جست‌وجوی استاد و پژوهشگر

Pierre Gentineمشاهده پروفایل

استاد

• Climate Science
• Machine Learning
• Remote Sensing
• +۳ مورد دیگر

Pierre Gentine is a Professor of Geophysics in the Department of Earth and Environmental Engineering at Columbia University, with additional appointments in Earth and Environmental Sciences and Climate. He directs the Center for Learning the Earth with Artificial Intelligence and Physics (LEAP). His research integrates machine learning, remote sensing, and multiscale modeling to address climate change impacts on water cycles, land-atmosphere interactions, and extreme weather events. Key areas include drought forecasting, vegetation-climate feedbacks, and improving Earth system models through AI. Education: PhD (2010) and MSc (2006) in Civil and Environmental Engineering from MIT, and an M.Eng. (2002) in Applied Mathematics from SupAéro, France. Research focuses on advancing climate science through innovative applications of machine learning, such as parameterization of subgrid-scale processes, data assimilation, and climate prediction. His work bridges geophysical turbulence, hydrological extremes, and ecological dynamics, with recent emphasis on urban climate and generative AI for climate modeling. Notable awards include the AGU Macelwane Medal (2022), AGU Fellowship (2022), and multiple NSF/DOE Early Career awards. He leads high-impact projects like the LEAP Center and contributes to global datasets on evapotranspiration, vegetation phenology, and climate extremes. Advising and grants encompass over 22 publications and leadership in interdisciplinary initiatives. His lab (GentineLab) develops open-source tools for climate modeling and AI integration, fostering collaboration across geosciences and computer science.

• Bioinformatics
• Transcriptomics
• Gene Expression Analysis
• +۴ مورد دیگر

David Philip Kreil is an Associate Professor at the Institute of Molecular Biotechnology within the Department of Biotechnology and Food Science at the University of Natural Resources and Life Sciences, Vienna (BOKU) . He holds a joint appointment as Chair of Bioinformatics at the University of Warwick (2012-2014) and has held research positions at the European Bioinformatics Institute (EBI) and the University of Cambridge . His work bridges computational biology, transcriptomics, and plant-nematode interactions. 2012-2014: Chair of Bioinformatics, University of Warwick 2005: WWTF Vienna Science Chair at BOKU 2001-2004: MRC Research Fellow, University of Cambridge 1997-2001: EMBL PhD Programme, EBI Kreil's research focuses on transcriptomic analysis of plant-pathogen interactions (e.g., Fusarium resistance in wheat, nematode-induced syncytia) and computational methods for microarray calibration, data normalization, and statistical modeling. He has applied machine learning to weather forecasting and biomedical data. Scientific Awards : Research Fellow, Darwin College, University of Cambridge (2002-2005) His 15 most recent articles emphasize bioinformatics tools, plant immunity, and interdisciplinary applications of machine learning in biology and climate science. He has supervised theses on wheat transcriptomics and nematode-induced gene expression.

Daniele Zambonمشاهده پروفایل

پژوهشگر

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +۷ مورد دیگر

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

• Environmental Management
• Risk Analysis
• Probability Theory
• +۳ مورد دیگر

Dr. Łukasz Kuźmiński is a Professor at the Department of Process Management within the University of Economics in Wrocław. His research focuses on environmental management, risk analysis, and probabilistic modeling, with notable contributions to flood risk assessment, energy policy, and inter-organizational relationships. He has conducted extensive studies on the Odra River basin’s flood dynamics using extreme value theory and Bayesian networks. His work also explores sustainable energy systems, waste management, and economic instruments for prosumer energy models. Kuźmiński frequently applies statistical methods like kernel densities and stochastic modeling to address challenges in energy consumption, budgetary variance, and software development partnerships. His recent projects include analyzing drought risks, predicting leasing contract defaults via machine learning, and evaluating the potential of Gorzów Wielkopolski as a sustainable river port hub. Education details are not explicitly provided in the text, but his academic ranks (Dr. DSc, Eng.) suggest advanced training in engineering and economics. His publications span interdisciplinary topics, integrating environmental science, mathematics, and business management. Despite prolific output, no student advisees or formal grants are listed in the provided texts. Research trends in his articles emphasize probabilistic quantification, energy transition policies, and cross-border environmental collaboration. Themes of sustainability, risk mitigation, and quantitative analysis recur across his work, reflecting a focus on data-driven solutions for socio-ecological challenges.

Katsu Godaمشاهده پروفایل

دانشیار

• Earthquake Engineering
• Tsunami Risk Assessment
• Seismic Hazard Analysis
• +۵ مورد دیگر

Dr. Katsu Goda is an Associate Professor and Canada Research Chair in Multi-Hazard Risk Assessment at the Department of Earth Sciences, Western University. His research focuses on earthquake and tsunami risk management, combining engineering, financial analysis, and decision-making frameworks. He holds an office in BGS 1076 and can be reached at kgoda2@uwo.ca. Dr. Goda's work spans multidisciplinary areas including probabilistic seismic hazard modeling, tsunami engineering, and financial risk analysis. His research addresses lifecycle cost analysis of structures, seismic risk mitigation techniques, and decision-making under uncertainty. Notable contributions include stochastic finite-fault modeling, seismic loss estimation, and multi-hazard risk assessment for subduction zone earthquakes. His recent articles emphasize advanced methodologies in rupture recurrence analysis, Coulomb stress changes on faults, and tsunami hazard modeling in Makran and Cascadia subduction zones. He has also contributed to studies on earthquake insurance demand in Canada and social vulnerability assessments in coastal regions like Gwadar, Pakistan. Awards: Canada Research Chair (CRC) Tier 2 in Multi-Hazard Risk Assessment Research Themes: Earthquake-Tsunami Interactions Risk-Based Early Warning Systems Financial Instruments for Catastrophe Mitigation Teaching: Courses include Data Analysis in Earth Sciences (ES 2222), Environmental Geophysics (ES 3320), and Natural Catastrophes (GP 9573). Dr. Goda collaborates internationally, with active projects in Japan, Malawi, and Canada. His research integrates cutting-edge computational models with real-world applications to enhance community resilience against natural disasters.

• Extreme Weather Attribution
• Climate Change Impact Analysis
• Statistical Modeling
• +۳ مورد دیگر

Clair Barnes is a Research Associate at Imperial College London's Centre for Environmental Policy within the Faculty of Natural Sciences. She contributes to climate change research through her work at the Grantham Institute and the World Weather Attribution initiative since 2022. PhD in Statistical Science from University College London Her research focuses on improving probabilistic attribution methodologies to quantify climate change impacts on extreme weather events. She specializes in multi-model ensemble forecasts and uncertainty quantification within a multivariate Bayes Linear framework. Recent publications analyze extreme weather attribution across diverse regions, including wildfires in Canada, droughts in the Horn of Africa, and heatwaves in Europe. Her work emphasizes the interplay between climate change and natural variability, with methodological advances in spatial climate modeling and data analytics. Clair actively collaborates on climate research tools like xclim and investigates compound hazards from climate extremes. She participates in interdisciplinary initiatives addressing climate impacts on vulnerable communities globally.

Raj Mehrotraمشاهده پروفایل

پژوهشگر ارشد

• Hydrology
• Climate Change Impact Assessment
• Statistical Modeling
• +۳ مورد دیگر

Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.

• Risk management support
• Energy forecasting
• Energy market modelling
• +۴ مورد دیگر

Dr. Antonio Bello Morales is a Research Associate Professor at the Institute for Research in Technology (IIT) of Comillas Pontifical University's School of Engineering. He holds a Ph.D. in Electrical Engineering (2016) with a dissertation on probabilistic electricity price forecasting. Industrial Engineering (BSc) Electric Power Systems (MSc) Electrical Engineering (Ph.D, 2016) His research focuses on energy systems modeling , with specialization in electricity/gas market forecasting , risk management and artificial intelligence applications . He has developed advanced models for market equilibrium, wind/solar power forecasting, and unit commitment optimization. Recent work includes LSTM-based wind forecasting (2023), computational performance analysis of balance constraints (2024), and multi-area electricity market modeling (2023). Journal publications span IEEE Transactions on Power Systems , Applied Energy , and International Journal of Electrical Power & Energy Systems . Scientific recognition includes: Best Paper Award at IREC 2022 Erasmus Scholarship for research at London Business School (2015) Active in industry collaboration through 133+ projects with Endesa, Enel, and Mercado Ibérico del Gas. Supervises Ph.D. research in renewable energy modeling and market optimization. Participates in editorial work at Energies and MDPI journals.

Roman Krzysztofowiczمشاهده پروفایل

استاد

• Probabilistic Forecasting
• Decision Making Under Uncertainty
• Hydrology
• +۴ مورد دیگر

Roman Krzysztofowicz is a Professor of Systems Engineering and Professor of Statistics at the University of Virginia's College and Graduate School of Arts & Sciences. He joined U.Va.'s faculty in 1982 after holding posts at the University of Arizona and MIT. His research focuses on probabilistic forecasting, decision-making under uncertainty, and Bayesian methodologies applied to hydrology, meteorology, and engineering. He pioneered the Bayesian Forecast-Decision Theory, a unifying framework for probabilistic forecast systems and optimal decision systems. Dr. Krzysztofowicz has authored/co-authored over 120 archival articles, 15 books, and 60+ papers, with publications in prestigious journals like the Journal of Hydrology. He served as Editor-in-Chief of the Journal of Hydrology (1996–2007) and held editorial roles in several other journals. His work spans theoretical advancements and practical applications in flood forecasting, weather prediction, and risk assessment. His research interests include stochastic environmental systems, Bayesian probabilistic models, and the integration of forecasts with decision-making processes. Notable contributions include the Bayesian Processor of Output for probabilistic quantitative precipitation forecasting and the Hydrologic Uncertainty Processor for river stage forecasting. Key Achievements: Recipient of the Presidential Young Investigator Award Eminent Engineer designation from Tau Beta Pi 5 Departmental Teaching Awards Featured in multiple Marquis 'Who's Who' publications His work emphasizes bridging scientific understanding and societal applications, with implications for disaster management, climate adaptation, and infrastructure resilience. He has advised international organizations like the World Meteorological Organization and trained forecasters at the National Weather Service.

Jayasankar C Bمشاهده پروفایل

پژوهشگر ارشد

• Regional Climate Modeling
• Global Climate Modeling
• Climate Change Projections
• +۵ مورد دیگر

Jayasankar C B is a Research Fellow at the Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University, specializing in regional climate modeling and monsoon dynamics. His research focuses on high-resolution climate projections, extreme weather events, and dynamical downscaling techniques to assess climate change impacts. Education: Ph.D. in Atmospheric Sciences (Regional Climate Modeling & Climate Change Projections), AcSIR/CSIR-4PI, Bangalore, India (2013–2019) M.Sc. in Meteorology, Cochin University of Science and Technology (2010–2012) B.Sc. in Physics, University of Calicut (2007–2010) Research Focus: Jayasankar investigates climate variability, monsoon systems, and extreme events using advanced regional coupled models (e.g., WRF-ARW, RSM-ROMS). His work emphasizes improving seasonal forecasts and climate projections for regions like India, Florida, and Central America, with applications in agriculture, hydrology, and disaster preparedness. Awards & Honors: Postdoc Five Minute Research Finalist, FSU (2022) INSPIRE Fellowship, DST India (2013) University First Rank in M.Sc. Meteorology, CUSAT (2012) International Travel Support, SERB India (2019)

• Artificial Intelligence
• Machine Learning
• Time Series Forecasting
• +۷ مورد دیگر

Dr. Antonio Muñoz San Roque is an Ordinary Professor at the Institute of Technological Research (IIT), affiliated with the Comillas Pontifical University, where he also serves as Director of the ICAI School of Engineering since 2018 and previously as Director of IIT since 2008. He teaches in the Department of Electronics, Automation, and Communications and conducts research in Intelligent Systems, focusing on the application of Artificial Intelligence and Machine Learning to industrial monitoring, time series forecasting, and electricity market analysis. His research interests include Machine Learning , Time Series Forecasting , Industrial Process Diagnosis , and Electricity Market Optimization . He applies advanced data analysis techniques to problems in energy systems, predictive maintenance, and smart grids, often combining statistical modeling with domain-specific engineering knowledge. The most recent publications highlight a strong trend in functional data forecasting , neural network interpretability , and energy market modeling . His work spans high-impact journals such as Energy , Applied Soft Computing , and IEEE Transactions on Power Systems , demonstrating consistent contributions to both theoretical methodologies and real-world industrial applications. Director of the award-winning work in the 'Axpo Iberia Award for the best Final Year Project 2023-24' He has advised numerous PhD students, including G. Mestre, J. Portela, and P. Moreno Alonso, and leads multiple industry-funded research projects with companies such as Endesa and Enel. His work integrates academic rigor with practical implementation, particularly in forecasting models for electricity markets and AI-driven industrial diagnostics. He is also involved in academic dissemination through seminars and conference organization, and has contributed to the development of training programs in machine learning for industry partners.

• Atmospheric Sciences
• Climate Modeling
• Bayesian Statistics
• +۳ مورد دیگر

Jana de Wiljes is a researcher at the University of Reading, specializing in interdisciplinary applications of Bayesian statistics and data assimilation. Her work spans atmospheric sciences, medical informatics, and nonlinear dynamics. Published key research on atmospheric circulation regime identification and climate forecasting (2020-2023). Developed Bayesian frameworks for clinical decision-making in chemotherapy. Contributed to causal inference methods in observational time series analysis. Her research demonstrates strong methodological innovation across geophysics and computational pharmacology. No explicit academic rank was stated in the source materials, but her publication record indicates active research contributions.

Jeremy C Phillipsمشاهده پروفایل

استاد

• Volcanology
• Environmental Hazard Assessment
• Risk Management
• +۷ مورد دیگر

Professor Jeremy C Phillips is affiliated with the University of Bristol as a Professor of Volcanology and Natural Hazards at the School of Earth Sciences. He leads projects under the Cabot Institute for the Environment and collaborates internationally with institutions in Ecuador , Colombia , St Vincent and the Grenadines , China , and emerging partners in Peru and DPRK . Research Focus : Physical volcanology with emphasis on lahar dynamics , volcanic ash transport , and hydrometeorological hazard interactions Interdisciplinary risk management frameworks integrating social vulnerability and physical hazard models Development of open-source tools: PlumeRise for ash hazard modeling and LaharFlow for lahar risk assessment Scientific Collaborations : NERC-ESRC projects (STREVA, BRIDGE) GCRF-funded initiatives in volcanic resilience

• Control Engineering
• Automation
• Marine Icing
• +۵ مورد دیگر

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.

Henning Rustمشاهده پروفایل

استاد

• Weather and Climate Statistics
• Extreme Weather Events
• Stochastic Modeling
• +۳ مورد دیگر

Prof. Dr. Henning Rust is a Professor of Statistical Meteorology at the Department of Geosciences, Freie Universität Berlin, and head of the Statistical Meteorology Group. He co-speaks the Hans-Ertel-Centre for Weather Research, funded by the German Weather Service (DWD), and contributes to national and international climate projects like MiKlip and BINGO. His work bridges statistical modeling, extreme event analysis, and climate risk communication. PhD in Theoretical Physics (2007, University of Potsdam) Diplom in Physics (2001, Albert-Ludwigs-Universität Freiburg) BSc with Honours in Physics (1998, Brock University, Canada) Research Interests focus on stochastic precipitation models , extreme convective events , statistical downscaling , and forecast verification . He specializes in time series analysis and long-range dependence in climatological data. His recent publications analyze decadal climate predictability, compound extremes (e.g., ozone-weather interactions), and citizen science applications in weather education. Notable works include skill score decomposition for Atlantic climate phases and flexible IDF curve modeling for urban hydrology. Awards include a summa cum laude PhD and the Distinguished Graduation Student Award for Physics. He supervises PhD and Master’s students on topics like windstorm risk modeling and precipitation extremes . Additional roles: German Meteorological Society (executive board) , MiKlip steering committee , and climate communication via self-built weather station projects.