جستجوی استاد راهنمای دانشگاهی

Marcin Mucha-Kruczynskiمشاهده پروفایل

مدرس ارشد

• Condensed Matter Physics
• Two-Dimensional Materials
• Graphene
• +۴ مورد دیگر

Dr Marcin Mucha-Kruczynski is a Senior Lecturer in the Department of Physics at the University of Bath, with affiliations to the Centre for Nanoscience and Nanotechnology, Condensed Matter Physics CDT, and the Centre for Nonlinear Mechanics. His research focuses on the electronic, optical, and mechanical properties of two-dimensional (2D) atomic crystals such as graphene and transition metal dichalcogenides, as well as van der Waals heterostructures. He explores phenomena like charge density waves, moiré superlattices, and twistronics. Education: Doctor of Philosophy in Physics (Theory of Bilayer Graphene Spectroscopy), Lancaster University (2007–2011) Master of Science in Material Engineering (Electronic band structure of graphene systems), Adam Mickiewicz University in Poznan (2002–2007) Research Interests: Electronic properties of 2D materials and their heterostructures Moiré superlattices, charge density waves, and topological phase transitions Angle-resolved photoemission spectroscopy (ARPES) and theoretical modeling Recent work highlights the role of van Hove singularities and anisotropic properties in controlling electronic behavior. His studies on graphene/h-BN heterostructures and rhenium dichalcogenides demonstrate expertise in interdisciplinary approaches combining theory and experiment. Active projects include ARPES studies of charge density wave materials and twistronic systems. He has secured grants from the Engineering and Physical Sciences Research Council (EPSRC) for projects on graphene/h-BN heterostructures and moiré band engineering. His collaborative networks span international institutions, focusing on 2D materials and their applications. Labs/Teams: Active in the Condensed Matter and Quantum Materials research group, contributing to experimental/theoretical collaborations via the University of Bath’s advanced nanoscience facilities.

Ioana Colfescuمشاهده پروفایل

پژوهشگر ارشد

• Climate Dynamics
• Machine Learning for Climate Prediction
• Extreme Events
• +۵ مورد دیگر

Dr. Ioana Colfescu is a Principal Research Fellow in Climate and Machine Learning at the University of St Andrews' School of Earth & Environmental Sciences, with a joint role at the National Centre for Atmospheric Science (NCAS). She leads the NCAS St Andrews research group, specializing in physics-informed machine learning for climate system analysis. Her academic roles include Digital Atmosphere Programme Leader at NCAS and teaching specialized atmospheric science courses. Colfescu holds a PhD in Climate Dynamics from George Mason University and degrees from the University of Bucharest. Her research focuses on large-scale climate dynamics (ENSO/AMO), machine learning applications for climate prediction, extreme events, and mountain meteorology, with notable contributions to North Sea emissions studies and mountain wave modeling. She has collaborated with institutions worldwide, including the National Centre for Atmospheric Research (USA) and universities in Italy, Spain, and Romania. Colfescu's fieldwork spans the Arctic, Iceland, and Italy, and she is a Certified Mission Scientist for airborne research. She actively mentors postgraduate students, emphasizing collaborative, cross-disciplinary approaches. Her work bridges fundamental climate science with applied impacts, supported by industry and international research partnerships.

Stephanie Barde-Cabussonمشاهده پروفایل

پژوهشگر

• Volcanology
• Hydrogeology
• Geophysics
• +۴ مورد دیگر

Stephanie Barde-Cabusson is a Permanent Researcher at Geo3bcn, part of the Spanish National Research Council (CSIC) in Barcelona, Spain. She specializes in volcanology and geophysics, with expertise in underground imaging and hydrogeological studies of volcanic systems. Her educational background includes: PhD in Earth Sciences from Université Blaise Pascal, Clermont-Ferrand II, France (2007) DEA (M.Sc.) in Magmatic and Metamorphic Processes and Volcanology from Université Blaise Pascal (2004) Licence and DEUG in Earth Sciences (B.Sc.) from Université Blaise Pascal and Université Claude Bernard, Lyon I (2002) Dr. Barde-Cabusson's research focuses on the structure, hydrogeology, and hydrothermal circulations of volcanoes, particularly volcanic islands and monogenetic volcanic fields. She employs near-surface geophysical methods, with a special emphasis on the Induced Polarization technique, to study underground parameters like water content, porosity, and clay content. Her work contributes to understanding volcanic processes, improving hazard assessment, and managing water resources in volcanic regions. Since 2022, she has expanded her research to include applications in natural hazards, archaeology, and civil engineering. Analysis of her recent publications (2019-2024) reveals a strong focus on geophysical methods applied to volcanic systems, particularly Induced Polarization and electrical conductivity measurements. Her research spans multiple volcanic locations including the Garrotxa Volcanic Field in Spain, Stromboli in Italy, Furnas in the Azores, and volcanic systems in Hawaii. There's a clear trend toward interdisciplinary approaches combining geophysics with hydrology and volcanology to address volcanic hazards and water resource management. Her scientific outreach and recognition include: Ambassador of Volcano Active Foundation since 2017 Member of NGO Volcan Explor Action since 2003 for volcanic risk education Community engagement with STEMulate project in Hawaii (2019) Multiple popularization articles in scientific magazines and websites Dr. Barde-Cabusson has been actively involved in volcanic risk education, including creating a volcanic risk education center in Arequipa, Peru, and collaborating with Peruvian geological institutions. Her field experience spans multiple continents and diverse volcanic settings. She leads research projects focused on volcanic monitoring and hydrogeophysical studies, working with international teams across Europe, Hawaii, and South America. Her laboratory work combines field measurements with advanced data analysis techniques to improve our understanding of volcanic systems.

Daniela Domeisenمشاهده پروفایل

دانشیار

• Atmospheric Dynamics
• Climate Predictability
• Stratosphere-Troposphere Interaction
• +۴ مورد دیگر

Daniela Domeisen is an Associate Professor of Atmospheric Processes at the Institute for Land Surface Dynamics (IDYST), University of Lausanne (UNIL). Her academic career includes roles as an Assistant Professor at GEOMAR Helmholtz Centre for Ocean Research Kiel and Christian-Albrechts-Universität zu Kiel, where she led the 'Atmospheric Dynamics and Predictability' group. She holds a PhD in Climate Physics and Chemistry from MIT, complemented by postdoctoral research at Cornell University and the University of Hamburg. Education: Bachelor's in Physics at ETH Zurich Master's in Climate and Society at Columbia University PhD in Climate Physics and Chemistry from MIT (2012) Mini-MBA from Harvard Business School (2012) Research Interests: Atmospheric fluid dynamics theory and predictability Vertical processes within the atmosphere Tropical-extratropical interactions Subseasonal forecasting (weeks-months) Rossby waves and their role in extreme weather Articles Trends: Recent work focuses on Rossby wave dynamics, stratospheric influences on weather extremes, and machine learning applications in forecasting. Key themes include heatwave predictability, teleconnections from climate oscillations (e.g., MJO, ENSO), and climate model improvements. Awards & Grants: ERC Starting Grant SNSF Professorship Grants & Advising: Leads projects on subseasonal heatwave prediction and European windstorm damage projections. Her work bridges theoretical atmospheric science with practical forecasting applications. Labs/Teams: Active in the UNIL IDYST research group and collaborates with institutions like ETH Zurich, MIT, and international meteorological organizations.

Qian Sunمشاهده پروفایل

دانشیار

• Urban Informatics
• Spatial Modelling
• GeoAI
• +۴ مورد دیگر

Dr. Qian (Chayn) Sun is an Associate Professor and Deputy Head of the School of Science at RMIT University, specializing in urban informatics, spatial modeling, and GeoAI. Her research integrates geospatial technologies with social-ecological theories to address climate change and urban sustainability challenges. She focuses on leveraging cloud computing, big Earth observation data, and AI to derive actionable insights for sustainable urban planning and environmental management. Research interests include urban heat island mitigation, drought risk assessment, and digital twin technologies for flood resilience. She has contributed to projects such as responsive flood monitoring systems, solar energy potential mapping, and health-integrated heat vulnerability assessments in Australian cities. Her interdisciplinary approach addresses complex human-environment interactions through quantitative geography and spatial data science. Education: PhD in Geospatial Science (implied by role). Industry Experience: Previously worked in government and engineering consultancies in New Zealand and Australia. Teaching interests span cloud-based GIS solutions, professional practice in science, and work-integrated learning. Dr. Sun actively supervises research projects on topics like urban flood resilience, sustainable building design, and disaster risk management in Pacific Island nations.

• Signal Processing
• Machine Learning
• Optimization Algorithms
• +۳ مورد دیگر

Enrique Alexandre Cortizo is a Full Professor at the Department of Signal Theory and Communications, Universidad de Alcalá. His research focuses on machine learning, optimization algorithms, and their applications in renewable energy systems, audio coding, and smart technologies. He leads the GHEODE research group, dedicated to modern heuristics and network design. He earned his doctorate in 2004 from Universidad de Vigo, with a thesis on audio coder information flow management, supervised by Dr. Miguel Antonio Peña Jimenez. His work spans computational intelligence in renewable energy, signal processing for hearing aids, and metaheuristic optimization techniques. Recent studies include explainable AI for smart agriculture, hybrid learning models in education, and sensor optimization for environmental monitoring. He has contributed to over 50 publications since 1999, addressing topics like wind energy systems, wave height prediction, and acoustic signal classification in hearing aids. Education: Ph.D. in Signal Theory, Universidad de Vigo (2004) Research Group: GHEODE (Modern Heuristics and Network Design) Key Areas: Renewable Energy Optimization, Machine Learning Applications, Audio Signal Processing His research emphasizes practical solutions for energy efficiency, environmental monitoring, and assistive technologies. He has pioneered methodologies for sensor placement in temperature and wind field reconstructions, as well as hybrid algorithms for feature construction in supervised learning.

• Machine Learning
• Climate Modeling
• Optimization Algorithms
• +۷ مورد دیگر

Jorge Pérez Aracil is an academic researcher at the Department of Signal Theory and Communications at the University of Alcalá. His work focuses on applying artificial intelligence and optimization algorithms to solve complex problems in climate science, renewable energy systems, structural engineering, and extreme event prediction. He leads the GHEODE research group, specializing in modern heuristic optimization techniques for network design and environmental modeling. His research interests span multiple disciplines including machine learning for weather prediction, metaheuristic algorithms for engineering design, vibration isolation systems, and the integration of AI with numerical models for climate analysis. Notable contributions include hybrid deep learning frameworks for energy forecasting, spatio-temporal analysis of droughts, and AI-driven models for understanding heatwaves and their socio-economic impacts. Recent work emphasizes extreme event modeling through projects like Spain on fire (wildfire risk assessment via satellite data) and Autoencoder-based flow-analogue methods for reconstructing heatwaves. He also explores pedagogical innovations, such as hybrid flipped/project-based learning models for AI-era education. Key technical contributions include the CRO-SL optimization algorithm, used in applications ranging from smart grid management to structural design optimization. His publications often bridge theory and practice, with a focus on explainability in AI systems and real-world implementation challenges.

Akanksha Guptaمشاهده پروفایل

پژوهشگر ارشد

• Geophysical Fluid Dynamics
• Environmental Fluid Dynamics
• Ocean Particle Transport
• +۳ مورد دیگر

Akanksha Gupta is a Research Fellow at the Scripps Institution of Oceanography (SIO), University of California, San Diego, where she collaborates with Dr. Falk Feddersen and Dr. Sarah Giddings on data assimilation for pathogen forecast models in the San Diego region. She received her Ph.D. from IIT Kanpur, India, in 2022 under the supervision of Dr. Anirban Guha. Her doctoral research investigated surface wave interactions with rippled seafloors and their impacts on tracer particles including pathogens, sediments, microplastics, algae, and nutrients. Her expertise spans environmental and geophysical fluid dynamics, with specialized focus on particle transport mechanisms in marine environments driven by surface/internal waves, mean flows, and topographic effects. Previously, from March 2023 to March 2025, she conducted postdoctoral research in SIO's Marine Physical Laboratory with Dr. Xuanting Hao and Dr. William R. Young.

Johna Rudzinمشاهده پروفایل

استادیار

• Air-sea interaction
• Tropical cyclone-ocean interaction
• Atmospheric boundary layers
• +۵ مورد دیگر

Dr. Johna Rudzin is an Assistant Professor in the Department of Geosciences at Mississippi State University with a joint affiliation with the Northern Gulf Institute. Her expertise lies in tropical cyclone-ocean interaction, atmospheric and oceanic boundary layer dynamics, and air-sea exchange processes. She utilizes a combination of in situ observations, satellite data, and numerical modeling to advance understanding of hurricane intensity change mechanisms. Education: Ph.D. in Meteorology and Physical Oceanography, University of Miami, 2012-2018 B.S. in Meteorology, The Florida State University, 2008-2012 Dr. Rudzin's research focuses on the complex interactions between tropical cyclones and the ocean, particularly how upper ocean processes influence hurricane intensity change. Her work examines atmospheric and oceanic boundary layer dynamics, the effects of salinity stratification on air-sea fluxes, and the role of surface ocean waves in tropical cyclone environments. She employs a holistic approach that bridges meteorology and oceanography to better understand the fully coupled air-sea system during hurricanes. Her research has important implications for improving hurricane intensity forecasting. Dr. Rudzin's publication record demonstrates a consistent focus on air-sea interactions in tropical cyclones, with recent work expanding into coupled modeling, wave processes, and the impacts of oceanic features like freshwater barrier layers on hurricane intensity. Her research combines observational analysis with numerical modeling approaches, addressing fundamental questions about momentum and heat exchange across the air-sea interface during extreme wind conditions. Scientific Awards: 2023 MSU College of Arts and Sciences Research Award for the Natural and Physical Sciences 2022 Office of Naval Research Young Investigator Program Awardee 2018 Barrett Prize for Best Dissertation on Latin American and Caribbean Topics from the University of Miami Institute for Advanced Study of the Americas Dr. Rudzin actively mentors graduate students, currently advising four students working on tropical cyclone-ocean interaction topics. She has secured competitive research funding, including the Office of Naval Research Young Investigator award. Her service includes membership on the AMS Air-Sea Interaction Committee and participation in NOAA working groups focused on ocean observations during extreme events. She is currently seeking a postdoctoral researcher to work on coupled tropical ocean-atmospheric-wave processes. Dr. Rudzin is an active member of research teams including the Ocean Observations Initiative Early Career Scientist Working Group and the NOAA GOMO Extreme Event Ocean Observations Task Team. Her collaborative approach bridges meteorological and oceanographic communities to address critical questions in tropical cyclone research.

Kumudu Amarawardhanaمشاهده پروفایل

مدرس

• Microgrid Systems
• Renewable Energy Integration
• Mission Engineering
• +۳ مورد دیگر

Dr. Kumudu Amarawardhana is a Lecturer at the Capability Systems Centre, UNSW Canberra, specializing in microgrid systems and renewable energy integration. She holds a PhD in Maritime Engineering from the University of Tasmania and an MSc in Sustainable Energy Engineering from the University of Gävle, Sweden. With over a decade of engineering experience in the Sri Lanka Air Force, her expertise spans mechanical, aeronautical, and explosive engineering. Currently, she leads projects in professionalizing mission engineering in defense and industries. Education: Doctor of Philosophy (Maritime Engineering), University of Tasmania (2023) MSc Sustainable Energy Engineering, University of Gävle (2014) BSc (Honours) Mechanical Engineering, University of Peradeniya (2001) Research Interests: Optimization of microgrid systems using advanced constraint handling techniques Integration of renewable energy sources into grid systems Development of mission engineering education frameworks and micro-credential models Recent Research Trends: Her work focuses on advancing microgrid optimization algorithms, educational innovation in mission engineering, and sustainable energy solutions. Key contributions include constraint handling methodologies for energy systems and curriculum design for interdisciplinary engineering practices. Labs/Teams: Her research is conducted within the Capability Systems Centre at UNSW Canberra, Room 242, Building 21.

Aurelien Fraisseمشاهده پروفایل

مدرس

• Cosmology
• Astrophysics
• Gravitational Lensing
• +۳ مورد دیگر

Aurelien Fraisse is a Research Scholar and Lecturer at Princeton University's Department of Physics, contributing to cosmology and astrophysics research. His work focuses on cosmic microwave background (CMB) observations using balloon-borne instruments like SPIDER and SuperBIT, gravitational lensing studies, and instrumental developments for high-altitude platforms. Office: Jadwin Hall 221 Email: afraisse@princeton.edu Key research interests include CMB polarimetry, weak gravitational lensing, galactic dust composition, and the design of stratospheric observatories. Fraisse has led contributions to the SPIDER and CMB-S4 projects, advancing instrumentation for detecting primordial gravitational waves and characterizing cosmic foregrounds. His recent work emphasizes improving data retrieval from high-altitude balloons and optimizing observational strategies for suborbital telescopes. Publications span technical advancements (e.g., TES arrays, in-flight monitoring) and cosmological analyses using Planck and SPIDER datasets. Research collaborations include the SuperBIT telescope (commissioned in 2019), enabling diffraction-limited visible-to-near-UV imaging from stratospheric balloons, and refining CMB power spectrum estimation methods for future surveys.

Hafeez Ullah Aminمشاهده پروفایل

مدرس ارشد

• Neuroimaging
• Biomedical Signal Processing
• EEG Signal Processing with Machine Learning
• +۳ مورد دیگر

Hafeez Ullah Amin is a Senior Lecturer in the School of Computer Science at Edge Hill University, UK. Previously, he served as an Assistant Professor at the University of Nottingham Malaysia Campus (2020–2023) and held various research positions at Universiti Teknologi PETRONAS (UTP), including Postdoctoral Researcher and Research Scientist. He holds a PhD in Electrical and Electronic Engineering from UTP, specializing in EEG Signal Processing with Machine Learning. His expertise spans Neuroimaging, Biomedical Signal Processing, and Applied AI/ML in healthcare and education. Education: PhD (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, 2011–2015 MPhil (Computer Science with AI), Kohat University of Science and Technology, 2006–2009 BSc (Information Technology), Kohat University of Science and Technology, 2001–2005 Research Interests: Neuroimaging, EEG Signal Processing, AI in Mental Healthcare, Data Analytics, and Machine Learning applications in education and business. His work intersects neuroscience, biomedical engineering, and computational methods to address challenges in memory assessment, stress mitigation, and learning efficacy. Publications: Over 50 articles in high-impact journals and conferences, including work on EEG-based neurofeedback, machine learning for healthcare analytics, and 3D educational content efficacy. Recent trends focus on EEG functional connectivity, fraud detection via graph autoencoders, and energy forecasting. Awards: Senior Member, IEEE Fellow (FHEA), Advance HE, UK Advising & Grants: Supervises PhD students in AI/ML applications. Active in research grants involving EEG experiments, patent on LTM assessment via EEG, and collaborations in smart cities and energy systems. Teaching: Leads courses in Data Science, AI, Microprocessor Systems, and Knowledge Representation. Past roles include Module Leader for Programming Principles and Techniques. Labs/Tech Teams: Associated with Edge Hill's Data and Complex Systems Research Centre and the International Centre for Applied Research in Education.

• Probability
• Statistics
• Fractional Calculus
• +۷ مورد دیگر

Mark M. Meerschaert is a University Distinguished Professor in the Department of Statistics and Probability at Michigan State University. He holds a Ph.D. from the University of Michigan and has taught at multiple institutions including the University of Nevada, Reno, and the University of Otago in New Zealand. His research focuses on probability, statistics, fractional calculus, anomalous diffusion, and applications in fields like groundwater flow and medical imaging. His academic journey includes professional experience as a systems analyst at Vector Research, Inc., and extensive collaborations across disciplines. Research interests span fractional partial differential equations, continuous time random walks, heavy-tailed models in finance, and periodic covariance structures in river flows. Notable contributions include textbooks on Mathematical Modeling and Stochastic Models for Fractional Calculus . Recent work involves unified spectral methods for fractional PDEs, sensitivity analysis in phase-field modeling, and parsimonious climate data modeling. His publications reflect interdisciplinary applications of fractional calculus in physics, engineering, and environmental science. Academic accolades include a University Distinguished Professor title at MSU. Collaborations extend to departments like Mechanical Engineering and Computational Mathematics at MSU, emphasizing applied and computational research.

Edwin Gerberمشاهده پروفایل

استاد

• Climate variability and change
• Atmospheric dynamics
• Stratosphere-troposphere interactions
• +۳ مورد دیگر

Edwin Gerber is a Professor of Mathematics and Atmosphere/Ocean Science at New York University's Courant Institute of Mathematical Sciences, affiliated with the Department of Environmental Studies and Center for Data Science. He holds a Ph.D. from Princeton University (2006), focusing on Applied and Computational Mathematics. His research centers on understanding climate variability and atmospheric dynamics, particularly stratosphere-troposphere interactions and improving climate models through idealized studies. He has contributed to the Dynamics and Variability Model Intercomparison Project (DynVarMIP) for CMIP6 and received the Friedrich Wilhelm Bessel Research Award (2021). Key research interests include the role of the upper atmosphere in surface climate, gravity wave parameterization, and the response of jet streams to global warming. Gerber has authored over 100 publications, including studies on stratospheric ozone dynamics, sudden stratospheric warmings, and machine learning applications in climate modeling. His work bridges theory and complex Earth system models to enhance climate prediction accuracy. Notable awards include the Friedrich Wilhelm Bessel Award and Hertz Foundation Fellowship. He served as Director of Graduate Studies for the Atmosphere Ocean Science PhD program and Associate Editor of the Quarterly Journal of the Royal Meteorological Society. His grants include NSF-funded projects on gravity wave parameterization and stratospheric dynamics.

Merrick Hallerمشاهده پروفایل

استاد

• Nearshore Remote Sensing
• Coastal Engineering
• Surface Wave Transformation
• +۱۰ مورد دیگر

Merrick Haller is a Professor in the School of Civil and Construction Engineering at Oregon State University, College of Engineering. He holds a joint appointment and is actively involved in coastal and ocean engineering research and education. His work is centered at the interface of remote sensing and coastal dynamics, with strong institutional support and collaboration across multiple agencies. Education: Ph.D., Civil Engineering, University of Delaware, 1999 M.C.E., Civil Engineering, University of Delaware, 1996 B.S., Earth & Atmospheric Sciences (Geophysics), Purdue University, 1993 Professor Haller’s research focuses on developing and applying remote sensing tools—particularly X-band marine radar—to study coastal ocean processes. His primary interests include nearshore remote sensing, wave transformation, rip currents, sediment transport, wave-current interaction, and hazardous waves at navigational inlets. His teaching encompasses hydraulics, wave mechanics, and coastal engineering. He previously coordinated the coastal and ocean program (2003–2010) and served as associate head of the School for seven years. The trends in his research, as reflected in recent and representative publications, emphasize real-time monitoring, data assimilation, and predictive modeling using radar and optical systems. Key themes include bathymetry estimation (e.g., cBathy), wave forecasting for wave energy applications, and submesoscale frontal dynamics. His work integrates field deployments, laboratory experiments, and numerical modeling, often in collaboration with NOAA, ONR, and USACE. Scientific Awards: 2007 Editors' Citation for Excellence in Refereeing, Journal of Geophysical Research-Oceans 2002 Editors' Citation for Excellence in Refereeing, Journal of Geophysical Research-Oceans Professor Haller leads the Haller Research Group and has advised numerous Ph.D. and M.S. students, many of whom have pursued careers in coastal engineering, government agencies, and academia. His research has been funded by the Office of Naval Research, Department of Energy, National Science Foundation, NOAA, Sea Grant, and the U.S. Army Corps of Engineers. He is a key contributor to major projects such as DARLA and NANOOS, involving radar deployments at New River Inlet, Cape Disappointment, and Yaquina Bay. He also collaborates with the Coastal Imaging Laboratory and the University of Washington’s Applied Physics Laboratory. Labs and Research Teams: He is affiliated with the Haller Research Group, the Nearshore Remote Sensing Group, and the DARLA project team. His group operates the Wave Imaging Marine Radar (WIMR) system and contributes to the National Marine Renewable Energy Center (now Pacific Marine Energy Center). Field sites include the Columbia River mouth, Newport, and New River Inlet, where radar and camera systems are deployed for coastal monitoring.