جست‌وجوی استاد و پژوهشگر

Erhan Yumukمشاهده پروفایل

پژوهشگر

• Control Theory
• Fractional Order Systems
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Erhan Yumuk is a researcher at Ghent University and an alumnus of Istanbul Technical University , where he earned his Ph.D. in Control and Automation Engineering . He has served as a lecturer and deputy head of the department at Istanbul Technical University. His research focuses on: Fractional order control systems Anesthesia depth and hemodynamic control Pharmacokinetics/pharmacodynamics modeling Industrial control laboratory development Remote access control education Artificial intelligence in biomedical applications Recent publications (2024-2025) demonstrate expertise in fractional order PID controllers for time-delay systems, obesity-specific drug distribution models, and AI-enhanced pain monitoring during anesthesia. His work spans mathematical control theory, clinical implementation, and industrial applications. Scientific Recognition: Best Paper Award, IEEE Control System Society (2015) Erhan Yumuk contributes to Journal of Process Control , IFAC Journal of Systems and Control , and Applied Sciences , with emerging leadership in postdoctoral research at Ghent University.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +۶ مورد دیگر

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.

• Robotics
• Control Systems
• Unmanned Aerial Vehicles
• +۳ مورد دیگر

Pascal Morin serves as a University Professor at Sorbonne University within the Faculty of Science and Engineering. He is an active member of the ASIMOV research team at the Intelligent and Robotic Systems Institute (ISIR), located at 4 Place Jussieu in Paris. His office H17 serves as the base for his research activities in advanced robotics and control systems. Professor Morin's research focuses on cutting-edge robotics applications, particularly in unmanned aerial vehicle navigation and control systems. His work spans nonlinear control theory, visual servoing techniques, and micro air vehicle development. Key interests include homography estimation for image stabilization, thermal anemometry for airflow sensing, and obstacle avoidance algorithms for precision robotics. His publications demonstrate consistent innovation in combining theoretical control frameworks with practical robotic implementations. Analysis of his 15 most recent publications reveals strong thematic continuity in UAV navigation systems, with increasing integration of deep learning techniques since 2020. His work consistently addresses GPS-denied environments and sensor fusion challenges, with notable contributions to thermal-based odometry and visual-inertial navigation systems. The research trajectory shows progression from theoretical control frameworks toward increasingly complex real-world applications including power line inspection and microscopy manipulation. Professor Morin actively collaborates with major aerospace institutions including ONERA and DLR, as evidenced by his participation in the EuRoC challenge. His editorial work on MAV navigation special collections demonstrates leadership in the micro air vehicle research community. While specific grant details aren't provided in the source material, his sustained publication output across multiple high-impact journals indicates successful funding acquisition. As a core member of the ASIMOV team at ISIR, Professor Morin contributes to Sorbonne University's leadership in robotics research. The team maintains strong industry connections through publications in Aerospace Lab and collaborations with organizations like AIAA. Current research directions appear focused on thermal sensor integration for MAVs and nonlinear control solutions for challenging operational environments.

Scott Hoskingمشاهده پروفایل

عضو هیئت علمی

• AI for Environmental Science
• Climate Change
• Machine Learning Applications in Meteorology
• +۷ مورد دیگر

Scott Hosking is a Senior Research Fellow at British Antarctic Survey (BAS) and The Alan Turing Institute , serving as Mission Director for Environmental Forecasting and Head of the BAS AI Lab. His work bridges AI innovation with critical climate science challenges, particularly in data-sparse polar regions. PhD in Climate Science from University of Cambridge (2005-2009) Established BAS AI Lab (2018) Co-Director of AI4ER Doctoral Training Programme (2019-2023) Research focuses on: AI-powered environmental forecasting systems Digital twin technologies for planetary monitoring Machine learning in climate modeling Sea ice and wildlife population tracking Reproducibility in geoscientific research Recent publications span weather prediction , precipitation downscaling , and extreme event analysis , with articles appearing in Nature , Nature Communications , and Geophysical Research Letters . Key projects include IceNet (Arctic sea ice forecasting) and Aardvark Weather (end-to-end AI weather prediction). Scientific recognition includes: The Laws Prize (BAS, 2017) Designation as AI for Good Exemplar (UK government, 2023) Inclusion in Nature Communications 25 most downloaded Earth science papers (2021) Secured over £25M in research funding as Principal Investigator, including projects with NERC , EPSRC , and ESA . Collaborates with institutions across Cambridge , Lund , and UiT The Arctic University .

Xiaolin Huمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems Science
• Agent-Based Systems
• +۳ مورد دیگر

Xiaolin Hu is a Professor in the Department of Computer Science at Georgia State University within the College of Arts & Sciences. His research focuses on modeling and simulation theory with applications in complex systems science, agent-based systems, and advanced computing environments. Ph.D. in Electrical and Computer Engineering, University of Arizona (2004) NSF CAREER Award recipient Senior member of IEEE and Society for Modeling and Simulation International (SCS) Associate editor for ACM Transactions on Modeling and Computer Simulation (TOMACS) and other journals SIMULATION: Transactions journal and International Journal of Modeling, Simulation, and Scientific Computing (IJMSSC) Dr. Hu's research spans three major areas: Dynamic Data-Driven Simulation (DDDS): Development of simulation frameworks that assimilate real-time data, particularly applied to wildfire spread prediction and autonomous systems. Multi-Agent Systems: Study of adaptive behaviors and coordination mechanisms for unmanned aircraft and social/public health systems. Advanced Computing: Implementation of parallel and cloud computing solutions for large-scale simulations. His recent publications (2025-2023) demonstrate a strong focus on integrating real-time data from UAVs and satellite systems into wildfire simulations, utilizing Bayesian methods for sequential data assimilation, and developing cloud-based simulation services. These works bridge computational modeling with environmental science applications. Scientific recognitions include: National Science Foundation (NSF) CAREER Award Dr. Hu has chaired multiple international conferences in modeling and simulation and leads the SIMS Lab at Georgia State University, which specializes in simulation technologies and their applications in diverse domains.

Andrei Volodinمشاهده پروفایل

استاد

• Statistical Inference
• Limit Theorems
• Distributions on Abstract Spaces
• +۱۲ مورد دیگر

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

Rafael Ruizمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Vibration Control
• Piezoelectric Energy Harvesting
• +۲ مورد دیگر

Rafael Ruiz is an Assistant Professor of Mechanical Engineering at the University of Michigan-Dearborn. His research develops computational methods for uncertainty quantification and optimization of mechanical systems, with applications in energy harvesting and structural dynamics. Research domains include: Stochastic modeling of material behavior Bayesian inference for engineering systems Vibration-based energy harvesting Multifunctional metamaterials His publications demonstrate strong emphasis on probabilistic approaches for piezoelectric energy harvester design, particularly for infrastructure monitoring applications. Recent work integrates machine learning with physical models for improved reliability predictions.

Cristina Archerمشاهده پروفایل

استاد

• Renewable Energy
• Wind Power
• Meteorology
• +۳ مورد دیگر

Cristina L. Archer is a Professor and Unidel Howard Cosgrove Career Development Chair in Environment at the University of Delaware. She holds a split appointment between the Department of Geography and Spatial Sciences and the Department of Mechanical Engineering. Her roles include Director of the Center for Research in Wind (CReW) and Faculty Director of the Eco-Entrepreneurship certificate program. She earned her Ph.D. in Civil and Environmental Engineering from Stanford University (2004), an M.S. in Meteorology from San Jose State University (1998), and an M.S. in Civil and Environmental Engineering from Politecnico di Milano (1995). Her research focuses on renewable energy, wind power, meteorology, climate change, air quality, and numerical modeling of atmospheric processes. She leads the Atmosphere and Energy Research Group (AERG), advising numerous PhD students and postdocs. Her work explores wind energy impacts on climate, air quality, and hurricane dynamics, with contributions to wind farm optimization and energy-food nexus studies. Dr. Archer’s research has been published in journals like *Monthly Weather Review*, *Environmental Research Letters*, and *Applied Energy*. She serves on editorial boards for *Meteorological Applications* and *Bulletin of the Atmospheric Science and Technology*. Her lab, CReW, investigates technical, environmental, and policy dimensions of wind energy deployment. Grants & Funding: Extensive support for wind energy research, including studies on offshore wind impacts, hurricane mitigation via turbines, and climate modeling. Labs/Teams: Center for Research in Wind (CReW), Atmosphere and Energy Research Group (AERG). Advising: Mentored over 20 graduate students and postdocs since 2012, with notable contributions to wind farm modeling, air quality, and energy policy.

Hang Zhouمشاهده پروفایل

پژوهشگر ارشد

• Functional and high-dimensional data analysis
• Complex-structured data analysis
• ODE/PDE modeling
• +۳ مورد دیگر

Hang Zhou is a Post-Doctoral Scholar at the University of California, Davis, specializing in advanced statistical and computational methodologies. His research focuses on functional and high-dimensional data analysis, complex-structured data including manifold and Wasserstein types, and ODE/PDE modeling. He is affiliated with the Mathematical Sciences Building at UC Davis. His research interests encompass functional data analysis, high-dimensional statistics, and the integration of differential equations into modeling frameworks. He explores complex-structured data such as manifold and Wasserstein geometries, advancing methodologies for analyzing distribution-valued processes and error detection in numerical data. Zhou’s recent work bridges machine learning and statistical theory, exemplified by studies on deep regression for repeated measurements and event-attended graph ODE frameworks. His research also emphasizes conformal inference and optimal transport representations, reflecting a focus on robust statistical methods and interdisciplinary applications in data science. As a postdoctoral researcher, Zhou has not yet listed advisees or grants in the provided information. His website ( https://hg-zh.github.io/ ) provides additional details on his ongoing projects and collaborations.

Roozbeh Kianiمشاهده پروفایل

استاد

• Decision Making
• Neuroscience
• Psychology
• +۵ مورد دیگر

Roozbeh Kiani is a Professor of Neural Science and Psychology at New York University's College of Arts and Science. His research focuses on decision-making processes and their neural underpinnings, combining electrophysiological recordings, fMRI, and computational modeling. He holds a Ph.D. from the University of Washington (2009) and leads a lab studying perceptual and mnemonic decision mechanisms. Key areas include neural integration of sensory evidence, confidence estimation, and the role of prefrontal and parietal cortices. Education: Ph.D. in Neuroscience, University of Washington (2009). Research emphasizes understanding how the brain integrates information to form decisions, with applications to cognitive disorders. His lab employs advanced techniques like single-neuron recordings and causal manipulation of neural circuits to dissect decision dynamics. Current projects explore hierarchical decision-making, neural representations of choice confidence, and the impact of perturbations on circuit function. Students advised include Isabella Rischall (PhD student) and Chenghao Zhou (Master’s student). Collaborations span computational neuroscience, biomedical engineering, and cognitive psychology. Publications highlight topics like recurrent neural circuit compensation (2024), functional causal flow predictions (2023), and Bayesian models of perceptual confidence (2021). His work bridges cellular mechanisms with behavioral outcomes, aiming to inform treatments for cognitive impairments.

Natalie Harveyمشاهده پروفایل

پژوهشگر

• Atmospheric Transport Processes
• Volcanic Ash Modeling
• Forecast Verification
• +۳ مورد دیگر

Dr. Natalie Harvey is a Senior Research Scientist at the Department of Meteorology, University of Reading. She specializes in atmospheric transport processes, volcanic ash dispersion modeling, and uncertainty quantification in natural hazard forecasts. Her work focuses on improving volcanic ash forecasting through advanced model techniques and satellite data integration. She is affiliated with projects like R4Ash, IMPALA, and RACER, addressing volcanic hazards and climate-related risks. Her research interests include boundary layer dynamics, remote sensing applications, and decision-making under uncertainty. She has contributed to over 30 peer-reviewed articles, analyzing volcanic eruptions such as Raikoke 2019 and Grímsvötn 2011. Her methods enhance forecast accuracy by integrating ensemble meteorology and source inversion techniques. Notably, she explores how AI models compare to traditional physics-based approaches in weather prediction, highlighted in a 2024 study on Storm Ciarán. Dr. Harvey holds a PhD from the University of Reading (Boundary-layer type classification and pollutant mixing) and has developed classification algorithms for atmospheric layers using Doppler lidar. She collaborates with institutions like the Met Office and has received international recognition for her work in volcanic ash transport and dispersion modeling.

Robin Smithمشاهده پروفایل

دانشیار

• Earth System Modeling
• Ice-Sheet-Climate Interactions
• Sea-Level Rise
• +۳ مورد دیگر

Dr. Robin Smith is an Associate Professor in the Department of Meteorology at the University of Reading, affiliated with the National Centre for Atmospheric Science (NCAS). He specializes in Earth System modeling, particularly focusing on ice-sheet-climate interactions, sea-level rise, and the stability of the Atlantic Meridional Overturning Circulation. His work involves developing and applying models such as UKESM and FAMOUS to understand past and future climate dynamics. Research Interests: Earth system model development Ice-sheet-climate interactions Paleoclimate studies, especially glacial cycles Geoengineering impacts on climate systems Key Projects: Lead developer for UKESM's ice sheet modeling capabilities Manager of the FAMOUS climate model Involved in ISMIP6, TerraFIRMA, and OCEAN:ICE projects Advising & Grants: Supervises PhD students including Steve George and Charlotte Lang Recipient of EU and NERC grants for climate modeling and paleoclimate research

Steven Yu-Pang Chuoمشاهده پروفایل

مدرس

• Railway intervention planning
• Asset management optimization
• Digitalization in infrastructure
• +۳ مورد دیگر

Steven Yu-Pang Chuo is a Lecturer at ETH Zurich’s Department of Civil, Environmental and Geomatic Engineering, affiliated with the Infrastructure Management group. His research focuses on railway intervention planning, digitalization of asset management, and prefabrication supply chain optimization. He holds a BASc (2018) and MASc (2020) from the University of Waterloo, with a specialization in Management Sciences and Structural Engineering. His work bridges data analysis and practical decision-making, aiming to enhance efficiency in infrastructure projects. Education Bachelor of Applied Science (BASc) in Civil Engineering with Honors, University of Waterloo (2018) Master of Applied Science (MASc), University of Waterloo (2020) Research Interests Chuo’s research emphasizes railway asset management, including intervention planning optimization, digital twin integration with BIM, and risk mitigation in construction projects. He develops predictive models for asset condition assessment and automates decision-support tools for infrastructure stakeholders. His work on prefabrication supply chains addresses spatiotemporal planning challenges in diverse building environments. Teaching & Supervision He teaches Infrastructure Management 1: Process and supervises master’s theses on topics like BIM integration with intervention planning and railway asset recycling. Recent projects include optimizing copper recycling at SBB and modeling small railway networks. Labs & Teams Chuo is part of the Chair of Infrastructure Management at ETH Zurich, collaborating on initiatives like the STABILITY project to digitize railway planning processes. He actively contributes to industry partnerships in risk consulting and asset management.

François Bouffardمشاهده پروفایل

دانشیار

• Power Engineering
• Smart Grids
• Systems and Control
• +۳ مورد دیگر

François Bouffard is an Associate Professor at McGill University's Faculty of Engineering, specializing in Power Engineering and Systems Control. He holds the William Dawson Scholar title and serves as Associate Chair (Undergraduate Affairs). His research focuses on smart grids, renewable energy integration, and advanced control systems. Research interests include optimizing power systems through data-driven methods, demand response mechanisms, and energy storage solutions. He contributes to projects like the Group for Research in Decision Analysis (GERAD), emphasizing sustainable energy systems and grid resilience. Recent work explores flexibility in smart grid architectures, cold load management, and multi-agent reinforcement learning for energy systems. His publications highlight optimization techniques, stochastic modeling, and machine learning applications in energy control. Awarded the William Dawson Scholar distinction, his work bridges theoretical advancements with practical grid operations. He collaborates on interdisciplinary projects addressing energy transition challenges, such as hybrid storage systems and distributed energy resource coordination.

Yann Quilcailleمشاهده پروفایل

پژوهشگر

• Climate Extremes
• Climate Emulators
• Climate Modeling
• +۳ مورد دیگر

Dr. Yann Quilcaille is a Researcher at ETH Zurich's Department of Environmental Systems Science, affiliated with the Professorship for Land Climate Dynamics. His work focuses on climate extremes, climate emulators, and socio-economic integration of climate models. He holds a PhD from Université Paris-Saclay and previously served as a research scholar at IIASA (Austria), advancing the OSCAR climate model. Key contributions include the MESMER-X emulator for spatially resolved climate extremes and research on fire weather, extreme event attribution, and climate overshoot scenarios. Dr. Quilcaille's research integrates physics-based and statistical approaches to develop low-cost climate emulators, aiming to bridge climate modeling with socio-economic frameworks. His expertise spans heatwaves, droughts, and fires, with a focus on global and regional climate impacts. Notable projects include analyzing fire weather overlaps between continents and quantifying carbon majors' contributions to historical heatwaves. His academic journey includes studies at Ecole Normale Supérieure de Paris-Saclay (Theoretical Physics), AgroParisTech (Economics of Sustainable Development), and Université Pierre & Marie Curie (Ocean, Atmosphere, Climate). He has authored over 50 publications on climate modeling, emulation techniques, and policy-relevant climate science.