جست‌وجوی استاد و پژوهشگر

Paula Federicoمشاهده پروفایل

استاد

• Mathematical Ecology
• Computational Ecology
• Population Dynamics
• +۳ مورد دیگر

Paula Federico serves as a Professor in the Department of Mathematics, Computer Science & Physics at Capital University, where she has been a faculty member since 2009 following postdoctoral positions at the University of Tennessee Knoxville and Ohio State University's Mathematical Biosciences Institute. Her teaching responsibilities include Calculus I, Calculus III, Differential Equations, Introduction to Computational Sciences, and Linear Algebra. Her academic credentials include: Ph.D. in Ecology and Evolutionary Biology from the University of Tennessee Graduate Certificate in Applied Statistics from the University of Tennessee B.Sc. in Mathematics from Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina Dr. Federico's research program centers on mathematical and computational ecology, with specific applications to population dynamics, natural resource conservation, epidemic/epizootic modeling, and animal movement behavior. She emphasizes mathematics' relevance across disciplines and seeks to reveal its inherent beauty to students, particularly mathematics majors. Analysis of her publication record reveals consistent focus on developing mathematical frameworks for ecological challenges, especially in spatio-temporal population modeling, habitat quality assessment for mobile species, and migratory network dynamics. These works typically involve large interdisciplinary collaborations addressing conservation biology and wildlife management. No scientific awards are documented in the provided materials. Information regarding graduate student advising, research grants, laboratory facilities, or administrative roles was not included in the source text.

• Glaciology
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Dr Rebecca Dell is an Assistant Professor in Glaciology at the Scott Polar Research Institute (SPRI), University of Cambridge, and a Director of Studies and Geography Fellow at Trinity Hall, Cambridge. She specializes in remote sensing and fieldwork-based studies of Antarctic ice shelves , focusing on meltwater dynamics and stability. Her research integrates machine learning with satellite data to advance understanding of polar environments. PhD in Polar Studies (2021), University of Cambridge MPhil in Geography (2017), Newcastle University BSc (First Class) in Geography (2016), University of Durham Dr Dell's research explores the spatio-temporal variation of ice shelf meltwater using remote sensing and machine learning. She developed a Random Forest Classifier to detect slush and ponded water across Antarctica, improving climate models. Her work spans collaborations with NSF/NERC projects and ESA's Climate Change Initiative. Recent publications highlight her contributions to Antarctic surface hydrology , including studies on meltwater-induced fracture, slush detection, and SAR/optical satellite integration. She has conducted fieldwork in Antarctica and Iceland. NERC Field Spectrometry Facility Loan (2022) ESA Climate Change Initiative Fellowship (£83,000) (2020) NERC PhD Studentship (~£78,000) (2017) Dr Dell supervises postgraduate students on topics like ice shelf stability and supraglacial hydrology. She is affiliated with Trinity Hall, Cambridge, and previously held roles at Newnham College and the European Geosciences Union.

Blanca Maria Priego Torresمشاهده پروفایل

استادیار

• Biomedical Engineering
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Blanca Maria Priego Torres serves as an Assistant Professor in the Department of Automation, Electronics, Architecture and Computer Networks Engineering at the Universidad de Cádiz, where she conducts research through the Institute for Research and Innovation in Biomedical Sciences (INiBICA) and the TIC212 Bioengineering, Automation and Robotics group. Her academic foundation includes a Doctorate from Universidade da Coruña (2017) with the thesis "A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequences" supervised by Dr. Richard J. Duro Fernández. Dr. Priego Torres' research spans biomedical engineering , computer vision , and robotics , with pioneering work in deep learning applications for medical imaging and hyperspectral processing. Her contributions include automated disease screening from chest X-rays, lung segmentation algorithms, breast cancer diagnostics, and cellular automata-based hyperspectral image analysis, demonstrating interdisciplinary innovation at the intersection of engineering and medicine. Analysis of her 2020-2025 publications reveals dominant themes in medical image analysis (chest X-rays, breast histopathology) using advanced architectures like Mamba-YOLOvX and SAM, alongside sustained contributions to hyperspectral imaging for environmental monitoring and biomedical applications, highlighting her dual expertise in clinical diagnostics and remote sensing technologies. As an active member of the TIC212 research group at INiBICA, she leads projects integrating automation, robotics, and AI for biomedical solutions, including autonomous ship modeling for marine testing and cough detection systems using wearable sensors, advancing practical applications of her theoretical work.

• Visual Analytics
• Information Visualization
• Time-Oriented Data Analysis
• +۵ مورد دیگر

Phong H. Nguyen is an active researcher in visual analytics and information visualization, with verified affiliation to RedSift. His work spans interdisciplinary collaborations across institutions like City University London, Fraunhofer IAIS, and Middlesex University. Key research themes include time-oriented data visualization , user behavior analytics , and human-computer interaction . He contributes to spatio-temporal analysis , security visualization , and emergency response systems . Research Trends in his publications emphasize analytic provenance for sensemaking, time-set relationships in data visualization, and machine learning integration for behavioral pattern recognition. Notable applications include cybersecurity monitoring , epidemiological modeling , and laser material processing . Collaborative Networks involve partnerships with leading experts in visual analytics, including Professor Cagatay Turkay (University of Warwick) and Professor Jason Dykes (City St George's, University of London).

• Hydraulic Engineering
• Flood Risk Management
• Hydrology
• +۳ مورد دیگر

Susanna Dazzi is a Fixed-term Researcher at the Department of Engineering and Architecture, University of Parma, Italy. Her work focuses on hydraulic engineering, flood risk management, and computational modeling, leveraging advanced techniques like physics-informed neural networks and GPU acceleration for real-time flood prediction. Key Research Areas: Flood hazard estimation, levee breach dynamics, dam-break scenarios, 2D hydraulic modeling, and sediment transport analysis. Teaching: Delivers courses on Hydraulic Infrastructures and Hydraulic Protection of the Territory for Master's Degree programs in Civil Engineering. Publications: Develops data-driven models (e.g., FloodSformer) for real-time flood forecasting and investigates hydrodynamic interactions with infrastructure. Contact: Email: susanna.dazzi@unipr.it ; Office: Science Area Park, 181/A, 43124 Parma, Italy.

Kashif Mahmudمشاهده پروفایل

استادیار

• Remote Sensing
• Hydrogeology
• Terrestrial Carbon Cycle
• +۱۲ مورد دیگر

Kashif Mahmud is an Assistant Professor at the Kimbell School of Geosciences, Midwestern State University, and an Adjunct Senior Lecturer at the School of Biological, Earth and Environmental Sciences, University of New South Wales. His research integrates remote sensing, hydrogeology, and carbon cycle dynamics, focusing on karst environments, groundwater recharge, and climate change impacts. Develops innovative methodologies using terrestrial LiDAR and drip loggers for subsurface hydrology studies Specializes in data assimilation for improving terrestrial biosphere model predictions Collaborates on international projects like the ARC-funded 'Caves and their stalagmites: linking climate to groundwater recharge' His lab engages in advanced geospatial projects, including the 'Texas as Art' initiative and NSF-funded GeoTech Center activities. Research areas span from cave hydrology to wildfire smoke impact analysis, with a strong emphasis on computational modeling and environmental sustainability. Recent publications highlight his expertise in: Stochastic modeling of karst aquifers with training image algorithms LiDAR-based biomass estimation for urban forests Climate change feedbacks in terrestrial carbon cycling Wildfire PM2.5 pollution dynamics using satellite data The lab's work has received recognition through student awards (AIPG Shoemaker Award, GSA Karst Division Grant) and collaborative grants like the $3,038,909 ARC project. He supervises graduate students in projects ranging from cave exploration with emerging technologies to spatiotemporal deforestation analysis, maintaining active fieldwork at sites like Natural Bridge Caverns, Texas.

Emilia Sivieroمشاهده پروفایل

پژوهشگر

• Spatial Statistics
• Extreme Value Theory
• Nonparametric Covariance Estimation
• +۴ مورد دیگر

Emilia Siviero is a Post-doctoral Researcher at Università Ca' Foscari, Venezia in the Department of Environmental Sciences, Informatics and Statistics (DAIS) , working on Spatial Statistics and Extreme Value Theory under the supervision of Ilaria Prosdocimi and Carlo Gaetan. Previously, she earned her PhD at Télécom Paris with a thesis titled Statistical Learning for Spatial Data: Theory and Practice , focusing on spatial statistics and spatio-temporal Hawkes processes. Her research spans theoretical and applied work in Spatial Statistics , Nonparametric Covariance Estimation , Spatio-temporal Hawkes Processes , and Extreme Value Theory . Recent publications address parametric inference for Hawkes processes, Kriging from a machine learning perspective, and stochastic algorithms in optimal transport. She has presented at international conferences such as COMPSTAT and CAp, and organized events like the Chaire DSAIDIS day. Her teaching experience includes courses on Statistical Learning , Machine Learning , and Probability for Télécom Paris and Mines ParisTech students. She is actively involved in computational statistics and climate data analysis projects, with software tools available on GitHub.

Aroune Duclosمشاهده پروفایل

مدرس

• Acoustics
• Plant Biomechanics
• Ultrasonics
• +۴ مورد دیگر

Aroune Duclos is a Lecturer and researcher at Le Mans University, working within the Institute of Acoustics. He is a member of the Materials team, focusing on Acoustics and Mechanics of Porous Materials and Ultrasound research areas. His research interests span multiple interdisciplinary fields, with a primary focus on plant acoustics - investigating how sound and mechanical vibrations affect plant physiology and defense mechanisms. His work bridges physics, biology, and materials science, exploring topics such as plant response to acoustic stimuli, pathogen interactions, and ultrasonic applications in material characterization. Analysis of Duclos's recent publications reveals a strong trend toward interdisciplinary research connecting acoustics with plant biology. His work demonstrates how mechanical cues from pathogens and acoustic stimuli trigger specific defense responses in plants, with implications for both agricultural science and acoustic sensor development. He also maintains active research in ultrasonic imaging techniques and non-linear acoustics applications. Duclos collaborates extensively with researchers across multiple institutions, as evidenced by his co-authorship on diverse projects ranging from polymer science to plant biomechanics and non-destructive testing methodologies.

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +۳ مورد دیگر

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

Nikolaos E. Panagiotouمشاهده پروفایل

پژوهشگر

• Text Mining
• IoT/Smart-Cities
• Spatio-temporal Analysis
• +۳ مورد دیگر

Nikolaos E. Panagiotou is a Research Engineer at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been working since March 2014. He is also a Ph.D. candidate in Real-time Data Analysis in Massive Streams and Social Media at the same institution under the supervision of Prof. D. Gunopulos. He is an active member of the KDDLAB research group at the University of Athens, participating in EU research projects including INSIGHT and VaVeL. His educational background includes: MSc. with Distinction in Artificial Intelligence, Learning from Data from the School of Informatics, University of Edinburgh (2012-2013) BSc. in Informatics and Telecommunications from the Department of Informatics and Telecommunications, University of Athens (2007-2011) Dr. Panagiotou's research focuses on real-time analysis of high-volume data streams with particular emphasis on text and sensor streams, including news and social media content. His current research interests include: Text Mining: event detection, first story detection, open information extraction IoT/Smart-Cities: sensor analysis, heterogeneous sensor aggregation, real-time visualization Spatio-temporal Analysis: congested road-segments, travel time estimation, OSM His work bridges theoretical research with practical applications in smart city technologies and real-time data processing systems. His publication record demonstrates a strong focus on smart city applications, real-time data processing, and information extraction from diverse data sources. The research shows progression from foundational work on stream mining and event detection to sophisticated applications in urban mobility and news monitoring systems. His most recent work continues to advance techniques for handling sparse data environments and real-time analytics, with particular emphasis on travel time estimation and hybrid analytical approaches. Dr. Panagiotou has been involved in significant EU-funded projects: INSIGHT project: Implementation of real-time analysis modules and visualization engine for the smart city of Dublin VaVeL project: Design of integration schemes and development of real-time analysis modules using spatio-temporal data He has developed multiple demonstration systems including the News Monitor for real-time news analysis and various smart city monitoring dashboards. His technical expertise spans real-time data processing, stream mining, spatio-temporal analysis, and visualization systems, with applications in urban mobility and news monitoring. His collaborative work with researchers across Europe demonstrates his integration into the international research community in data science and smart city applications.

• Occupational and Environmental Exposure
• Safety Assessment of Chemicals
• Non-Animal Toxicity Testing
• +۴ مورد دیگر

Dr. Esmeralda Krop serves as Project Manager at Utrecht University's Institute for Risk Assessment Sciences (IRAS) within the Department of Population Health Sciences under the Veterinary Medicine school. With a PhD in occupational allergy from the University of Amsterdam, she has dedicated her career to studying human exposure scenarios in occupational and environmental contexts. Project Manager for VHP4Safety (Virtual Human Platform for Safety Assessment) Lead for AFARA (Animal-Free Assays for Endocrine Disruption) Specializes in transitioning from animal-based to human-relevant safety testing Expertise in exposure assessment, immunotoxicology, and environmental epidemiology Her research focuses on: Developing animal-free safety assessment frameworks Understanding exposure-response relationships for chemicals Creating integrated modeling approaches for resident pesticide exposure Studying immune marker dynamics in environmental contexts Advancing human biology-based toxicity testing Recent publications highlight her work on: Pesticide exposure pathways in agricultural communities Longitudinal immune marker stability in health research Microbial exposures in moisture-damaged buildings Endocrine disruption assessment methodologies Occupational health in construction and livestock industries She works closely with: International consortia including NWO, H2020 EXPANSE Interdisciplinary teams across toxicology, epidemiology, and data science Regulatory bodies and industry partners in chemical safety

• Global Health
• Health Transitions
• Public Health Nutrition
• +۷ مورد دیگر

Associate Professor Matthew Kelly is a prominent researcher at the National Centre for Epidemiology and Population Health (NCEPH) within The Australian National University. With over 15 years of research experience focused on the Asia-Pacific region, particularly Southeast Asia, he leads the Master of Public Health program at ANU and maintains close collaborations with universities and ministries of health in Indonesia, Thailand, Laos, and Cambodia. Department of Applied Epidemiology Building 62, Room 2.08 ORCID: 0000-0001-7963-2139 Dr. Kelly's research spans critical health challenges in the Asia-Pacific region, with special emphasis on Southeast Asia. His work addresses changing nutrition burdens, health information system strengthening (particularly for mortality surveillance), water, sanitation and hygiene (WASH) and related health burdens, and non-communicable disease and infection interactions, with a specific focus on tuberculosis-diabetes mellitus co-morbidity. His research methodology combines epidemiological approaches with spatial analysis and health systems strengthening. Analysis of his recent publications reveals a strong focus on tropical diseases, multimorbidity patterns, and health system interventions across Southeast Asia. His work increasingly incorporates spatial modeling techniques and addresses the intersection of infectious and non-communicable diseases, particularly in resource-limited settings. The research demonstrates a clear trajectory toward addressing complex health challenges through integrated, multi-sectoral approaches. Dr. Kelly actively supervises numerous graduate students and leads multiple research projects focusing on health resilience in Indonesia, mortality surveillance in Pacific nations, and combating health misinformation. His collaborative approach is evident in the extensive international partnerships reflected in his publications. His current research portfolio includes projects on environmental enteric dysfunction in Central Java, mortality surveillance models in Solomon Islands and Timor Leste, climate-resilient primary healthcare in Indonesia, and innovative approaches to health education in the Lower Mekong Basin.

Insa Otteمشاهده پروفایل

پژوهشگر ارشد

• Datacubes
• Climate Change
• Alpine and Tropical Ecosystems
• +۷ مورد دیگر

Dr. Insa Otte is a Research Fellow at the University of Würzburg , affiliated with the Institute of Geography and Geology under the Faculty of Philosophy . Her work focuses on datacubes , climate change , and spatio-temporal analysis in alpine and tropical ecosystems. PhD: 2017, University of Marburg — "Global Climate Change vs. Local Land-Use Change and its Impact on Atmospheric Water Input at Mt. Kilimanjaro" Diploma: 2012, University of Marburg — "Nutrient Input in South Ecuadorian Rainforests" Her research spans East and Southern Africa , analyzing precipitation isotopes , extreme weather , and ecosystem responses through GIS and remote sensing . She contributes to agricultural decision support systems and invasive species modeling like Lantana camara in savannahs. Current projects integrate Earth observation datacubes for climate adaptation in West Africa. Her work combines satellite data (Landsat, Sentinel) with ground observations to study vegetation dynamics and land degradation in tropical mountains and savannahs.

Orkun Furatمشاهده پروفایل

مدرس

• Machine Learning
• Stochastic Modeling
• Spatial Statistics
• +۵ مورد دیگر

Dr. Orkun Furat is a Lecturer at the Institute of Stochastics, University of Ulm, Germany, where he conducts research at the intersection of machine learning, stochastic modeling, and image analysis for materials science applications. His work focuses on developing advanced computational methods to characterize and reconstruct 3D microstructures from 2D image data, with significant contributions to battery materials and particle systems. His primary research interests include generative adversarial networks (GANs) and spatial stochastic models for tomographic image analysis of functional materials. He has pioneered techniques for super-resolving microscopy images, quantifying electrode degradation in batteries, and modeling particle morphology/separation processes in mineral processing. His interdisciplinary approach bridges statistics, computer science, and materials engineering through rigorous mathematical frameworks. Recent publications (2024-2025) reveal a concentrated focus on lithium-ion and all-solid-state battery technologies, particularly analyzing how operating conditions (charge rate, temperature, cycling) induce electrode degradation. Simultaneously, his particle systems research employs multidimensional stochastic models to optimize mineral beneficiation processes like flotation, using copula-based approaches for particle property distributions. Dr. Furat actively supervises seminar students in generative machine learning and spatial stochastic modeling while teaching core courses including Point Processes and Advanced Statistics. His research impact is evidenced by numerous invited talks at premier venues like the Dagstuhl Seminar (2025) and European Congress for Stereology (2025), where he presents as a plenary speaker on AI-driven microstructure reconstruction. Collaborating with interdisciplinary teams across materials science and engineering, his work on digital twins for battery electrodes and virtual materials testing has been featured in University of Ulm press reports (2024) highlighting applications in efficient battery recycling and sustainable material design. Current projects integrate generative AI with stochastic geometry to solve industrial-scale challenges in energy storage and mineral processing.

Samuel McDougleمشاهده پروفایل

استادیار

• Cognitive Psychology
• Neuroscience
• Motor Learning
• +۳ مورد دیگر

Samuel McDougle is an Assistant Professor in the Department of Psychology at Yale University, where he leads the Action, Computation, and Thinking (ACT) Lab. His research explores the interplay between cognitive processes and motor behavior using behavioral experiments, computational modeling, neuroimaging, and neuropsychology. Research Interests: McDougle investigates how humans acquire and perform motor skills, emphasizing cognitive demands beyond habit formation. Key areas include instrumental learning, reinforcement learning, working memory, and sensorimotor adaptation, with applications to attention, metacognition, and brain-computer interfaces. Recent Trends: His recent publications focus on dual-tasking in learning, memory of observed actions, spectrotemporal correlations in pitch detection, and the role of prediction errors in adaptation. The ACT Lab also studies attentional modulation by rewards and non-Markovian learning structures. Scientific Awards: NIH R01 grant (2023) Labs & Teams: The ACT Lab at Yale (100 College St.) conducts interdisciplinary research on motor cognition, recruiting PhD students and postdocs. The lab emphasizes collaborations between human learning and computational neuroscience.