جست‌وجوی استاد و پژوهشگر

• Aquatic Biodiversity
• Environmental Science
• Freshwater Ecosystems
• +۴ مورد دیگر

Tatenda Dalu is a prominent researcher at Stellenbosch University specializing in Aquatic Biodiversity and Environmental Science . His work focuses on freshwater ecosystems, including invasive species management, water quality dynamics, and anthropogenic impacts on aquatic environments. Research Interests Ecological impacts of invasive species Water quality and pollution monitoring Anthropogenic and climate change effects on aquatic systems Conservation strategies for freshwater biodiversity Ecosystem responses to management interventions Key Scientific Trends Recent publications (2012–2020) highlight his expertise in invasive fish species (e.g., Clarias gariepinus , Cyprinus carpio ), cyanobacteria dynamics, and long-term ecological monitoring in tropical reservoirs and southern African aquatic systems. His work spans disciplines like hydrobiology, parasitology, and ecosystem restoration. International Collaborations Dalu has collaborated with institutions such as the Institute for Advanced Study Berlin and Wissenschaftskolleg Berlin , with research locations spanning South Africa , Zimbabwe , Botswana , and multi-country studies.

Nienke van Geelمشاهده پروفایل

پژوهشگر ارشد

• Marine Mammal Ecology
• Bioacoustics
• Cetacean Distribution
• +۳ مورد دیگر

Dr. Nienke van Geel is a Research Fellow (Post-Doctoral Research Associate) at the Scottish Association for Marine Science (SAMS), University of the Highlands and Islands, where she works within the Energy Innovation Team. Her research focuses on marine mammal ecology and acoustics, particularly cetacean distributions and anthropogenic interactions. Her primary research interests include: Cetacean spatio-temporal distributions and habitat preferences Marine mammal bioacoustics and conservation Interdisciplinary approaches integrating biology, ecology and conservation methods Environmental impact assessments of marine renewable energy developments Dr. van Geel's publications consistently address marine anthropogenic impacts, particularly from wind farms and tidal turbines, using acoustic monitoring techniques. Her work demonstrates strong focus on: Population-level impact assessments of marine infrastructure Acoustic telemetry applications for marine species management Standardization of underwater sound analysis methodologies She actively contributes to public understanding through media engagements, including: Webinars on dolphin behavior (e.g., 'Barra Boys' dolphin pod) Participation in global underwater sound experiments Research features on whale and dolphin acoustics in Scottish waters

Lingzhong Guoمشاهده پروفایل

مدرس

• Signal Processing
• Biomedical Engineering
• Nonlinear Systems Identification
• +۳ مورد دیگر

Lingzhong Guo is a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield , affiliated with the Insigneo Institute for in silico Medicine and Neuroscience Institute . His research bridges signal processing, nonlinear systems identification, and biomedical applications. PhD from Bristol Robotics Laboratory (UWE Bristol) Specializes in spatio-temporal system identification, PDE control, and machine learning for medical imaging Recent work focuses on deep learning applications for muscle segmentation in MR images and EIS analysis for oral disorder detection. Collaborations with Zilico Ltd. and participation in Horizon2020 projects highlight his translational research. Scientific Recognition SANO Centre grant (H2020, £2.5M) for computational diagnostics SPINe: Numerical and Experimental Repair strategies (H2020 European program, €1.58M) Innovate UK KTP for EIS-based medical diagnosis (£190K) His publications span nonlinear dynamics , multiscale modeling , and biomedical signal processing , with methodological contributions to Volterra series and coupled PDE-ODE systems.

• Multi-source Sensor Fusion
• Semantic Mapping
• Collaborative Robotics
• +۵ مورد دیگر

Hiba Al-Assaad serves as a Teacher-researcher at CESI Higher Education Group in Toulouse, France, specializing in Engineering and Digital Tools within the Computer Networks and Telecommunications domain. Her work bridges academic instruction in preparatory and engineering cycles with advanced research in robotics and navigation systems. Her research focuses on multi-source sensor fusion, semantic mapping for mobile robotics, and cyber-physical systems. Key areas include developing robust navigation algorithms using digital elevation models, creating decentralized decision systems for industrial applications, and advancing federated learning frameworks. Her work consistently addresses real-world industrial challenges through collaborative robotics and smart transportation solutions. Analysis of her recent publications reveals a strong trajectory toward industrial cyber-physical systems, with increasing emphasis on federated learning, meta-algorithms, and human-robot collaboration in manufacturing environments. Her research integrates geospatial engineering with machine learning to solve navigation and perception challenges. She actively supervises doctoral candidates including A. Achour (defense 2024) researching real-time semantic mapping with robot swarms, and B. Guendouzi (defense 2025) developing decision support systems for industrial cyber-physical environments. Her technical contributions span from fundamental navigation algorithms to cutting-edge machine learning applications, maintaining consistent output across journals like International Journal of Image and Data Fusion , IEEE CSR , and Journal of Network and Computer Applications .

• Artificial Intelligence
• Decision Support Systems
• Uncertain Data Analysis
• +۳ مورد دیگر

Zeineb El Khalfi is a Teacher-researcher at CESI Bordeaux Campus specializing in artificial intelligence and decision support systems, with research focused on mobility/smart transport and uncertain data analysis. Her academic credentials include: Doctorate from Paul Sabatier University of Toulouse and Higher Institute of Management of Tunis (2017) on "Lexicographic refinement in possibilistic sequential decision-making" Research Master's from École Polytechnique de Nantes (2014) in Data Mining and Knowledge Management Research Master's from Higher Institute of Management of Tunis (2014) in Computer Science and Knowledge Management Her research bridges theoretical AI frameworks with real-world applications, particularly in possibilistic reasoning for decision-making under uncertainty. Early work centered on lexicographic refinements in Markov decision processes and possibilistic decision trees, evolving toward smart mobility solutions where she applies spatio-temporal forecasting to micro-mobility redistribution challenges. This trajectory demonstrates a strategic shift from foundational AI theory to transportation-focused implementations while maintaining core expertise in uncertainty modeling. Analysis of her 2016-2023 publications reveals consistent contributions to possibilistic AI (6 conference papers, 3 journal articles), with recent work emphasizing practical transportation applications through micro-mobility systems and data-driven urban mobility solutions. No information is available regarding student supervision or research grants in the provided materials. She actively contributes to CESI's Engineering and Digital Tools Research Team, leveraging interdisciplinary collaboration to advance smart transportation technologies through computational methods.

Stergios Christodoulidisمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Medical Imaging
• Earth Sciences
• +۷ مورد دیگر

Stergios Christodoulidis is a researcher specializing in artificial intelligence and its applications in medical imaging and earth sciences. His work spans deep learning methodologies for image analysis, including multiple instance learning, dosiomics, and registration techniques. He contributes to interdisciplinary research in computational biology, digital pathology, and radiomics. Core research in AI-driven medical image analysis Contributions to earth sciences through remote sensing and satellite imagery Focus on low-data regimes and uncertainty quantification in AI models His recent publications highlight innovative approaches in: Visually grounded bias discovery in clinical AI Conformal prediction adaptation for vision-language models Self-supervised learning for histopathology and radiology applications Integration of biological constraints in machine learning workflows

Andre Amaralمشاهده پروفایل

مدرس

• Spatio-temporal Statistical Modelling
• Bayesian Modelling
• Machine Learning
• +۲ مورد دیگر

Dr André V. Ribeiro Amaral is a Lecturer in Statistical Learning at the School of Mathematical Sciences, University of Southampton. His research focuses on spatio-temporal statistical methods for environmental and public health applications, including Bayesian computation and machine learning for infectious disease modeling. PhD in Statistics from KAUST (2023) MSc in Statistics from UFMG (2020) Previous Research Associate at Imperial College London (2024–2025) Key research themes include: Computationally tractable spatio-temporal models Bayesian inference for complex datasets Machine learning integration in statistical frameworks Applications to epidemiology and environmental science His methodological work spans data fusion, preferential sampling correction, and disease spread dynamics, with recent publications on dengue fever nowcasting and Antarctic krill abundance modeling. Teaching: Co-teaching Statistical Modelling II (MATH3091)

Filip Elvanderمشاهده پروفایل

استادیار

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

Otthein Herzogمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Multi-Agent Systems
• Wearable Computing
• +۵ مورد دیگر

Prof. Dr. Otthein Herzog is a Research Professor at Universitaet Bremen (since 2009), Affiliate Research Professor at George Mason University (1998–2024), and Professor of Artificial Intelligence at Tongji University (2015– ). He leads the Artificial Intelligence Research Group and contributes to interdisciplinary clusters including LogDynamics Research Cluster for Dynamics in Logistics International Graduate School for Dynamics in Logistics CIUC - China Intelligent Urbanization Co-Creation Center His research focuses on Multi-agent systems for autonomous processes Knowledge acquisition with LLMs Mobile/wearable computing for logistics and AAL Image/video analysis for content-driven archiving Integration of deep learning with digital twins Urban planning with small language models Recent work explores sociotechnical modeling of cities through digital twins and AI-driven logistics. Scientific honors include 2024 Chinese Government Friendship Award Magnolia Silver Award (2021) Honorary Professor at Tongji University (2021) Fellow of acatech, GI, and CAE Key projects: EU WearIT@Work (2004–2009) DFG CRC on Autonomous Logistics (2004–2015) MTRC Mobile Technologies Research Center (2004–2010) He serves on boards of UNESCO IKCEST, Shanghai Research Institute for Intelligent Autonomous Systems, and Fraunhofer FIT.

Robert J. Brunnerمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning
• Data Science
• +۴ مورد دیگر

Robert J. Brunner is a Professor in the School of Information Sciences and Department of Accountancy at the University of Illinois Urbana-Champaign, with affiliate appointments across Astronomy, Computer Science, Electrical Engineering, Informatics, Physics, Statistics, Beckman Institute, Computational Science and Engineering, and NCSA. He serves as Data Science Expert in Residence at Research Park and leads the Illinois Data Science Initiative. Education: Ph.D. in Astrophysics, Johns Hopkins University (Sloan Digital Sky Survey development under Alex Szalay) Research Focus: Brunner pioneers statistical and machine learning applications for real-world problems, emphasizing algorithmic optimization, uncertainty quantification, and computational efficiency. His work bridges Observational Cosmology , Data Science , and Information Science , with expertise in transient phenomena analysis and large-scale data systems. Publication Trends: Recent work (2021-2025) reveals a trajectory toward financial data science (stock recovery modeling, ChatGPT annotation evaluation), multimodal earnings call analysis, and foundational algorithm improvements (Extended Isolation Forest). His research consistently integrates spatio-temporal forecasting, community detection, and neural network optimization across disciplines. Scientific Recognition: Arthur Andersen Faculty Fellow Academic Leadership: Brunner advises doctoral candidates including Junhyung Edward Kim (recent PhD defense) and teaches cross-disciplinary courses (ASTR 330/406/496/596, INFO 490). He hosts the University Data Summit to address institutional data challenges and promotes data storytelling techniques across campus initiatives. Research Infrastructure: Leads the Laboratory for Computation, Data, and Machine Learning (LCDM@UIUC), driving NCSA technical white papers on big data implementations and fostering campus-wide data science collaboration.

• Complex systems
• Data analysis
• Statistical analysis
• +۷ مورد دیگر

Anna Filomena Carbone is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where she directs the NOISELAB research laboratory. Her academic career spans over three decades since earning her PhD in Physics in 1993, with significant contributions to interdisciplinary research at the intersection of physics, data science, and complex systems. Her research interests encompass complex systems, data analysis, statistical analysis, modeling and simulation, and time series analysis, with a particular focus on noise in physical systems. Dr. Carbone has authored over 100 articles in international journals and conference proceedings, with recent publications (2023-2025) demonstrating continued productivity in areas including financial time series analysis, urban scaling laws, quantum information, and brainwave dynamics. Her work shows a clear trend toward increasingly interdisciplinary applications of statistical physics methods to real-world complex systems across multiple domains. Member of the Board of the Nonlinear and Statistical Physics (NSP Board) of the European Physical Society (EPS) since 2009 Member of Scientific Committee - European Physical Society, Belgium (2009-2015) Editorial board member for multiple journals including Physica A, European Physical Journal B, and Chaos, Solitons and Fractals Program chair for multiple International Conferences on Data Science Challenges Dr. Carbone actively supervises PhD students (including Chiara Panico in Computer and Systems Engineering) and serves as Course Instructor for Quantum Information and Quantum Computing courses. Her research projects include TED4LAT (2022-2025), FuturICT 2.0 (2017-2020), and Challenges in Data Science (2016). She maintains active collaborations with institutions including the European Commission and the Department for Digital Transformation of the Presidency of the Council of Ministers.

Julen Astigarragaمشاهده پروفایل

پژوهشگر

• Forest Ecology
• Climate Change Impacts
• Global Change Biology
• +۷ مورد دیگر

Julen Astigarraga is a Visiting Research Fellow affiliated with Lund University's Department of Physical Geography and Ecosystem Science. He holds additional research positions with BECC (Biodiversity and Ecosystem services in a Changing Climate) and MERGE (ModElling the Regional and Global Earth system). His work focuses on ecological research examining global change impacts on forest dynamics through computational modeling and big data analysis of forest inventory systems. Dr. Astigarraga's research interests span forest ecology, climate change impacts, and ecosystem science with emphasis on human-forest interrelations. His work integrates computer modeling with large-scale forest inventory data to understand spatio-temporal trends in forest dynamics. Key research areas include drought response mechanisms, tree recruitment patterns across European forests, canopy structure analysis, and remote sensing applications for ecosystem monitoring. Recent publications demonstrate strong focus on hotter drought impacts, non-stationary forest responses, and innovative remote sensing tools. His research utilizes European forest inventory data, Sentinel satellite systems, and machine learning approaches to address climate change challenges in forest ecosystems. The work shows particular emphasis on Iberian Peninsula forests and continental-scale European forest dynamics. Affiliated with two major research projects: SilvaTrend (2025-2029) tracking European tree species demography from 1990-2050 where he serves as Principal Investigator, and Tree2Globe (2024-2029), a Horizon Europe project focused on global forest carbon sink reanalysis where he contributes as a researcher. His collaborative network spans multiple European countries with significant engagement in Spain-related forest research.

• Machine Learning
• Neuroscience
• Artificial Intelligence
• +۴ مورد دیگر

Vadym Gryshchuk is a PhD Fellow at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and Neurocomputing . His research focuses on adaptive AI systems, particularly addressing lifelong learning , catastrophic forgetting , and event-based data processing . Education: Master of Science in Computer Science His work spans neuroscience-inspired AI , with publications on EEG-based semantic analysis , self-supervised learning for medical classification , and modular neural architectures . Recent articles emphasize neurodegenerative disorder diagnostics and spatio-temporal data modeling . Research trends include: Lifelong learning from event-based data streams Patient-specific modeling for neurological conditions Pruning techniques to mitigate catastrophic forgetting Self-supervised learning for brain-computer interfaces Based in Copenhagen, Gryshchuk collaborates across computational and cognitive disciplines.

Hyunsun Leeمشاهده پروفایل

مدرس ارشد

• Statistical data analysis
• Stochastic branching process in wireless ad-hoc network
• Sound generation mechanism for aircraft engine nozzle
• +۲ مورد دیگر

Hyunsun Lee is a Senior Lecturer at the School of Design , University of Massachusetts Amherst , affiliated with the Department of Mathematics and Statistics . She contributes to the M.S. Option in Statistics and holds a PhD in Mathematics from Stony Brook University, alongside M.S. and B.S. degrees in Mathematics from Yonsei University. Education: PhD in Mathematics, Stony Brook University M.S. in Mathematics, Yonsei University B.S. in Mathematics, Yonsei University Research Interests span statistical data analysis, stochastic branching processes in wireless networks, aircraft engine sound generation mechanisms, homogenization for heterogeneous composites, and Rayleigh-Taylor instabilities. These areas intersect applied mathematics, fluid dynamics, and computational modeling. Scientific Awards include the 2017 Golden Apple Award for Excellence in Mentoring at Hawaii Pacific University and a 2021-22 Distinguished Teaching Award nomination at UMass Amherst.

• Evidence-based medicine
• Cardiovascular disease prevention
• Influences on prescribing practices
• +۳ مورد دیگر

Brett Montgomery is an Associate Professor and Senior Lecturer at the University of Western Australia (UWA) Medical School's General Practice department. He maintains dual roles as a General Practitioner at East Fremantle Medical Centre and an academic, coordinating third-year medical student teaching in the MD program. His academic career began with an academic registrar position at UWA in 2004, followed by GP training completion in 2006. His Master of Medical Science thesis explored statin prescribing patterns in Australia and Ireland. His research focuses on evidence-based medicine, cardiovascular disease prevention, and prescribing practices influenced by drug promotion. **Research Interests**: Brett’s work addresses critical areas such as the environmental impact of respiratory medications, prostate-specific antigen testing trends, and pediatric asthma management. His studies often bridge clinical practice and public health, emphasizing sustainable healthcare practices and patient-centered decision-making. Recent efforts include developing guidelines for prudent prescribing and reducing unnecessary medical imaging. **Awards & Grants**: He has received multiple accolades, including the UWA Medical School Award for Excellence in Teaching (2021) and the RACGP WA Faculty Budding Researcher Award (2011). His 2007 PHCRED/UWA Researcher Development Programme Fellowship supported his early career research. His articles span topics like asthma, COPD, and statin use, reflecting a commitment to advancing general practice standards. **Media & Outreach**: Brett has contributed to public health discourse through The Conversation, addressing topics like drug interactions and environmental impacts of healthcare. His work aligns with UN Sustainable Development Goals, particularly in reducing inequalities and promoting health equity.