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Sonja GrünView profile

Professor

• Neuroscience
• Computational Neuroscience
• Neural Networks
• +3 more

Prof. Sonja Grün is a Professor and Director of the Computational and Systems Neuroscience division (IAS-6) and the JARA-Institute Brain structure-function relationships (INM-10) at Forschungszentrum Jülich. She leads the Statistical Neuroscience research group, focusing on cell assemblies, dynamical neuronal interactions in cortex, higher-order correlation analysis, and reproducible workflows. Her work bridges experimental and computational approaches to understand neural coding and network dynamics. Key contributions include methodologies for spike pattern detection (e.g., SPADE) and tools for data curation (UnitRefine). She actively develops neuroinformatics standards (e.g., Neuroelectrophysiology Analysis Ontology) and promotes reproducible research through provenance tracking initiatives like NFDI-Neuro. Her research spans motor cortex activity, visual processing, and model validation frameworks. Research interests emphasize spatio-temporal spike patterns, cortical network models, and artifact detection in high-density recordings. She collaborates on projects integrating electrophysiological data with computational simulations (e.g., NEST) and brain wave analysis (Cobrawap). Current efforts address challenges in data sharing, artifact removal, and scalable analysis pipelines for heterogeneous datasets.

Michael DenkerView profile

Researcher

• Neuroscience
• Reproducible Science
• Data Analytics
• +4 more

Dr. Michael Denker is a Team Leader in the Computational and Systems Neuroscience department at the Institute for Advanced Simulation (IAS-6) , Forschungszentrum Jülich. His research focuses on data analytics for electrophysiology and optophysiology in behavioral neuroscience, emphasizing reproducible science practices and collaborative workflows. He leads the Data Science of Electro- and Optophysiology in Behavioural Neuroscience team, developing tools like Elephant , Neo , and Cobrawap to standardize data analysis across neuroscience experiments. His work addresses challenges in metadata management, provenance tracking, and interoperability between experimental and simulation datasets. Key contributions include the Neuroelectrophysiology Analysis Ontology (NEAO) for structured data sharing and frameworks for validating neural network simulations. Denker actively promotes open science through initiatives like the German National Research Data Infrastructure (NFDI-Neuro) and collaborates with the Human Brain Project (HBP) on EBRAINS services. His research integrates multi-scale data from electrophysiological recordings and computational models to study cortical waves and spike patterns, particularly in rodent and primate systems.

• Wildlife Conservation
• Ecology
• Biodiversity
• +3 more

Dipanjan Naha is affiliated with the Warnell School of Forestry and Natural Resources at the University of Georgia. His research focuses on wildlife conservation, large carnivore ecology, and human-wildlife conflict mitigation. He investigates spatial interactions between apex predators, the impacts of human activities on wildlife habitats, and strategies to resolve conflicts between human populations and large mammals. Key research areas include the behavior and coexistence of sympatric large carnivores (e.g., tigers, leopards, lions), the effects of tourism and land use changes on wildlife movement patterns, and the socio-economic dimensions of conservation in multi-use landscapes. His work often employs spatial modeling and ecological analysis to address biodiversity conservation challenges. Recent studies highlight his contributions to understanding human-leopard conflicts in the Himalayas, the role of fencing in carnivore management, and the historical fragmentation of elephant habitats in Nepal. His findings emphasize the importance of integrating ecological and socio-cultural factors in developing sustainable conservation policies. No scientific awards or grants are explicitly listed in the provided text. However, his extensive publication record reflects active engagement in global conservation research, particularly in South and Southeast Asia.

• Geomorphology
• Karst Geology
• Environmental Geology
• +6 more

Professor Carlos Roque Pau is affiliated with the Environmental Sciences Department at the University of Girona. His research focuses on geomorphology, karst geology, and environmental geology, particularly in the context of sinkhole dynamics, salt tectonics, and climate change impacts on geological hazards.

Sakshi AryaView profile

Assistant Professor

• Multi-armed bandit problem
• Sequential decision making
• Nonparametric estimation
• +2 more

Sakshi Arya serves as an Assistant Professor in the Department of Mathematics, Applied Mathematics and Statistics at Case Western Reserve University, located in Room 205D at 2145 Adelbert Road. She maintains active research and teaching responsibilities within the department, with contact accessible via sxa1351@case.edu and phone 216-368-0470. Her research focuses on advanced statistical methodologies and decision theory, specifically: Multi-armed bandit problem for optimal resource allocation Sequential decision making under uncertainty Nonparametric estimation techniques for complex data structures Statistical learning theory foundations Spatio-temporal analysis for geographically distributed data Dr. Arya's work bridges theoretical statistics with practical applications in adaptive systems, emphasizing algorithmic efficiency and robustness in dynamic environments. Her research contributes to machine learning frameworks requiring real-time data processing and decision optimization, particularly relevant in fields like autonomous systems and large-scale data analytics where traditional parametric models prove insufficient.

• Wind Energy Systems
• Smart Grid Technologies
• Power Electronics
• +5 more

Prof. Dr.-Ing. Reiner Schütt is a faculty member at the Department of Technology , West Coast University of Applied Sciences, specializing in energy system integration and smart grid technologies. He leads the Grid Integration Working Group at the Institute for the Transformation of the Energy System (ITE) and contributes to large-scale energy projects in Schleswig-Holstein. Research Focus: Wind energy systems, MVDC grid integration, energy storage optimization, and smart grid applications Key Projects: MVDC-Opt, SYSTOGEN100, QUARREE 100, SmartRegion Pellworm His recent publications (2022-2024) examine power electronics for grid compatibility, MVDC link control strategies, and spatio-temporal energy system evaluation metrics. Conference participation spans IEEE PEDG, International Conference on Electrical Engineering and Green Energy, and local energy workshops in Germany. Advising: Supervised 30+ theses on topics ranging from wind turbine control to smart metering systems, with industry collaborations including Siemens, DNV GL, and E.ON Hanse AG.

Paula FedericoView profile

Professor

• Mathematical Ecology
• Computational Ecology
• Population Dynamics
• +3 more

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
• +4 more

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 TorresView profile

Assistant Professor

• Biomedical Engineering
• Computer Vision
• Deep Learning
• +4 more

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
• +5 more

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
• +3 more

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 MahmudView profile

Assistant Professor

• Remote Sensing
• Hydrogeology
• Terrestrial Carbon Cycle
• +12 more

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 SivieroView profile

Researcher

• Spatial Statistics
• Extreme Value Theory
• Nonparametric Covariance Estimation
• +4 more

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 DuclosView profile

Lecturer

• Acoustics
• Plant Biomechanics
• Ultrasonics
• +4 more

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
• +3 more

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.