جست‌وجوی استاد و پژوهشگر

Natesh Pillaiمشاهده پروفایل

استاد

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Luca Schenatoمشاهده پروفایل

استاد

• Control Systems
• Distributed Optimization
• Federated Learning
• +۵ مورد دیگر

Luca Schenato is a Full Professor in the Department of Information Engineering at the University of Padova. His research focuses on distributed control systems, federated learning, multi-agent optimization, and wireless communication protocols. He has extensive experience in developing algorithms for cyber-physical systems, with applications in robotics, smart grids, and sensor networks. Education and Appointments section lists his academic journey but lacks explicit details. He has held positions related to control systems and information engineering throughout his career. Research interests include: Design of resilient wireless control systems Federated learning architectures for edge computing Distributed optimization under communication constraints Robotics and multi-agent coordination Smart energy management systems His recent publications (2021–2025) demonstrate a strong focus on: Over-the-air federated learning innovations High-speed wireless control systems (e.g., 1 kHz Wi-Fi control) Resilient distributed optimization algorithms Human-centric building automation He has contributed to numerous projects related to networked control systems and has organized conferences like ECC13. His work emphasizes bridging theoretical control principles with practical industrial applications.

Edwin P. Gerberمشاهده پروفایل

استاد

• Climate Variability
• Climate Change
• Atmospheric Dynamics
• +۳ مورد دیگر

Edwin P. Gerber is a Professor of Mathematics and Atmosphere/Ocean Science at New York University’s Courant Institute of Mathematical Sciences. He holds joint affiliations with the Department of Environmental Studies and the Center for Data Science. His research focuses on understanding climate variability and dynamics, particularly the role of stratosphere-troposphere interactions and simplified climate models. Gerber earned his Ph.D. in Applied and Computational Mathematics from Princeton University (2006), following an M.A. (2002) and B.S. in Mathematics and Chemistry from the University of the South (2000). His work bridges theory and Earth System models, investigating topics like the Brewer-Dobson Circulation, sudden stratospheric warmings, and ozone layer dynamics. Key achievements include the DynVarMIP initiative for CMIP6 and contributions to gravity wave parameterization. Gerber has received awards such as the Friedrich Wilhelm Bessel Research Award (2021) and Hertz Foundation Fellowship (2000-2005). His research has been supported by grants from NSF, NASA, and international collaborations. Gerber’s lab explores machine learning applications in climate modeling, including data-driven parameterization of gravity waves. He serves as Associate Editor for the Quarterly Journal of the Royal Meteorological Society and has led initiatives like the SPARC Reanalysis Intercomparison Project (S-RIP). His recent studies address tropical teleconnections, stratospheric ozone responses to global warming, and extreme event predictability. Gerber’s teaching includes courses on atmospheric dynamics, climate change, and differential equations. He emphasizes interdisciplinary approaches, integrating theory, computation, and observational data to advance climate science understanding.

• Ecohydrology
• Water Quality Management
• Environmental Impact Assessment
• +۶ مورد دیگر

Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.

Joannes Westerinkمشاهده پروفایل

استاد

• Computational fluid dynamics
• Finite element methods
• Coastal ocean modeling
• +۳ مورد دیگر

Joannes Westerink is the Joseph and Nona Ahearn Professor in Computational Science and Engineering at the University of Notre Dame's College of Engineering. He holds concurrent faculty titles in the Departments of Aerospace and Mechanical Engineering, Earth Sciences, Computer Science and Engineering, and other disciplines. His research focuses on computational fluid dynamics, tidal hydrodynamics, and hurricane storm surge prediction. He earned his Ph.D. from MIT in 1984, followed by M.S. and B.S. degrees in Civil Engineering from SUNY Buffalo. Key research interests include finite element methods, coastal circulation modeling, and geophysical turbulence. In 2025, he was awarded the International Coastal Engineering Award for his contributions to storm surge forecasting and coastal hazard mitigation. Westerink leads the Computational Hydraulics Laboratory, advancing numerical models like STOFS (Surge and Tide Operational Forecast System) for global water level predictions. His recent work integrates AI-driven nudging techniques and machine learning to enhance model accuracy, with applications in disaster risk assessment and climate adaptation.

Rasmus Waagepetersenمشاهده پروفایل

استاد

• Spatial Statistics
• Quantitative Genetics
• Markov Chain Monte Carlo Methods
• +۳ مورد دیگر

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

• Lithium-ion Battery Technologies
• Redox Flow Batteries
• Bioenergy
• +۵ مورد دیگر

Dr. Jacob Joseph Lamb is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Energy and Process Engineering within the Faculty of Engineering. He serves as the Study Program Manager for the Bachelor of Engineering in Renewable Energy (BIFOREN) and leads the Digital Energy Systems laboratory group. His roles include WP8 leadership in the FME Battery initiative and Theme Leader for Efficient Energy Use at ENERSENSE. Education: PhD in Energy and Process Engineering, NTNU (2013–2016) MSc in Chemical Engineering, University of Otago, New Zealand (2011–2012) BSc in Chemical Engineering, University of Otago, New Zealand (2008–2010) Research Interests: Dr. Lamb focuses on sustainable energy systems, particularly lithium-ion battery technologies , redox flow batteries , bioenergy , and digitization of energy systems . He explores sensor technologies for real-time monitoring and optimization of energy storage devices. His work integrates machine learning with physics-based models to enhance battery performance and reliability. Key Projects: FME Battery: Norwegian research initiative for advanced battery technologies ReZinc: Zinc-air flow battery development for stationary storage HeaLiSelf: Self-healing lithium-ion batteries using optical sensing and data analytics Computational studies on pre-lithiation processes and cold-climate battery performance Publications: His recent work emphasizes lithium-ion battery digitalization , SoC estimation , and electrode structuring . He bridges experimental and computational methods to address challenges in energy storage scalability and safety. Affiliations: ENERSENSE (Efficient Energy Use Theme) NTNU Thermal Engineering Laboratories

John Allenمشاهده پروفایل

دانشیار

• Meteorology
• Severe Weather
• Climatology
• +۱۰ مورد دیگر

Dr. John Allen is an Associate Professor of Meteorology in the Department of Earth and Atmospheric Sciences at Central Michigan University, where he also serves as Director of the Earth and Ecosystem Science Ph.D. Program and Associate Director of the NSF Artificial Intelligence Institute for Environmental Sciences (AI2ES). He is actively engaged in interdisciplinary research focusing on severe convective storms, their climatology, and responses to climate change. Education: Ph.D., Earth Sciences (Meteorology), The University of Melbourne, 2013 B.S., Research Honours (Meteorology), The University of Melbourne, 2008 B.S., Meteorology and Applied Mathematics, The University of Melbourne, 2007 Dr. Allen’s research centers on global severe weather, particularly tornadoes and hail, and their interactions with climate variability and change. He employs machine learning, statistical modeling, and environmental datasets to improve forecasting and risk assessment. His work spans climatology, atmospheric dynamics, and operational meteorology, with a strong emphasis on translating climate projections into actionable resilience strategies. His recent publications demonstrate a strong trend in applying artificial intelligence and deep learning to severe weather prediction, including hail nowcasting, frontal boundary detection, and hodograph analysis for hazard discrimination. These works reflect a focus on both theoretical understanding and practical forecasting applications. Scientific Awards: National Science Foundation CAREER Award (2020) Central Michigan University Provost’s Award for Outstanding Research (2019/2020) College of Science & Engineering Award for Outstanding Research (2019) AGU Editors' Citation for Excellence in Refereeing (2015) European Severe Storms Laboratory Heini Tooming Award (2015) AMS STAC Outstanding Early Career Award (2022) AMS Editor’s Award for Reviews (2022) Dr. Allen advises numerous Ph.D. students and postdoctoral researchers, and leads multiple funded projects with NSF and NIST. He has served as an editor for Weather and Forecasting and Artificial Intelligence for the Earth Systems . His research group, the Allen Research Group, conducts fieldwork and collaborates internationally on hail and tornado studies. Key Research Projects: NSF-funded ICECHIP hail field campaign (co-lead, summer 2025) Quantifying Risk of Wind and Hail Storms in a Warming Climate (NIST-funded) Geospatial Predictive Analysis of Hail and Wind (AON Inc.) Left-moving Supercell Storm Processes (NSF) Improving High-Impact Hail Forecasts (NSF PREEVENTS)

Matthew B Huffordمشاهده پروفایل

استاد

• Plant Genetics
• Genomics
• Evolutionary Biology
• +۳ مورد دیگر

Matthew B Hufford is a Professor in the Department of Ecology, Evolution, and Organismal Biology (EEOB) at Iowa State University. He joined the faculty in 2013 as an Assistant Professor and currently holds the Cassling Family Professorship. His research focuses on the evolution and ecology of crops, particularly maize and its wild relatives, including teosintes. Key areas include demography of maize landraces, gene flow dynamics in Zea genus, and comparative genomics of grass species. Education: B.S. in Biology from Wheaton College (1999), M.S. in International Agricultural Development and Ph.D. in Ecology, both from UC Davis (2010). Research interests span genomic diversity in maize, adaptation to environmental conditions, and the role of transposable elements in genome evolution. His lab investigates genomic resources like pan-genomes and assembly tools (e.g., Gapless assemblies using long-read technologies). Recent trends in publications highlight advancements in genomic tools, climate adaptation, and comparative analyses across grass species. Labs/Teams: The Hufford Lab at Iowa State University focuses on evolutionary genomics, leveraging genomic data to understand crop domestication and adaptation. Collaborations involve bioinformatics, genetics, and ecological studies.

Jihye Parkمشاهده پروفایل

دانشیار

• Remote Sensing
• Geodesy
• Geophysics
• +۳ مورد دیگر

Jihye Park is an Associate Professor in the Department of Civil and Construction Engineering at Oregon State University's College of Engineering. She specializes in GNSS (Global Navigation Satellite System) positioning/navigation and remote sensing applications, focusing on enhancing system performance in harsh environments and leveraging GNSS for atmospheric monitoring. Her research includes detecting ionospheric disturbances caused by earthquakes, volcanic eruptions, and artificial explosions, as well as developing algorithms for real-time anomaly detection and navigation system improvements. Dr. Park holds a Ph.D. in Geodetic Science and Surveying from The Ohio State University (2012), an M.S. in Geodetic Science and Surveying from the same institution (2010), and prior degrees in Geoinformatics from The University of Seoul (B.S. 2004, M.S. 2006). Her research interests span GNSS remote sensing applications such as monitoring ionospheric anomalies, hurricane-induced rainfall trajectories, and coastal hazards. She also explores GNSS-Reflectometry (GNSS-R) for sea level changes, storm surge monitoring, and climate-related phenomena. Her work emphasizes real-time data processing, machine learning integration, and interdisciplinary applications in disaster response and geophysical monitoring. Dr. Park has contributed to advancements in GNSS accuracy through projects like JPL's GUARDIAN system and studies on the Galileo High Accuracy Service. Her recent work highlights automated detection systems and deep learning approaches for TID (Traveling Ionospheric Disturbance) identification, reflecting her commitment to bridging geomatics with environmental and disaster resilience challenges.

Ga Wuمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۱۵ مورد دیگر

Ga Wu is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads the Applied Machine Learning Research Lab (DAMLR). His research spans machine learning, artificial intelligence, recommender systems, and large-scale data analytics, with strong industry and interdisciplinary collaborations. Research Interests: Machine Learning and Large-scale Data Analysis Recommender Systems and Personalization AI Safety, Fairness, Accountability, and Transparency Generative AI and Large Language Models Computer Vision (Object Detection on Low-power Devices) AI for Healthcare and Mental Health Physical Dynamic Modeling with ML Bioactive Peptides and Protein Property Understanding His recent publications have been accepted at premier venues such as SIGIR and ICLR, reflecting impactful contributions in information retrieval and deep learning. His work is increasingly focused on ethical, safe, and interpretable AI systems with real-world applications. Scientific Awards & Grants: NSERC RTI Award (2025) Mitacs Elevate Grant (awarded multiple times in 2024–2025) Transforming Climate Action Research Program (TCA) Support (2025) Advising and Funding: Dr. Wu actively supervises graduate students, including PhD candidates and Master’s students like Naihe Feng and Mahtab Sarvmaili. His lab is fully funded and currently recruiting PhD students for projects in computer vision and big data infrastructure in collaboration with industry partners. Labs & Teams: He founded and directs the Applied Machine Learning Research Lab (DAMLR) , which fosters collaboration between academia and industry to advance theoretical and applied AI research.

Peter Bauer-Gottweinمشاهده پروفایل

استاد

• Hydrology
• Water Resources Management
• Hydrogeophysics
• +۹ مورد دیگر

Peter Bauer-Gottwein is a Professor at the Water Systems Department of Space Research and Technology at the Technical University of Denmark. His research focuses on hydrology, water resources management, hydrogeophysics, and remote sensing techniques. Hydrology and Water Resources Hydrogeophysics and Remote Sensing Hydroeconomic Modeling and Satellite Data He actively uses UAV technologies and satellite altimetry in hydrological studies, with recent work on river monitoring and flood forecasting systems. His publications highlight interdisciplinary approaches combining geospatial technologies with environmental management. Current projects include: GNSS reflectometry for river monitoring UAV navigation systems Machine learning for flood prediction Arctic river modeling

• High-Performance Computing
• Parallel Programming
• Domain-Specific Languages
• +۳ مورد دیگر

Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments

Hamid Emami-Meybodiمشاهده پروفایل

دانشیار

• Petroleum Engineering
• Carbon Capture and Storage
• Reservoir Simulation
• +۳ مورد دیگر

Hamid Emami-Meybodi is an Associate Professor at the Pennsylvania State University within the Department of Energy & Mineral Engineering (College of Earth and Mineral Sciences). His research focuses on unconventional reservoirs, hydrocarbon recovery, and carbon dioxide sequestration in geological formations. Research Themes : Climate and Natural Systems, Integrated Energy Systems Affiliations : Affiliate Researcher, International Energy Agency (IEE) Research Interests include hydraulic fracturing, gas transport in nanoporous media, diffusion coefficients, and enhanced oil recovery techniques. His work often integrates machine learning with energy systems analysis. Recent Publications emphasize binary gas transport modeling, CO₂ storage optimization, and unconventional reservoir dynamics, with applications to shale gas, hydrogen storage, and climate impact assessments. Collaborations span institutions in China, Canada, and Kazakhstan, including projects on CCUS (Carbon Capture, Utilization, and Storage) and AI applications in energy industries. Laboratory Focus : His research involves advanced modeling of fluid transport in dual-scale porous media and surface diffusion mechanisms in unconventional reservoirs.

• Metocean Statistics
• Wave Modeling
• Coastal Engineering
• +۵ مورد دیگر

Konstantinos Christakos is a researcher at the Norwegian Meteorological Institute and an adjunct researcher at the Department of Marine Technology, Norwegian University of Science and Technology (NTNU). His work focuses on metocean statistics, numerical wave modeling, and marine renewable energy applications. Doctoral dissertation: Wind-Generated Waves in Fjords and Coastal Areas (University of Bergen, 2021) Master’s thesis: Characterization of the coastal marine atmospheric boundary layer (MABL) for wind energy applications (University of Bergen, 2013) Christakos specializes in wave modeling for coastal and fjord environments, with a focus on wind-wave interactions, inhomogeneous wave dynamics, and open-source software development. He has contributed to tools like metocean-api , DNORA , and metocean-stats for metocean data analysis and visualization. His recent publications and conference presentations address Arctic wave energy resource assessment, downscaled wave hydrodynamics, and fjord-specific modeling challenges. He actively collaborates with institutions like SFI BLUES, SINTEF, and NOAA, and lectures on marine dynamics and stochastic methods at NTNU. Konstantinos participates in interdisciplinary teams and workshops, emphasizing computational modeling, machine learning integration, and environmental engineering for marine structures.