جست‌وجوی استاد و پژوهشگر

Stefano Castruccioمشاهده پروفایل

دانشیار

• Environmental Statistics
• Machine Learning
• Spatio-temporal Statistics
• +۷ مورد دیگر

Stefano Castruccio is a Notre Dame Collegiate Associate Professor at the University of Notre Dame, specializing in environmental statistics and spatio-temporal modeling since joining in 2017. His research bridges statistical methodology with critical environmental applications including renewable energy assessment and air pollution impact analysis. His educational background includes: Ph.D. in Statistics from the University of Chicago (2013) M.S. in Mathematical Engineering from Politecnico di Milano, Italy (2007) B.S. in Mathematical Engineering from Politecnico di Milano, Italy (2005) Prof. Castruccio's research centers on developing computationally efficient spatio-temporal models for environmental systems, with major contributions in wind energy resource assessment and climate modeling. His methodological innovations integrate physics-informed machine learning with traditional statistical frameworks to address challenges in high-resolution data analysis and uncertainty quantification. His recent publications (2018-2025) reveal a strategic evolution toward deep learning integration for wind modeling and physics-constrained statistical frameworks, significantly advancing computational approaches for renewable energy forecasting and environmental risk assessment. His scientific recognition includes: Elected fellow of the American Statistical Association Elected member of the International Statistical Institute 2024 Gordon Bell Prize for Climate Modelling 2023 TIES President's invited lecture 2021 TIES Abdel El-Shaarawi Early Investigator Award 2020 Early Investigator Award from ASA Section on Statistics and the Environment Research funding includes major grants from NSF, NASA, and KAUST, most recently a $240,000 NSF CDS&E-MSS award for physics-informed forecasting of high-resolution spatio-temporal data. He maintains active international collaborations with Politecnico di Milano and KAUST through his research group's GitHub repository. His team focuses on open-source development of statistical tools for environmental applications, with ongoing projects in turbulent boundary layer modeling and high-resolution wind energy forecasting.

Daniela Beisserمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Microbial Ecology
• +۶ مورد دیگر

Prof. Dr. Daniela Beisser is a Professor at the Department of Engineering and Natural Sciences (FB 8) of the Westphalian University of Applied Sciences in Recklinghausen, Germany. Her research focuses on bioinformatics and biostatistical methods for high-throughput 'omics data, applied to biomedicine and freshwater ecology. She previously held academic roles at the University of Duisburg-Essen (2017–2023) and University Hospital Essen. 2004–2008: B.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2006–2008: M.Sc. in Molecular Biology with Bioinformatics focus, FH Gelsenkirchen 2008–2011: Ph.D. in Bioinformatics, University of Würzburg Her research integrates computational approaches with experimental data to study molecular responses to environmental stressors in freshwater organisms, genome analyses in human and protists, and proteomic studies in plants. She also investigates eco-evolutionary theories in microorganisms and links biodiversity to ecosystem functions. Recent publications highlight her work on amplicon sequencing (Natrix2 pipeline), metatranscriptomic analysis of microbial communities, and machine learning frameworks for environmental data. She contributes to software tools like TaxMapper and BioNet for reproducible workflows. Best Poster Award, German Conference on Bioinformatics (2013) Travel scholarships: DAAD, DAAD PROMOS, German Symposium on Systems Biology E-fellows.net scholarship (2006–2008) She has supervised numerous PhD, Master’s, and Bachelor’s students on topics such as protist community dynamics , fungal degradation processes , and stressor recovery mechanisms . Her lab collaborates on the CRC 1439 'RESIST' project and develops tools for environmental DNA analysis.

• Bayesian statistics
• robust estimation
• information-assisted hypothesis testing
• +۳ مورد دیگر

Jordan Bryan is an Assistant Professor of Data Science at the University of Virginia's School of Data Science, specializing in multivariate statistical methods with applications spanning environmental monitoring, high-energy physics, and cancer genomics. He earned his Ph.D. in Statistics from Duke University in 2023 and a B.S. in Mathematics from Stanford University, and currently serves as Secretary of the junior section of the International Society for Bayesian Analysis. Education: Ph.D. in Statistics, Duke University B.S. in Mathematics, Stanford University Research Interests: Dr. Bryan develops advanced methodologies in Bayesian statistics , robust estimation , and information-assisted hypothesis testing , with particular emphasis on integrating auxiliary data sources to enhance statistical power. His work bridges theoretical rigor with real-world applications in genomics, environmental systems, and physics, recently expanding to incorporate large language model-derived information for genomic analysis. Publication Trends: Analysis of his 15 most recent publications (2020-2025) reveals a dual trajectory: foundational contributions to statistical methodology (e.g., multirank likelihood frameworks, subscedastic estimation) and high-impact applications in cancer genomics (functional screens, drug repurposing) and environmental science (source apportionment). A notable 2025 publication pioneers LLM integration for genomic hypothesis testing, signaling a strategic expansion into AI-augmented statistics. Scientific Awards: No specific awards, prizes, or fellowships were documented in the source materials. Advising and Grants: Supported by National Institute of Environmental Health Sciences (NIEHS) and National Heart Lung and Blood Institute (NHLBI) training grants during his UNC Chapel Hill postdoctoral fellowship, Dr. Bryan now leads independent research at UVA. While formal student advising isn't detailed in current records, his extensive publication record suggests active mentorship in collaborative projects.

Jeffrey G. Baguleyمشاهده پروفایل

استاد آموزشی

• Marine Meiobenthic Ecology
• Deep-Sea Ecology
• Polar Ecology
• +۳ مورد دیگر

Jeffrey G. Baguley is a Teaching Professor in the Department of Biology at the University of Nevada . Holding a Ph.D. in Marine Science from the University of Texas at Austin and a B.A. in Biology from Linfield College, he has conducted postdoctoral research at the Baruch Institute for Marine and Coastal Science. Education : B.A. in Biology, Linfield College (2000) Ph.D. in Marine Science, University of Texas at Austin (2004) Research Focus : Baguley's work examines marine meiobenthic ecology across three key areas: Deep-sea ecology: Biodiversity assessment and community structure analysis in northern Gulf of Mexico Polar ecology: Spatial-temporal dynamics of extreme environment communities Ecotoxicology: Contaminant impacts on meiobenthic populations through field studies and mesocosm experiments

• Analytical Chemistry
• Bioinformatics
• Environmental Sciences
• +۷ مورد دیگر

Dr. Beatriz Galindo-Prieto is a Researcher at Imperial College London's School of Public Health (Faculty of Medicine), part of the Environmental Toxicology Group within the Environmental Research Group. She specializes in developing multiblock/multi-omics and multivariate data analysis methods, with a focus on big data interpretation, variable selection, and applications in environmental health and bioinformatics. Her current projects include analyzing air pollution effects on health via RNA-seq datasets (UKRI NERC-funded) and policy studies on urban air quality. Affiliations include the MRC Centre for Environment and Health, and NIHR Health Protection Research Units in Environmental Exposures and Chemical/Radiation Hazards. Education: MSc in Chemistry (University of the Balearic Islands) and PhD in Chemometrics (Umeå University, Sweden). Her PhD involved novel variable selection algorithms for multivariate data (VIPOPLS, VIPO2PLS, MB-VIOP). Post-PhD roles include postdoctoral fellowships at NTNU (Norway) and Weill Cornell Medical College (USA), focusing on machine learning for big data in neuroscience and epidemiology. She has taught undergraduate/graduate courses and co-supervised students. Research Interests: Combining chemometrics, bioinformatics, and machine learning to address societal challenges. Key areas include multivariate data analysis for environmental systems, neurodegenerative diseases (Alzheimer’s), and real-time epidemic modeling (Ebola, COVID-19). She emphasizes interdisciplinary collaboration across analytical chemistry, systems biology, and public health policy. Grants & Funding: UKRI NERC, ERCIM (Big Data Cybernetics project), and institutional support from Imperial College London. Her work bridges academia and real-world applications, aiming to translate computational methods into actionable insights for health and environmental policy. Labs/Teams: Active in the Environmental Toxicology Group at Imperial, collaborating with the MRC Centre and NIHR units. Her lab develops novel methodologies for data fusion, visualization, and predictive modeling in complex systems.

Evina Katsouمشاهده پروفایل

استاد مهمان

• Sustainable resource recovery from wastewater
• Circular economy assessment
• Data analytics for environmental systems
• +۵ مورد دیگر

Dr Evina Katsou is a Visiting Professor in the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences at Brunel University London. She serves as Course Director for the Water Engineering MSc program and leads the Water & Environmental Engineering research group with 20 researchers. Dr Katsou's educational background includes a PhD in 'Wastewater Treatment with the Use of Membranes' (2008-2011), an MSc in Water Resources Science & Technology (2006-2008), and an MEng in Chemical Engineering (2000-2005), all from the National Technical University of Athens (NTUA), Greece. Her research program focuses on three interconnected areas: (1) Sustainable resource recovery from wastewater and safe reuse; (2) Data analytics, knowledge discovery and process modelling; and (3) Circularity & sustainability measurement and assessment. Dr Katsou has developed innovative approaches for biological nutrient removal from wastewater, including partial nitrification/denitrification and complete autotrophic nitrogen removal processes. Her work integrates advanced data analytics methods such as multivariate statistics, clustering techniques, machine learning algorithms, and artificial neural networks to identify complex relationships between process variables, particularly for measuring GHG emissions in water treatment systems. Dr Katsou has authored 98 journal publications (h-index: 27) and 12 book chapters with over 100 presentations, and holds a patent on biopolymers recovery. Her recent publications demonstrate a strong focus on circular economy applications in water systems, nature-based solutions for urban environments, and resource recovery technologies. The research trends show increasing integration of data-driven decision support systems with environmental sustainability metrics, particularly in the water-energy-food nexus domain. Chartered Chemical Engineer (Greece) HEA Fellow (FHEA) of the Higher Education Academy Mentor in the APEX (Academic Practice & Professional Excellence Framework) scheme Member of management group of the Royal Academy of Engineering (RAE) Visiting Professors Scheme Co-leader of Circular Economy VLT in Water Europe Leader of SMART WATER Working Group in ICT4Water Cluster-EC Member of ISO standards drafting team on Circular Economy Dr Katsou has successfully supervised 16 PhD students to completion and currently supervises several ongoing doctoral projects. She has secured substantial research funding as Principal Investigator on 11 national and international projects including RESILEX Horizon Europe, SYMBIOREM Horizon Europe, BORECER Horizon Europe, HYDROUSA H2020, and WATER-MINING H2020. Her industrial consulting portfolio includes significant contracts with leading UK water industry partners such as Affinity Water, Arup, Severn Trent, and Anglian Water, with projects ranging from carbon footprint assessment to corrosion mitigation and process optimization. She leads the Transformation Tools group of the Cost Action on Circular Cities, co-leads the Circular Water VLT of Water Europe, and co-leads the SMART-WATER group of the ICT4Water Cluster-EC. Dr Katsou is also a member of the drafting team for new ISO standards on Circular Economy (Measuring and Assessing Circularity - ISO5902).

• Environmental Chemistry
• Marine Chemistry
• Trace Metal Pollution
• +۳ مورد دیگر

Vasiliki Paraskevopoulou is a Researcher at the Laboratory of Environmental Chemistry, Department of Chemistry, National and Kapodistrian University of Athens. She works at the Zografou Campus (Greece) and has active research in marine and coastal environmental chemistry. Affiliation: National and Kapodistrian University of Athens Department: Chemistry Email: vparask@chem.uoa.gr Office: 3rd floor, Wing E, Office 4B Phone: +30-210-7274448 Her research focuses on trace metal pollution in marine ecosystems, chemical speciation analysis, and environmental monitoring of coastal zones. She has contributed to studies on heavy metal dynamics in transitional waters, sediment quality assessments, and hydrogeochemical investigations of industrial and natural contamination sources. Recent publications highlight her work on Mediterranean seafloor protection strategies (2025), mercury monitoring under EU directives (2024), metal bioaccumulation in marine species (2023), and groundwater quality assessments in river deltas (2014-2022). Her research spans environmental modeling, pollution assessment, and biogeochemical interactions in coastal systems. Awards: Teaching Personnel Award (2019-2024, 2016-2018) The Laboratory of Environmental Chemistry at the University of Athens provides analytical expertise and educational support in chemical speciation, heavy metal pollution, and marine environmental protection. She contributes to national monitoring programs and international directives like the Water Framework Directive through field surveys, modeling systems, and sediment analysis.

Satu-Pia Reinikainenمشاهده پروفایل

استاد

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +۳ مورد دیگر

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

Mevin Hootenمشاهده پروفایل

استاد

• Bayesian Statistics
• Spatio-Temporal Statistics
• Ecology
• +۶ مورد دیگر

Mevin Hooten is a Professor in the Department of Statistics and Data Sciences at The University of Texas at Austin and a Fellow of the American Statistical Association. He previously held a professorship at Colorado State University and served as Assistant Unit Leader for the Colorado Cooperative Fish and Wildlife Research Unit. His research focuses on Bayesian and spatio-temporal statistical methods with applications in ecology, epidemiology, and environmental science. Education: Ph.D. in Statistics from the University of Missouri (2006) Research Interests center on Bayesian statistics , spatio-temporal modeling , and MCMC methods , applied to ecological processes, disease spread, and environmental monitoring. His work addresses challenges in wildlife conservation, climate change impacts, and animal movement inference. Publication Trends reveal a strong emphasis on Bayesian hierarchical models , spatial statistics , and mechanistic ecological modeling . Key subfields include genomic adaptation , occupancy models , riverscape processes , phenological sensitivity , invasion dynamics , and multi-scale ecological inference . Scientific Contributions include authorship of 3 books and over 160 publications. Awards include ASA Fellow (2017) and editorial roles in leading journals like Biometrics and Annals of Applied Statistics . Advising Legacy spans current and former students/postdocs in fields such as ecological statistics , animal movement , and spatio-temporal modeling . Notable alumni include researchers at NOAA, USDA, and academic institutions like Virginia Tech and Penn State.

Nicholas Ouelletteمشاهده پروفایل

استاد

• Complex Systems
• Fluid Dynamics
• Environmental Engineering
• +۴ مورد دیگر

Nicholas Ouellette is a Professor of Civil and Environmental Engineering at Stanford University, specializing in complex systems far from equilibrium. His research emphasizes dynamical self-organization across disciplines like fluid dynamics, granular media, and collective animal behavior. He holds a Ph.D. in Physics from Cornell University (2006), an M.S. in Physics (2005), and a B.A. in Physics and Computer Science from Swarthmore College (2002). Prior to Stanford, he was on the faculty at Yale University’s Mechanical Engineering and Materials Science department from 2008-2015. He has received teaching awards at both institutions. His work spans experimental and theoretical studies of turbulence, granular erosion, insect swarms, and urban dynamics. Current research includes sediment transport in turbulent flows, collective behavior in bird flocks, and urban street network analysis. He leads the Environmental Complexity Lab and collaborates on projects involving ocean mixing instrumentation and computational modeling of complex systems. Research interests include turbulent flows in fluids, particle transport dynamics, granular bed erosion mechanics, and quantitative analysis of animal collective motion. Techniques employed range from particle tracking velocimetry to machine learning for free-surface reconstruction. Ouellette’s recent work explores interdisciplinary connections between physics and urban systems, such as pedestrian access optimization in suburban layouts and the interplay of multiple environmental stressors in policy modeling. Notable contributions include studies on midge swarm mechanics, jackdaw flock kinematics, and the application of spectral analysis to energy transfer in turbulence. His lab develops advanced measurement systems for oceanographic studies and applies network theory to environmental and urban systems analysis. Ouellette maintains active collaborations across engineering, biology, and policy domains.

Thomas Wahlمشاهده پروفایل

دانشیار

• Coastal Flood Risk
• Sea Level Rise
• Storm Surges
• +۷ مورد دیگر

Thomas Wahl is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), within the College of Engineering and Computer Science. He leads the Coastal Risks and Engineering (CoRE) Lab and is a key member of the UCF Coastal Systems Faculty Cluster, focusing on climate-driven coastal hazards. Education: Diploma in Civil Engineering, University of Siegen, Germany Ph.D. in Civil Engineering, University of Siegen, Germany (2012) His research centers on coastal flood risk , sea level rise , storm surges , and compound climate extremes . He investigates how multiple hazards—such as high tides, storm surges, rainfall, and river discharge—interact to create extreme flooding, especially under climate change. His work aims to develop actionable science for coastal resilience and adaptation planning. The recent publications (2022–2023) highlight a strong trend in analyzing compound flooding, sea-level extremes, and their socio-environmental impacts across the U.S., China, Europe, and global coasts. His research integrates observational data, climate modeling, and multivariate statistics to assess current and future risks. Scientific Awards: Walter L. Huber Civil Engineering Research Prize, ASCE (2023) NSF CAREER Award (2022) Early Career Scientist Medal, IAPSO (2021) Outstanding Early Career Scientist Award, EGU (2018) NASA Early Career Investigator Award (2018) Wahl advises graduate students and leads a dynamic research group in the Coastal Risks and Engineering (CoRE) Lab , which studies extreme sea levels, compound events, and coastal risk modeling. His research is supported by major grants from NSF, NASA, and other federal agencies. He has collaborated extensively with institutions including the University of South Florida, University of Southampton, and international climate research centers.

Bersimis Sotiriosمشاهده پروفایل

دانشیار

• Statistical Process Control
• Public Health Surveillance
• Health Analytics
• +۵ مورد دیگر

Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.

• Environmental Epidemiology
• Biostatistics
• Chemical Mixtures and Health Effects
• +۳ مورد دیگر

Nina Lazarevic is a Research Fellow at the Australian National University's Centre of Epidemiology for Policy and Practice within the National Centre for Epidemiology and Population Health. She holds a PhD in environmental epidemiology and biostatistics, along with Masters degrees in biostatistics and environmental science. Prior to her academic career, she worked as an econometrician in banking and market research. Research Fellow, Centre of Epidemiology for Policy and Practice PhD in Environmental Epidemiology and Biostatistics Masters in Biostatistics and Environmental Science Her research focuses on statistical methods in environmental epidemiology, particularly Bayesian nonparametric approaches for analyzing health effects of environmental chemical mixtures and exposome-health associations. Key projects include studies on PFAS exposure impacts on maternal and child health, telehealth policy evaluation, and asbestos-related cancer incidence in the ACT. Nina has published extensively on environmental chemical exposure impacts, including PFAS contamination studies (2023-2024) and prenatal exposure effects on fetal growth (2022). Her methodological work includes simulation studies for chemical mixture analysis (2020). Current projects involve: ACT Asbestos Health Study II: Linked Data Project ANU Telehealth in Primary Care Study PFAS Health Study Her work leverages large-scale linked data to inform government policy on cardiovascular disease prevention and chronic disease management.

Shuhab D Khanمشاهده پروفایل

استاد

• Remote Sensing
• Geospatial Research
• Hyperspectral Imaging
• +۵ مورد دیگر

Dr. Shuhab D Khan is a Full Professor in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. He leads the Remote Sensing and Geospatial Research Laboratory (GeoRS), focusing on advanced geospatial techniques for earth science applications. His research integrates remote sensing, geospatial analytics, and field geology to address critical geological and environmental challenges. PhD in Geoscience from The University of Texas at Dallas (1996-2001) Dr. Khan specializes in hyperspectral imaging, land subsidence analysis, tectonic studies, and rare earth element exploration. His work bridges traditional geological field methods with cutting-edge remote sensing technologies, particularly in developing innovative spectral indices for mineral identification. He has made significant contributions to understanding active tectonics in the Himalayas and hydrogeological systems in North Africa. His recent publications show a strong trend toward multi-scale analysis of geological phenomena, from microscopic mineral identification to regional tectonic studies. He has pioneered techniques using drone-based hyperspectral imaging for rare earth element detection and developed novel approaches for analyzing land subsidence in urban areas using InSAR technology. Dr. Khan actively mentors students through research projects and leads the Remote Sensing and Geospatial Research Laboratory. His laboratory work focuses on developing practical applications of geospatial technologies for resource exploration, environmental monitoring, and natural hazard assessment. He has secured research funding for projects related to coastal erosion monitoring, subsidence analysis, and mineral exploration.

Hoai Phuong Haمشاهده پروفایل

استاد

• Green and energy-efficient computing
• Parallel and distributed computing systems
• Cyber-physical systems
• +۳ مورد دیگر

Professor Hoai Phuong Ha is affiliated with UiT The Arctic University of Norway's Department of Computer Science. A leading expert in green computing and cyber-physical systems, they contribute to Arctic research through the Distributed Arctic Observatory (DAO) and Arctic Green Computing (AGC) group. Founded ARC (Arctic Center for Sustainable Energy) PI in EU FP7 EXCESS and H2020 TAILOR projects WP-leader in EEA POLNOR HAPADS and RCN PREAPP projects Their research focuses on energy-efficient computing, including IoT systems, edge computing, and parallel algorithms. Recent work addresses wireless charging trajectories (eU2U, 2025), smart grid networks (GridWatch, 2024), and pollution monitoring (2024). Publications span cyber-physical observatories, sensor calibration, and distributed systems optimization. Key trends in their 15 most recent articles (2017-2025) include: energy-aware data structures, Arctic-adapted IoT deployments, and sustainable computing methods. Collaborations span EU and Norwegian grants with applications in smart grids, environmental sensing, and high-performance computing. Co-founder of Arctic Center for Sustainable Energy (2017) Active in EEA POLNOR (2019-2023) and RCN eX3 infrastructure project Their lab (Realfagbygget A237) develops systems for Arctic tundra monitoring, including UAV-powered networks and energy-harvesting protocols. Students include researchers from multiple international collaborations.