جستجوی استاد راهنمای دانشگاهی

Ranjini Swaminathanمشاهده پروفایل

پژوهشگر ارشد

• Climate Science
• Earth System Modeling
• Machine Learning Applications in Climate Research
• +۳ مورد دیگر

Dr. Ranjini Swaminathan is a Research Fellow in the Meteorology Department at the University of Reading, serving as a Core Scientist for the NCEO UK Earth System Model (UKESM). Her research focuses on advancing Earth system modeling, particularly in climate sensitivity, machine learning applications for vegetation modeling, and ocean-climate interactions. She contributes to interdisciplinary studies on global warming thresholds, carbon cycle projections, and model evaluation frameworks like ESMValTool. Her work bridges climate science with computational methods, addressing critical questions in regional climate impacts and extreme weather prediction. Swaminathan’s expertise includes developing and validating Earth system models, collaborating on initiatives such as the UKESM project and the CMIP framework. She has published extensively on topics including climate extremes, AI in weather forecasting, and terrestrial biome classification through explainable clustering techniques. Her office is located in Harry Pitt Building, Room HP177. While no scientific awards are explicitly listed, her contributions to climate science have advanced understanding of future climate scenarios and model reliability. She has not listed advisees, but her work involves collaborative projects with international research teams.

Paola Crippaمشاهده پروفایل

استادیار

• Air Quality
• Aerosol Dynamics Modeling
• Climate Change
• +۷ مورد دیگر

Paola Crippa is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, College of Science. Her research focuses on air quality, aerosol dynamics, climate change, and wind energy, leveraging advanced computational models like WRF-Chem and large eddy simulations (LES). She holds a Ph.D. in Atmospheric Science from Indiana University-Bloomington (2013), and M.S. and B.S. degrees in Environmental Engineering from Politecnico di Milano, Italy (2007 and 2005). Her work integrates high-performance computing with atmospheric processes, exploring topics such as urban climate impacts, aerosol-cloud interactions, and extreme weather events. Notable contributions include studies on Sundowner events, wind energy potential in complex terrain, and the role of urbanization in deep convection. Research interests span multiscale modeling, environmental justice in air pollution epidemiology, and the application of machine learning (e.g., echo state networks) for weather forecasting. She actively participates in interdisciplinary projects like the Perdigão field campaign, addressing wind energy and turbulence dynamics. Her publications emphasize quantifying uncertainties in climate impacts, optimizing wind energy assessments, and understanding human health risks from air pollutants. Despite no explicitly noted awards or grants in the provided text, her extensive publication record reflects sustained contributions to atmospheric science and computational methods.

Maria Helgueraمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Biomedical Engineering
• Ultrasound Technology
• +۴ مورد دیگر

Maria Helguera is an Adjunct Professor in the Department of Electrical and Computer Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. Her research focuses on biomedical imaging technologies, including ultrasound characterization, image registration algorithms, and biomechanical modeling of vascular systems. She has contributed to advancements in lung nodule tracking, abdominal aortic aneurysm severity assessment, and brain atrophy detection in Alzheimer's disease. Her work spans multiple interdisciplinary areas: medical imaging (e.g., CT/MRI fusion, quantitative ultrasound), material science (edible coatings, collagen analysis), and optical engineering (diffraction-based sensors, coherence elastography). Key themes include developing automated methods for clinical image analysis and translating engineering principles to biomedical applications. Publications reflect a strong emphasis on translational research, with applications in cancer monitoring, vascular disease modeling, and food preservation. Though no awards are explicitly listed, her prolific output (over 40 publications since 1990) indicates sustained academic contribution. Advising and grant details are not provided in available materials.

Catherine Kimمشاهده پروفایل

پژوهشگر ارشد

• Marine Science
• Coral Reef Conservation
• Data Science Applications in Ecology
• +۳ مورد دیگر

Catherine Kim is a Postdoctoral Research Fellow at the School of Earth & Atmospheric Sciences. She focuses on marine science, particularly coral reef restoration and adaptation, leading projects like the Reef Restoration and Adaptation Program (Rubble and Decision Science sub-programs) alongside Professors Scott Bryan and Michael Bode. Her work includes predicting coral rubble dynamics on the Great Barrier Reef and developing a flood vulnerability index for Brisbane City with Dr Kate Saunders and Associate Professor Kate Helmstedt. She earned her PhD in marine science from the University of Queensland, where her research explored coral health and biodiversity in Timor-Leste. Dr. Kim’s research interests span coral reef ecology, geospatial analysis, and disaster risk assessment. She actively promotes STEM diversity through initiatives like R Ladies Brisbane and the Wonder of Science program. Notable achievements include a 2022 Queensland Women in STEM Awards Finalist recognition and grants totaling over AUD 100,000, including the Society for Conservation Biology Small Grant and the Elodie Sandford Explorer Award. Education: PhD (University of Queensland). Affiliations: Centre for Data Science, Coral Reef Ecosystems Laboratory, Brisbane Floods Hackathon leadership. Outreach: Co-organizer of Geospatial Community and R Ladies Brisbane, Young Science Ambassador (Wonder of Science). Her scientific contributions include advancing AI-driven coral reef monitoring and advocating for linguistically inclusive academic publishing. Current projects emphasize bridging geospatial science with environmental conservation to address climate change impacts.

• Earth Observation
• Remote Sensing
• Climate Change
• +۵ مورد دیگر

Professor Heiko Balzter is a Professor of Physical Geography and Director of the Institute for Environmental Futures at the University of Leicester's School of Geography, Geology & the Environment. He chairs the UKRI Landscape Decisions Programme, coordinating 70 projects, and is a leading member of the National Centre for Earth Observation. His research focuses on earth observation techniques for environmental monitoring, deforestation, climate change, and land use dynamics. He has received notable awards including the Royal Society Wolfson Research Merit Award (2011), Royal Geographical Society’s Cuthbert Peek Award (2015), Copernicus Masters Award (2017), and Leicestershire Live Innovation Award (2023). His work integrates satellite data with ecological models to address global challenges like carbon sequestration, biodiversity conservation, and sustainable land management. Research interests include remote sensing of biomass, forest degradation, and environmental vulnerability. He collaborates internationally on projects such as the LULUCF Scientific Steering Committee for the UK National Greenhouse Gas Account. His recent work emphasizes interdisciplinary approaches to landscape decisions, balancing ecological, agricultural, and energy demands while advancing net-zero strategies. Key contributions include developing tools like Pyeo for near-real-time forest monitoring and frameworks for deforestation-free supply chains using Copernicus data. Awards: Royal Society Wolfson Research Merit Award (2011) Royal Geographical Society’s Cuthbert Peek Award (2015) Copernicus Masters Award (2017) Leicestershire Live Innovation Award (2023) Advising and grants focus on interdisciplinary landscape solutions, with grants from UKRI and EU programs. His work with the Institute for Environmental Futures and National Centre for Earth Observation drives innovative applications of Earth observation to address climate and biodiversity crises. Ongoing projects include analyzing biomass dynamics in African savannahs, oil spill detection via SAR imagery, and modeling climate impacts on disease spread.

Roberto Furfaroمشاهده پروفایل

استاد

• Guidance, Navigation and Control
• Machine Learning in Aerospace
• Space Situational Awareness
• +۷ مورد دیگر

Roberto Furfaro is a Professor in the Department of Systems and Industrial Engineering and the Department of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He serves as Deputy Director of the Space, Security, Safety & Sustainability Center (S4C) and is a key figure in space situational awareness and planetary exploration systems. His educational background includes a Laurea Degree (MS equivalent) in Aeronautical Engineering from the University of Rome 'La Sapienza' (1998) and a PhD in Aerospace Engineering from the University of Arizona (2004). His research spans guidance and control of planetary landers, machine learning applications in space systems, hypersonic vehicle G&C, and systems engineering for small body proximity operations. He leverages advanced AI, particularly reinforcement learning and deep learning, to solve complex aerospace challenges. The recent publications reflect a strong trend toward intelligent, autonomous systems in spaceflight, especially using deep reinforcement learning for planetary landing, guidance under uncertainty, and space-based object tracking. His work bridges control theory, astrodynamics, and artificial intelligence. 2021 Da Vinci Fellow, College of Engineering AIAA Associate Fellow (2022) AAS Fellow (2021) Asteroid 133474 Roberto Furfaro named in honor of OSIRIS-REx contributions Prof. Furfaro has served as PI or Co-PI on over $80M in research grants from NASA, AFRL, and DoD. He has advised numerous students and leads research teams in autonomous space systems and machine learning applications. He is the Target Follow-up Working Group lead for the NASA NEO Surveyor Mission and was Systems Engineering Lead for SPOC during OSIRIS-REx Phase B-D. He is actively involved in professional service as Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and technical member of the AIAA Astrodynamics Committee and AAS Space Surveillance Committee.

Javier Garcia-Heras Carreteroمشاهده پروفایل

دانشیار

• Air Traffic Management
• Flight Planning under Uncertainty
• Convective Weather Prediction
• +۱۲ مورد دیگر

Javier Garcia-Heras Carretero is an Associate Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid (UC3M), where he conducts research and teaches in air traffic management, flight planning, and AI applications in aviation. He is a member of the Aerospace Engineering Research Group. Research Interests: His work focuses on enhancing air traffic flow management through machine learning and optimization, particularly in mitigating weather-related disruptions. Key areas include robust 4D flight planning under uncertainty, convective weather prediction, continuous climb operations, and environmental impact reduction in aviation. He integrates AI models such as LSTM and CNNs to forecast thunderstorms and optimize traffic flow. Recent Publication Trends: His most recent articles (2023–2024) emphasize deep learning for pre-tactical convection prediction, fast stochastic flight path simulation under uncertainty, and AI-driven hotspot detection in air traffic due to weather. These works reflect a strong trend toward data-driven, real-time decision support systems in ATM using neural networks and parallel computing. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: He supervises BSc and MSc theses, including one on data science techniques for weather mitigation in ATFM. He has led multiple research projects as Principal Investigator, including Collaborative Learning for Intelligent Meteorological Aviation (2024–2027) and Donut proyect (2019–2022). He has participated in numerous EU-funded projects such as KAIROS, REFMAP, ECONTRAIL, and ISOBAR, as well as industry collaborations with Airbus, Boeing, and SENASA. Labs and Teams: He is part of the Aerospace Engineering Research Group at UC3M, focusing on ATM systems, trajectory optimization, and digital aviation technologies. His team collaborates internationally on projects involving AI, weather forecasting, and sustainable aviation.

• Geological Modeling
• 3D Geophysical Modeling
• Structural Geology
• +۳ مورد دیگر

Lachlan Grose is a Research Fellow at Monash University's School of Earth Atmosphere and Environment, specializing in advanced geological and geophysical modeling. His work focuses on integrating machine learning, 3D structural analysis, and geophysical inversion techniques to address challenges in subsurface resource management and tectonic understanding. Key projects include the Three-dimensional Bayesian Modelling of Geological and Geophysical data (ARC-funded) and development of the LoopStructural software, a time-aware 3D geological modeling platform. Grose also contributes to industry collaborations like the AMIRA P1249 project on ore body knowledge modeling. Research interests span structural geology, igneous intrusion modeling, FAIR data principles, and geophysical inversion methods. He has led efforts to improve usability of Loop3D and pioneered machine learning applications for geological structure imaging. Leading developer of LoopStructural (open-source 3D modeling framework) Primary investigator on usability improvements for geoscientific workflows Recipient of ARC and industry grants totaling over AUD 3M Supervises PhD candidates focusing on interdisciplinary geoscience problems Recent publications emphasize transdimensional inversion methods, FAIR data integration, and application of machine learning to geophysical datasets. His work bridges academic research with industrial subsurface resource challenges.

• High-energy astrophysics
• Machine learning
• Statistical methods
• +۷ مورد دیگر

Dr. Sabrina Einecke is a Grant-Funded Researcher (B) at the School of Physics, Chemistry and Earth Sciences , University of Adelaide , with over a decade of experience in high-energy astroparticle physics, machine learning applications, and statistical methods. Leading research in Cherenkov Telescope Array (CTA) science and commissioning Developing software for gamma-ray emission modeling of supernova remnants Advancing machine learning techniques for astrophysical data analysis Her work spans active galactic nuclei , transients , cosmic-ray acceleration , and telescope site quantification in Australia. She serves as a Chief Investigator on ARC LIEF and DVCR-funded projects. The 9 most recent publications highlight her expertise in gamma-ray astronomy, statistical inference, and interdisciplinary collaborations, with significant impact on CTA design and PeVatron identification.

Sarah Aaronsمشاهده پروفایل

استادیار

• Isotopic Geochemistry
• Low Temperature Geochemistry
• Paleoclimatology
• +۱۲ مورد دیگر

Sarah Aarons is an Assistant Professor in the Geosciences Research Division at Scripps Institution of Oceanography, University of California San Diego. Her research focuses on understanding the geochemical evolution of Earth's surface through isotopic analysis of natural materials such as mineral dust, sediments, and rocks. Education: Ph.D. in Geology, University of Michigan (2016) M.S. in Earth and Environmental Sciences, University of Michigan (2012) B.S. in Geological and Environmental Sciences, Stanford University (2009) Her primary research interests include Isotopic Geochemistry, Low Temperature Geochemistry, Paleoclimatology, Atmospheric Aerosols, and Climate Sciences . She applies radiogenic and non-traditional stable isotope techniques to study dust provenance, weathering processes, and biogeochemical cycling in modern and ancient environments. Field sites span polar regions including Alaska and Antarctica, with a strong emphasis on clean lab methodologies and mass spectrometry. The recent publication trends reflect a consistent focus on using isotope systems (Sr-Nd, Li, Mg, Ca, K, REEs) to trace dust sources, reconstruct paleoenvironments, and understand nutrient delivery to oceans. Her work bridges marine, atmospheric, and terrestrial geochemistry, with strong implications for climate change and Earth system dynamics. Scientific Awards: Sarah Aarons leads the Climate & Earth Surface Geochemistry Group , which conducts field-based and laboratory-intensive research. She advises a team of early-career scientists and graduate students, overseeing projects funded by federal and institutional grants focused on dust-climate interactions, cryospheric geochemistry, and Earth surface processes. Her lab emphasizes interdisciplinary collaboration, open data practices, and integration of physical and chemical measurements. Labs and Research Groups: Climate & Earth Surface Geochemistry Group, Scripps Institution of Oceanography

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Tommaso Ruga serves as a Lecturer in the Department of Computer Engineering, Modellistica, Elettronica e Sistemistica (DIMES) at the University of Calabria, Italy. He teaches core courses including Data Base Systems for Management Engineering, Fundamentals of Computer Science for Mechanical Engineering, and Data Analytics for Business for Management Engineering programs in the 2025 academic year. His research centers on Artificial Intelligence applications across critical domains, with primary focus on healthcare innovation (cancer detection, pandemic forecasting, Alzheimer's diagnosis), sustainable agriculture (cassava disease detection, smart farming), and cultural heritage preservation . Key methodologies include federated learning, explainable AI frameworks, and deep learning architectures designed to address real-world challenges like dataset bias in dermatology and energy forecasting for sustainable communities. Analysis of his 15 most recent publications (2023-2025) reveals a cohesive trajectory: 60% target healthcare applications (particularly oncology and neurology), 25% focus on agricultural and environmental systems, and 15% address cultural heritage and tourism. His work consistently emphasizes technical explainability and ethical considerations , notably in mitigating racial bias in medical imaging datasets and developing transparent AI models for clinical decision support. Dr. Ruga operates within the University of Calabria's Department of Computer Engineering, Modellistica, Elettronica e Sistemistica, which provides the computational infrastructure for his AI research. His collaborations frequently involve co-authors Caroprese, Vocaturo, and Zumpano across multiple publications, indicating established research partnerships within the institution.

Chaoqun Luمشاهده پروفایل

دانشیار

• Ecosystem Ecology
• Climate Change Impacts
• Biogeochemical Cycling
• +۷ مورد دیگر

Chaoqun (Crystal) Lu is an Associate Professor in the Department of Ecology, Evolution, and Organismal Biology at Iowa State University. Her research focuses on quantifying ecosystem responses to global change using systems modeling, data assimilation, and geospatial analysis. Key interests include carbon, nitrogen, and water cycles; land-atmosphere greenhouse gas exchange; and riverine nutrient export. Education: PhD in Ecosystem Ecology (Auburn University/Chinese Academy of Sciences, 2009), MS in Ecology (Nanjing University, 2004), BS in Ecology (Inner Mongolia University, 2002). Her work integrates process-based models like CLM and has produced datasets such as HISLAND-US for historical land-use analysis. Research Highlights: Investigates how extreme precipitation, cropping systems, and management practices impact nitrogen pollution and GHG emissions. Recent projects include blockchain-based crop insurance platforms (AgBIS) and spatially explicit analyses of US cropland diversity. Grants/Labs: Leads the Lu Lab, which develops computational tools for environmental prediction. Active in interdisciplinary projects linking AI, remote sensing, and Earth system modeling. Collaborates on global datasets like CMIP6 and the Global Nitrogen Budget.

Tuba Ozkan-Hallerمشاهده پروفایل

استاد

• Coastal Engineering
• Oceanography
• Nearshore Circulation Modeling
• +۳ مورد دیگر

Tuba Ozkan-Haller is the current Dean of the College of Earth, Ocean, and Atmospheric Sciences and a Professor in the Department of Civil and Construction Engineering at Oregon State University. She holds a B.S. from Bogazici University (1991), an M.C.E. from the University of Delaware (1994), and a Ph.D. in Civil Engineering from the University of Delaware (1998). Her research focuses on nearshore ocean dynamics, including wave modeling, coastal circulation, and bathymetric change prediction. She applies numerical models to study coastal hazards like sneaker waves and extreme runup events, integrating field data with laboratory experiments. Key research interests include the impact of wave energy converters on coastal systems, data assimilation techniques for improving bathymetry estimates, and the physics of wave breaking in deep water. Ozkan-Haller collaborates on projects like the Pacific Marine Energy Center (PMEC) and uses machine learning to refine wave model accuracy. Her work bridges academia and policy, advocating for federal support in ocean science and advancing equity in STEM education through institutional transformation initiatives. Her articles highlight contributions to understanding coastal hazards, wave-structure interactions, and interdisciplinary applications of oceanography. Collaborations include biologging studies with marine species and multi-scale modeling efforts for tidal inlets. Ozkan-Haller’s research has implications for coastal resilience, renewable energy development, and informed environmental policy.

Amy McGovernمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• High-Impact Weather
• +۳ مورد دیگر

Amy McGovern is a Lloyd G. and Joyce Austin Presidential Professor at the University of Oklahoma, holding dual professorships in the School of Computer Science and School of Meteorology . She directs the NSF-funded AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), a multi-institutional initiative advancing AI for environmental decision-making. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2002) M.S. in Computer Science, University of Massachusetts Amherst (1998) B.S. (Honors) in Computer Science, Carnegie Mellon University (1996) Research Interests: Machine learning for severe weather prediction (tornadoes, hail, lightning) Trustworthy AI in environmental science Deep learning for weather data analysis and visualization Broadening participation in STEM through AI education Funding & Leadership: NSF AI Institute (AI2ES): $20M+ directorship NOAA-funded projects on tornado prediction (2020-2025) NASA grants for severe storm detection (2020-2023) Awards: American Meteorological Society Fellow (2020) NSF CAREER Award (2008-2015) OU Vice President for Research Award (2019) Outreach & Education: Developing AI curricula for HSI/MSI institutions K-12 STEM outreach programs IDEA Lab (Interaction, Discovery, Exploration, and Adaptation)

Lance Sherryمشاهده پروفایل

استاد

• Air Transportation System Modeling and Simulation
• Data Analytics
• Stochastic Simulation
• +۱۲ مورد دیگر

Lance Sherry is a Professor in the Department of Systems Engineering and Operations Research at George Mason University’s College of Engineering and Computing. He serves as Director of the Center for Air Transportation Systems Research (CATSR), where he leads cutting-edge research in air transportation systems, data analytics, and autonomous systems. Education: PhD, Industrial and Systems Engineering, Arizona State University MS, Industrial and Systems Engineering, Arizona State University BS, Electrical Engineering, Brown University His research focuses on the modeling and simulation of complex adaptive systems in aviation, including air traffic control, airport operations, and unmanned aerial vehicles. He has pioneered work in human-computer interaction, operator training, and big data applications in aviation safety. His recent publications reflect strong trends in predictive analytics, environmental sustainability in aviation, and decision support systems for complex operational challenges. Scientific Awards and Recognitions: U.S. Patent and Provisional Patent for probabilistic alerting of aircraft unstable approaches using big data Multiple 1st and 2nd place awards in IEEE SIEDS, PITCH-IT, and Capstone competitions Best paper award at ICRAT Conference (2016) Senior design teams consistently recognized for innovation in systems engineering Lance Sherry has advised numerous PhD students, many of whom have gone on to prominent roles in aviation, government, and tech. He has secured research funding from organizations such as the Decision Science Research Institute, Crown Consulting Inc., and LMI. His leadership extends to professional service, including chairing the IEEE ICNS Conference and serving on NSF merit panels and AIAA technical committees. He is also actively involved in promoting drone technology education and urban air mobility research. Laboratories and Teams: He leads the Center for Air Transportation Systems Research (CATSR), which conducts interdisciplinary research in aviation systems, big data analytics, and human factors. The center supports undergraduate and graduate student teams in national design competitions and collaborates with FAA, NASA, and industry partners.