, a Master's from the University of Melbourne, and a Bachelor's from HCMC University of Architecture. Roles : Lecturer/Assistant Professor since 2024, Postdoctoral Research Fellow (2019–2022), Research Scientist at NYU (2018–2019). Research Interests : LiDAR, Urban Science, Spatial Data Management, Distributed Computing, and Big Data. Key projects include CAMEO (Earth Observation platform), UrbanARK (flood risk assessment), and AIMVIE (mangrove mapping). He has authored over 30 peer-reviewed publications and secured grants such as the SFI Future Innovator Prize. Awards : 2022 EODisrupt Winner, 2015 IEEE GRSS Data Fusion Contest First Prize, Australian Endeavour Award. Teaching : Spatial Information Systems, Urban Sensing, and graduate-level courses. His work focuses on integrating LiDAR data with advanced computing frameworks to address urban challenges like flood risk and infrastructure planning.

Seda Salap-AycaView profile

Assistant Professor

• GIS
• Spatial Decision Making
• Uncertainty Analysis
• +4 more

Seda Salap-Ayca is an Assistant Professor of the Practice in Earth, Environmental, and Planetary Sciences at Brown University, affiliated with the Data Science Institute. Her research focuses on spatial decision-making, uncertainty in spatial models, and human-environment interactions. She holds a PhD in Geography from UC Santa Barbara and San Diego State University. Previously, she taught GIS courses at UMass Amherst. Education: PhD in Geography (2018), Joint Program, UC Santa Barbara & San Diego State University Research Interests: GIScience applications in environmental decision-making Spatial uncertainty and sensitivity analysis in land-use models GIS-based vulnerability mapping for natural hazards Integration of multi-criteria evaluation with spatial analysis Teaching: EEPS 1320: Introduction to GIS for Environmental Applications ENVS 1107: Cartography and Geovisualization Her recent work explores collaborative flood hazard mapping with communities, heat vulnerability models, and innovative GIS visualization techniques for spatial sensitivity analysis.

James E. FowlerView profile

Professor

• Hyperspectral Imagery Analysis
• Image and Video Coding
• Compressed Sensing
• +3 more

James E. Fowler is the William L. Giles Distinguished Professor and holder of the Billie J. Ball Endowed Professorship in the Department of Electrical & Computer Engineering at Mississippi State University. As of January 2022, he serves as a Program Director in the Communications and Information Foundations (CIF) cluster at the National Science Foundation (NSF). His primary affiliations include Mississippi State University and NSF, with visiting roles at Télécom ParisTech and Polytech Nantes. He earned his B.S., M.S., and Ph.D. in Electrical Engineering from The Ohio State University. Dr. Fowler's research focuses on hyperspectral imagery analysis, compressed sensing, and image/video coding. His work integrates signal processing, machine learning, and computational imaging. Notable contributions include innovations in hyperspectral compression, random projections, and deep learning frameworks for remote sensing applications. Education: Ph.D., Electrical Engineering, The Ohio State University, 1996 M.S., Electrical Engineering, The Ohio State University, 1992 B.S., Computer and Information Science Engineering, The Ohio State University, 1990 Awards & Roles: Fellow of the IEEE Editor-in-Chief of IEEE Signal Processing Letters (2017–2019) General Co-Chair of the Data Compression Conference and IEEE International Conference on Image Processing Research Labs: High Performance Computing Collaboratory (HPC2) at Mississippi State University.

Jingru MuView profile

Assistant Professor

• Nonparametric Statistics
• Spatio-temporal Analysis
• Large-scale Data Mining
• +2 more

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. PatrickView profile

Senior Lecturer

• Statistical Modeling
• Environmental Statistics
• Applied Statistics
• +2 more

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 SongView profile

Professor

• Statistical Science
• Bayesian Analysis
• Spatial Statistics
• +4 more

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-EricksonView profile

Assistant Professor

• Violence Against Women
• Domestic Violence
• Identity Fraud Victimization
• +6 more

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 GutehrléView profile

Researcher

• Digital Humanities
• Information Extraction
• Information Retrieval
• +4 more

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 PoisotView profile

Professor

• Computational Ecology
• Biotic Interactions
• Interaction Networks
• +1 more

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 HitchcockView profile

Professor

• Functional Data Analysis
• Cluster Analysis
• Environmental Data Analysis
• +5 more

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 IfeachorView profile

Research Professor

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +3 more

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 YangView profile

Professor

• Computer Science
• Data Management
• Machine Learning
• +9 more

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 HuangView profile

Associate Professor

• Information Science
• Bioinformatics
• Library and Information Technology
• +4 more

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.