جست‌وجوی استاد و پژوهشگر

Carlos E. Oteroمشاهده پروفایل

استاد

• Hardware Interfacing
• Embedded Systems
• Zero-trust Systems
• +۷ مورد دیگر

Dr. Carlos E. Otero is a tenured Professor in the Department of Electrical Engineering and Computer Science at Florida Institute of Technology (FIT). He serves as Technical Director of the Wireless Center of Excellence (WiCE) and the Center for Advanced Data Analytics and Systems (CADAS). Previously, he held positions at the University of South Florida and the University of Virginia’s College at Wise. His expertise includes hardware interfacing, embedded systems, zero-trust architectures, and AI-driven systems engineering. Education: Earned BS, MS, and PhD in computer science/engineering from Florida Institute of Technology. His research focuses on cyber-physical systems, wireless security, UAV platforms, and model-based systems engineering, with over $5M in funded projects and 90+ peer-reviewed publications. He authored the textbook Software Engineering Design: Theory and Practice . Research Interests: Embedded systems, cybersecurity, AI, and systems engineering. Notable projects include visibility forecasting with deep learning, acoustic situational awareness, and optimizing sensor networks for infrastructure monitoring. Awards: Charles Helmstetter Award for Research Excellence (FIT), Northrop Grumman Honor Roll of Inventors. Active in IEEE and ACM editorial roles. Teaches courses on algorithms, machine learning, and wireless communications, emphasizing real-world applications in mission-critical systems. Lab & Teams: Leads WiCE and CADAS, focusing on applied research in wireless technologies and data analytics. Collaborates with industry on defense contracting projects involving satellite communications and UAV systems.

Mohamed Iskandaraniمشاهده پروفایل

استاد

• Oceanography
• Climate Modeling
• Data Assimilation
• +۳ مورد دیگر

Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems. Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia. Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review , his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.

• Thermodynamics
• Heat Transfer
• Fluid Dynamics
• +۴ مورد دیگر

Marco Cavazzuti is an Associate Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia. His research focuses on thermodynamics, fluid dynamics, and energy systems, with a particular emphasis on computational fluid dynamics (CFD), heat transfer, and renewable energy applications. Cavazzuti teaches courses in Energy Engineering, Thermofluid Dynamics, and Industrial Heat Engineering, integrating theoretical and practical components with computational tools like OpenFOAM and Python. His research interests include multi-scale modeling of respiratory droplet transport for infectious disease risk assessment, plate-fin heat exchanger optimization, and thermal management of electric motors. He has contributed to projects like the IDEAS initiative for multi-source heat pump systems and the analysis of tunnel ventilation systems using 1D finite volume models. Cavazzuti's work spans both academic and industrial collaborations, addressing challenges in energy efficiency, CFD validation, and thermal system design. Key research trends in his publications include CFD-based modeling of complex flows (e.g., micro-channels, swirled jets), epidemiological droplet transport analysis, and optimization of thermal systems for renewable energy integration. He has also explored applications in automotive chassis design and cardiovascular flow dynamics using ultrasound and computational methods.

Keith Pembletonمشاهده پروفایل

استاد

• Agricultural Systems Modelling
• Crop Modelling
• Agronomy
• +۷ مورد دیگر

Professor Keith Pembleton is the Director of the Centre for Sustainable Agricultural Systems at the University of Southern Queensland (USQ), affiliated with the Institute for Life Sciences and the Environment. He holds a BAgSc(Hons) from the University of Queensland (2005) and a PhD from the University of Tasmania (2010). His career includes roles as Professor (2024–present), Associate Professor (2019–2023), and Senior Lecturer (2017–2019) at USQ, with prior experience as a Senior Research Fellow and Research Fellow. His research focuses on integrating computational tools like APSIM and AI into agricultural decision-making, addressing challenges in crop modeling, catchment systems, and climate adaptation. Research interests span agricultural systems modeling, crop and pasture agronomy, and climate resilience. He has led over 15 grants, including projects on sesame adoption in farming systems, soil constraint management, and drought resilience. Awards include the 2011 Minister for Agriculture award and Fulbright Scholarships. Professor Pembleton supervises doctoral and master's research on topics like farming systems modeling and ICT adoption. His 100+ publications include work on APSIM validation, tropical pasture growth, and machine learning applications in agriculture. He is a Fellow of the Higher Education Academy and active in professional organizations like the Australian Society of Agronomy.

Fred Maierمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Agriculture Technology
• +۴ مورد دیگر

Fred Maier serves as Associate Director of the Artificial Intelligence Institute and Assistant Research Scientist at the University of Georgia, with a Courtesy Professor appointment in Computer Science. His interdisciplinary research bridges artificial intelligence with practical applications across agriculture, environmental science, and healthcare. Research interests focus on machine learning applications in diverse domains: Agricultural technology : Crop yield prediction, biomass estimation, and precision farming using computer vision and data synthesis Human activity recognition : Wearable sensor data analysis for health monitoring and behavior classification Environmental forecasting : Solar radiation prediction and forest management optimization Semantic technologies : Knowledge representation and ontology engineering for intelligent systems Publication trends reveal consistent focus on practical AI applications, with recent emphasis on agricultural analytics (alfalfa yield prediction, cotton boll counting) and human activity recognition techniques. Earlier work demonstrates foundations in semantic web technologies and logic-based AI systems.

Steffen Meyerمشاهده پروفایل

استاد

• Household Finance
• Investor Behavior
• Environmental Factors in Decision Making
• +۳ مورد دیگر

Steffen Meyer is a Full Professor of Finance at Aarhus University and a member of the Danish Finance Institute (DFI). His research focuses on household finance, investor behavior, and the impact of environmental factors on financial decisions. He has held academic roles since 2010, contributing to groundbreaking studies on behavioral finance and economic policy analysis. Research Interests Investor decision-making under ambiguity and volatility Environmental and health factors affecting financial behavior Impact of financial advice and regulatory policies Climate change adaptation in economic systems Key Contributions Meyer’s work includes landmark studies on investor responses to market shocks (e.g., forced fund liquidations, epidemics) and the role of air quality in productivity. His research on ETFs revealed pitfalls in passive investing strategies. He co-led the Carlsberg Foundation’s CentR-A initiative (€1.3M budget), advancing decision-making under risk and ambiguity. Awards & Grants Best Paper of the Year Award (2013) for work on financial advice efficacy Carlsberg Foundation Grant (2024) for the CentR-A research center Teaching & Engagement Meyer teaches courses on corporate finance, asset management, and household finance at both undergraduate and executive levels. His teaching portfolio includes supervising 26 bachelor’s and 8 master’s theses, as well as mentoring PhD students like D. Kostopoulos. Media & Outreach His research has been featured in outlets like the Washington Post, Handelsblatt, and Morningstar, addressing topics such as ETF risks, investor psychology, and climate policy implications.

Dominik André Strebelمشاهده پروفایل

مدرس

• Urban Climate Modeling
• Mesoscale Meteorology
• Machine Learning Applications
• +۵ مورد دیگر

Dominik André Strebel is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Chair of Building Physics. His research focuses on urban climate modeling, mesoscale meteorology, and machine learning applications in environmental systems. He holds a MSc in Engineering (Energy and Environment) from HSR Rapperswil (now OST), where his studies emphasized climate models for renewable energy forecasting and Smart Grids. His Master’s thesis involved developing a coupled climate and multiphysics model for overhead power lines in collaboration with Swissgrid, alongside improving weather forecasts using UAV data integration in WRF models. Research Interests: Urban Heat Island simulation and mitigation Mesoscale meteorological modeling (WRF, COSMO) Machine learning for urban climate analysis High-Performance Computing (HPC) and Data Science GIS and mathematical modeling for urban environments Key Contributions: Developed frameworks coupling WRF-UCM-SOLWEIG for thermal comfort mapping at city scale Advanced methods for quantifying urban climate drivers (e.g., LCZ analysis) Improved mesoscale predictions using hybrid ML and sensor data Explored intra-urban warming patterns in heatwaves across multiple cities Current Projects: Hybrid machine learning-mesoscale modeling for urban climate prediction Mapping heat exposure indices in mid-latitude cities Urban morphology clustering for identifying heat-vulnerable neighborhoods

• Earth System Data Assimilation
• Ensemble Filter Algorithms
• High-Performance Computing
• +۳ مورد دیگر

Lars Nerger is the Head of the Data Assimilation Team within the Computing and Data Centre at the Alfred Wegener Institute (AWI). He specializes in Earth system data assimilation, focusing on ensemble filter algorithms and high-performance computing. His work integrates advanced computational methods with climate and ocean modeling to improve predictive capabilities. Research interests include ensemble Kalman filtering, non-linear systems, and scientific computing frameworks. He leads the development of the Parallel Data Assimilation Framework (PDAF), a widely used open-source tool for data assimilation in geoscience applications. His contributions enhance model accuracy in oceanography, climate dynamics, and cryosphere studies. Recent articles highlight advancements in data assimilation for ocean biogeochemistry, coupled climate models, and sea ice prediction systems. Collaborations span atmospheric chemistry, paleoclimatology, and environmental monitoring, reflecting his interdisciplinary impact. His team’s work supports operational forecasting and scientific understanding of Earth’s complex systems.

• Computer Vision
• Autonomous Vehicles
• Automotive Safety Systems
• +۵ مورد دیگر

Darragh Mullins is a researcher at University College Cork, specifically within the Department of Electrical and Electronic Engineering in the College of Science, Engineering and Food Science. His work is deeply embedded in the automotive perception and intelligent transportation systems research community at UCC, collaborating extensively with researchers including Martin Glavin, Edward Jones, Brian Deegan, and Jonathan Horgan. Mullins' research focuses on critical challenges in autonomous vehicle perception systems. His work spans computer vision for automotive applications, addressing issues such as lens blur effects, rain impacts, lighting conditions, and pedestrian occlusion on object detection systems. He has made significant contributions to V2X communications research, particularly examining 5G NR deployments for vehicle connectivity. His research bridges theoretical computer vision with practical automotive safety applications, demonstrating strong industry relevance. The publication pattern reveals a consistent research trajectory with increasing impact from 2021-2024. Mullins' work shows a clear progression from fundamental image processing challenges (ISP tuning, demosaicing algorithms) to more complex system-level problems (V2X communications, pedestrian intention forecasting). His research demonstrates strong interdisciplinary connections between computer vision, telecommunications, and transportation engineering. Mullins operates within a well-established research group at UCC focused on automotive safety and intelligent transportation systems. This group appears to have strong industry connections given the practical focus of their research on safety-critical automotive systems. The collaborative nature of his publications suggests active participation in both national and international research networks focused on autonomous vehicle technologies.

• Biomedical Engineering
• Automotive Systems
• Computer Vision
• +۳ مورد دیگر

Martin Glavin is a Professor at the National University of Ireland, Galway, affiliated with the Department of Electronic & Electrical Engineering within the College of Engineering and Informatics. His research focuses on cutting-edge areas such as biomedical engineering, automotive systems, and computer vision. Key contributions include advancements in image processing for low-light environments, driver gaze analysis for cyclist safety, and optimizing automotive camera systems. He has collaborated extensively with co-authors like Edward Jones and Darragh Mullins, yielding over 70 peer-reviewed publications since 2002. His work spans journals like IEEE Transactions and conferences such as ICC and ICARCV. Research interests include safety-critical object detection for autonomous vehicles, microwave imaging for medical diagnostics, and 5G-enabled V2X communication systems. Glavin's interdisciplinary efforts bridge signal processing, machine learning, and engineering applications, addressing real-world challenges in healthcare and transportation.

• Fatigue and Fracture Mechanics
• Probabilistic Computational Mechanics
• Risk Assessment and Management
• +۷ مورد دیگر

Yongming Liu is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He leads the Prognostic Analysis and Reliability Assessment Laboratory (PARA) and has been at ASU since 2012. His research focuses on fatigue/fracture mechanics, probabilistic computational methods, risk assessment, multi-physics damage modeling, and uncertainty quantification across scales. Key interests include experimental testing, diagnostics/prognostics, and structural durability. Education: Ph.D. Civil Engineering (Vanderbilt University, 2006); M.S. and B.S. Structural Engineering (Tongji University, Shanghai, 2002 and 1999). Research activity includes NSF-funded projects on fatigue prognosis for coastal bridges, DOE-supported studies on creep-fatigue analysis of alloys, and industry collaborations on pipeline diagnostics. Recent work emphasizes AI-driven approaches for structural health monitoring, aerospace safety, and energy systems. Courses taught span mechanics of materials, probabilistic methods in engineering design, and graduate supervision. Key contributions include physics-informed machine learning frameworks, fatigue life prediction under uncertainty, and multi-agent reinforcement learning for air traffic management. His lab develops advanced diagnostics/prognostics tools bridging structural and biomedical applications.

• Environmental Science
• Air Quality Modeling
• Machine Learning Applications
• +۳ مورد دیگر

Dimple Pruthi is a Research Fellow in the Gangarosa Department of Environmental Health at Emory University. Her work focuses on advanced computational methods for air quality prediction, integrating machine learning, satellite data analytics, and environmental modeling. She specializes in PM2.5 forecasting, wildfire impact analysis, and sustainable pollution monitoring systems. Her research interests include developing hybrid machine learning models for environmental applications, analyzing transient weather effects on pollution, and optimizing neuronal networks for air quality indices. She has extensively studied Delhi's air quality dynamics and California wildfires' particulate matter emissions. Key trends in her publications (2016–2025) reveal a focus on satellite-based PM2.5 prediction using GOES-R data, low-cost sensor systems, and fractal/wavelet analysis for pollutant variability. She combines computational techniques with environmental science to address urban pollution challenges and climate change impacts. No scientific awards or grants are explicitly listed. Her advising record is not provided, but her work contributes to environmental health policy through predictive modeling frameworks.

Kate Larsonمشاهده پروفایل

استاد

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +۳ مورد دیگر

Kate Larson is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Her research explores artificial intelligence with emphasis on multiagent systems, reinforcement learning, and AI applications for sustainability and climate challenges. She employs game-theoretic approaches to address coordination and cooperation in complex systems. Education Ph.D. in Computer Science from Carnegie Mellon University (2004), M.Sc. from Washington University in St. Louis (1999), and B.Sc. from Memorial University of Newfoundland (1997). Research Focus Professor Larson's work bridges theoretical AI and practical societal impacts. Key areas include: Designing cooperative AI frameworks for multiagent environments Developing value-alignment mechanisms for ethical AI Applying reinforcement learning to climate change mitigation Creating robust AI systems through game-theoretic guarantees Publication Trends Her recent publications (2024-2025) demonstrate strong focus on AI safety, game-theoretic foundations, and multimodal learning systems. Dominant themes include: algorithmic alignment techniques, multi-agent risk management, and interpretable AI architectures, with increasing emphasis on real-world climate and sustainability applications. Awards & Recognition No major scientific awards mentioned in source materials. Research Infrastructure No specific labs or teams detailed in available information.

Ákos Horváthمشاهده پروفایل

پژوهشگر

• Atmospheric Science
• Satellite Meteorology
• Remote Sensing
• +۳ مورد دیگر

Dr. Ákos Horváth is a researcher at the University of Hamburg , affiliated with the Meteorological Institute under the Faculty of Earth Sciences. His work focuses on satellite-based atmospheric observations and volcanic eruption impacts on climate systems. PhD (Atmospheric Sciences), University of Arizona (2004) MSc (Atmospheric Sciences), University of Arizona (1999) BSc (Meteorology), Eötvös University, Hungary (1996) Research interests span volcanology , cloud dynamics , satellite meteorology , and atmospheric remote sensing . Recent publications highlight advances in volcanic plume height estimation using GOES-R and MISR, diurnal cloud patterns, and vortex street analysis. Article trends reveal expertise in multi-satellite data fusion , atmospheric motion vectors , and radiative transfer modeling . His work bridges observational meteorology with practical applications in climate modeling and hazard assessment. Scientific contributions include the NASA Group Achievement Award (2001) and leadership in projects like CHASER and VolImpact. Collaborations span institutions including NASA, University of Arizona, and Hungarian research teams. Publications demonstrate methodological innovation in stereo wind retrieval , cloud liquid water path analysis , and polarimetric cloud detection . Current research investigates wave-cloud interactions post-Hunga Tonga eruption and volcanic aerosol aging processes.

Ehsan Salariمشاهده پروفایل

دانشیار

• Operations Research
• Healthcare Systems Optimization
• Radiation Therapy Planning
• +۵ مورد دیگر

Ehsan Salari is an Associate Professor in the Department of Industrial Systems and Manufacturing Engineering at Wichita State University's College of Engineering. His research focuses on optimization methodologies applied to healthcare systems, radiation therapy planning, and energy systems. He holds a Ph.D. in Industrial and Systems Engineering from the University of Florida, an M.S. from Sharif University of Technology, and a B.S. from Amirkabir University of Technology. His work integrates operations research techniques with medical and engineering challenges, including stochastic control for radiotherapy motion management, agent-based modeling for occupational safety, and spatiotemporal optimization in cancer treatment. Recent studies address severe weather impacts on urban mobility patterns and energy system integration using renewable resources. Salari's research portfolio spans over 40 publications since 2005, demonstrating expertise in mathematical programming, queueing theory applications, and medical imaging-guided treatment systems. His interdisciplinary approach bridges industrial engineering with healthcare delivery and environmental resilience.