Hyunjoo Kim Karlsson is a researcher at the Department of Economics and Statistics, School of Business and Economics, Linnaeus University. Her work focuses on statistics and finance, particularly in high-dimensional data analysis, wavelet decomposition, and machine learning applications. Doctoral thesis: Dynamics of macroeconomic and financial variables in different time horizons (2012), Jönköping International Business School. Her research spans shrinkage estimators, outlier detection, time series modeling, and multivariate analysis under multicollinearity. Recently, she has expanded into statistical learning and mixed data sampling (MIDAS) for economic nowcasting. Key publication trends include oil price impacts on economies, exchange rate dynamics, and nonlinear financial modeling using wavelet methods and machine learning. She collaborates with researchers like Krister Månsson and R. Scott Hacker. Hyunjoo is part of the Deterministic and Stochastic Modelling group within Linnaeus University's Data Intensive Sciences and Applications (DISA) center, contributing to interdisciplinary sustainable co-creation projects.
Dr. Menas Kafatos is the Fletcher Jones Endowed Professor of Computational Physics and Director of the Center of Excellence in Earth Systems Modeling and Observations at Chapman University . A physicist, philosopher, and interdisciplinary scholar, he bridges quantum theory, cosmology, and environmental science in his research, with a focus on climate change impacts, natural hazards (wildfires, hurricanes, droughts), and consciousness studies. His career spans over 45 years, including leadership roles at George Mason University and Chapman University as Founding Dean of Schmid College of Science and Technology (2009-2012). Education : B.A. in Physics (1967, Cornell University); Ph.D. in Physics (1972, MIT) His research interests include interdisciplinary Earth systems science , quantum cosmology , and consciousness-quantum theory intersections . He has published extensively in top journals like Nature and Scientific American , with over 340 refereed publications and 23 books. Recent work emphasizes machine learning for climate modeling , pre-earthquake atmospheric signals , and agricultural drought indices . Scientific awards include the Rustum Roy Award (2011) for integrating science and humanity, and IEEE's Leadership Award (2011). He serves on boards like the Universities Space Research Association and OCTANe , and is an honorary member of the Romanian Academy of Sciences .
Oleg Kitov serves as an Assistant Teaching Professor and Robert Martin Fellow in Economics at Selwyn College, University of Cambridge, where he also acts as Convener of Undergraduate Admissions for the Faculty of Economics. His academic profile centers on rigorous econometric methodology applied to macroeconomic phenomena and distributional economics, with significant contributions to time-series analysis and income inequality research. Kitov's primary research domains include Time-series Econometrics, Empirical Macroeconomics, and Income Distribution and Inequality. His methodological innovations focus on structural break detection in linear models using multiplicative indicator saturation, generative modeling of age-income dynamics, and cross-national Phillips curve analysis. His work bridges theoretical econometrics with empirical applications, particularly examining inflation-unemployment relationships across diverse economies and modeling long-term trends in income distribution using historical datasets. His publication trajectory reveals consistent methodological advancement from 2012-2022, with core themes spanning structural break detection in economic time series, generative income distribution modeling, and comparative Phillips curve analysis across 15+ countries. The research demonstrates increasing sophistication in handling non-stationary economic data while maintaining strong policy relevance for central banking and inequality measurement. Scientific Awards: Cambridge Centre for Teaching and Learning Technology-enabled Learning Prize (2021) As Convener of Undergraduate Admissions, Kitov shapes the academic cohort for Cambridge's Economics program while teaching advanced econometrics. His office hours (Tuesdays 11:15am-12:15pm in Room 79) support student engagement with complex quantitative methods, reflecting his dual commitment to research excellence and pedagogical innovation recognized by the 2021 teaching prize.
Anna Simoni is a Senior Researcher at CNRS/CREST and Professor of Econometrics and Statistics at ENSAE and École Polytechnique. She is a CNRS Research Fellow and Fellow of Hi! Paris and Institut Louis Bachelier. Her research spans econometrics, machine learning, and AI, focusing on high-dimensional models and Bayesian inference. Education: PhD in Economics, Toulouse School of Economics (2009) Habilitation à Diriger de Recherche (HDR), Toulouse School of Economics (2017) Research Interests: Her work integrates econometrics with machine learning to develop statistical methods for big data, including Google search data for macroeconomic forecasting and causal inference with minimal assumptions. Grants and Awards: She received the CNRS Bronze Medal in 2019 and leads the ANR-funded project "Moment Conditions Models and Bayesian Inference for Policy Evaluation" (2021-2026).
Steven Miller is a Professor in the Department of Atmospheric Science at Colorado State University (CSU) and serves as Director of the Cooperative Institute for Research in the Atmosphere (CIRA). He holds a Ph.D., M.S., and B.S. from CSU, UC San Diego (Electrical Engineering), and has led CIRA since 2021. His career includes roles as Senior Research Scientist at CIRA (2007–2021) and Senior Satellite Meteorologist at the Naval Research Laboratory (2000–2007). Education: Ph.D. in Atmospheric Science, Colorado State University (2000) M.S. in Atmospheric Science, Colorado State University (1997) B.S. in Electrical and Computer Engineering, University of California, San Diego (1995) Research Focus: Miller specializes in satellite algorithm development for nighttime phenomena, including aerosol detection, atmospheric gravity waves, bioluminescent marine events, and decision-support tools for weather forecasting. His work bridges applied research and operational use, particularly for NOAA and the Department of Defense. Key Contributions: His innovations include the GeoColor algorithm for geostationary satellites, optical flow techniques for tracking convective systems, and lunar-light mapping for nighttime aerosol studies. His research also explores human behaviors via nighttime lighting and marine bioluminescence detection. Awards: 2017 CO-LABS Governor’s Award for GeoColor algorithm 2017 NASA Group Achievement Award (GOES-R Team) 2012 NASA Award (Suomi NPP VIIRS Team) 2005 Alan Berman Award for Milky Sea discovery Labs/Teams: As CIRA Director, he oversees interdisciplinary projects at NOAA’s Cooperative Institute, fostering collaboration between academia, government, and industry.
Roland Potthast is a Full Professor for Applied Mathematics at the University of Reading and Director of Meteorological Analysis and Modeling at the German Weather Service (DWD). His roles include leading the FE1 department at DWD, responsible for numerical weather prediction (NWP), Earth System Models (ESM), and the ICON model development. He holds a PhD and Habilitation from the University of Göttingen and has held academic positions at institutions such as the University of Reading, Research Center Jülich, and Université de Rennes. His research focuses on inverse problems, data assimilation, and numerical weather prediction. Key areas include the development of high-resolution models like ICON, ensemble data assimilation methods, and applications in climate science and renewable energy. He has contributed to projects like the ESM initiative with GeoInfoDienst BW and the BeCoM Project (EU-funded). Publications span advancements in data assimilation techniques, particle filters, and applications of machine learning in weather forecasting. Awards include the Pichorides Lectureship and grants from DFG, EPSRC, and Volkswagen Foundation. Potthast leads interdisciplinary teams at DWD and collaborates with international organizations like ECMWF and the German Climate Computing Center (DKRZ). His work bridges operational weather modeling and academic research, emphasizing seamless prediction systems and digital twins.
Seba Contreras is a Postdoctoral Researcher in the Physics of Disease Spread at the Max Planck Institute for Dynamics and Self-Organization . His work bridges mathematical modelling , dynamical systems , and infectious disease epidemiology to uncover principles in outbreak controllability and disease dynamics. Dr. rer. nat. (Physics of Biological and Complex Systems, 2023) - Georg-August-Universität Göttingen MSc (Extractive Metallurgy, 2019) - Universidad de Chile BEng & Dipl.-Eng. (Civil Engineering, 2017) - Universidad de Chile His research integrates complex systems and infectious diseases with a focus on: Non-pharmaceutical interventions during pandemics Competition and co-infection dynamics between diseases Information-disease feedback loops Statistical methods for epidemiological data correction Parameter inference from limited medical datasets Machine learning applications in protein engineering Recent publications demonstrate expertise in machine learning for biological datasets, epidemic models with heterogeneous populations, and data-driven public health policy . Collaborations span human genetics , theoretical ecology , protein engineering , and social sciences across institutions in Chile and Germany.
Ardeshir Ebtehaj is an Associate Professor at the University of Minnesota's College of Science & Engineering, Department of Civil, Environmental and Geo-Engineering. He leads the Hydrologic Sciences and Remote Sensing (HydSens) Laboratory at the Saint Anthony Falls Laboratory (SAFL) and serves as Editor of the Journal of Hydrometeorology. His research integrates physical models with data science to address sustainable water, food, and energy systems. Education: Postdoctoral Research, Georgia Institute of Technology Ph.D., University of Minnesota, Water Resources and Hydrology M.Sc., University of Minnesota, Mathematics M.Sc., Iran University of Science and Technology, Environmental Engineering M.Sc., Iran University of Science and Technology, Structural Engineering B.Sc., Iran University of Science and Technology, Civil Engineering Research Interests: Electromagnetic hydrology, satellite hydrometeorology, microwave remote sensing, inverse problems, land-atmosphere interactions, climate intelligence, and machine learning applications for water and environmental monitoring. Awards & Grants: Recent funding from NASA (Arctic ecohydrology modeling, SMAP program) and USACE (Harmful Algal Blooms detection). Collaborations: HydSens team at SAFL, NASA Goddard Space Flight Center, University of Alaska.
Dr. Maria-Valasia Peppa is a researcher at Newcastle University's School of Civil Engineering and Geosciences . Her work focuses on remote sensing, photogrammetry, and geospatial technologies, with applications in hydrodynamic modeling, landslide monitoring, and urban infrastructure analysis. Key Research Areas: UAV-based photogrammetry, glacial lake dynamics, digital elevation models (DEMs), and environmental data uncertainty. Recent Trends: Integration of deep learning with CCTV for traffic analysis, calibration of multisensor UAV systems, and real-time flood nowcasting frameworks. Collaborations: Active partnerships with Professors Jon Mills, Claire Walsh, and international institutions in Africa and Nepal. Applications: Cultural heritage mapping, water security strategies, and urban air quality modeling.
Aureo de Paula is a Professor of Economics at University College London (UCL), Department of Economics. He holds positions at several prestigious research institutions including the Centre for Microdata Methods and Practice (UK), the Institute for Fiscal Studies (UK), and CEPR. He is an elected Fellow of the Econometric Society and the International Association for Applied Econometrics, and a Turing Fellow (2021/23). His educational background includes: B.A. and M.Sc. in Economics from Pontificia Universidade Catolica—RJ (Brazil) in 1996 and 2000 M.A. and Ph.D. in Economics at Princeton University in 2002 and 2006 Aureo de Paula is an applied econometrician whose research focuses on the intersection of applied economic theory, econometrics and empirical microeconomics. His work spans methodological questions like the identification and estimation of multi-agent models, and empirical applications, mostly problems in developing nations and industrial organisation. His recent publications demonstrate expertise in network formation, environmental economics, health economics, and econometric methodology. His research has been featured in prominent media outlets including The Times, The Daily Mail, Economics Observatory, WSJ, and Chicago Booth Review. Professor de Paula has held significant editorial positions, including: Co-editor at the Journal of Econometrics (since 2023) Director for the Review of Economic Studies (2020-2023) Associate editor for The Review of Economic Studies, Journal of Business and Economic Statistics, Econometrics Journal, Econometric Reviews, and the Journal of Econometrics His scientific awards and recognitions include: Elected Fellow of the Econometric Society Elected Fellow of the International Association for Applied Econometrics Irving B. Kravis Award for Excellence in Undergraduate Teaching at the University of Pennsylvania Faculty Education Award at UCL (joint with Dunli Li) ERC Starting Grant (2013) Turing Fellow (2021/23) Professor de Paula serves in important leadership roles, including as an elected member of the Econometric Society Council and as chair of its Latin America Regional Standing Committee from 2022 until 2025. His research has been supported by various grants, including the ERC Starting Grant, and he has collaborated with institutions like the Office for National Statistics and The Alan Turing Institute.
Istvan Szunyogh is a Professor in the Department of Atmospheric Sciences within the College of Geosciences at Texas A&M University, where he has held faculty positions since 2009. His research focuses on advancing numerical weather prediction through innovative integration of machine learning, statistical techniques, and physical modeling for Earth's atmosphere and complex systems. His academic foundation includes: Ph.D. in Earth Sciences from the Hungarian Academy of Sciences, Budapest Diploma in Meteorology from Eötvös Loránd University, Budapest Szunyogh's research spans Numerical Weather Prediction (NWP), Earth System Modeling (ESM), Data Assimilation (DA), Machine Learning applications, and predictability studies of atmospheric and oceanic systems. His group pioneers hybrid modeling approaches that combine physics-based frameworks with machine learning to overcome traditional limitations in weather forecasting, particularly for medium-range and subseasonal predictions. This work bridges atmospheric dynamics, computational science, and artificial intelligence to address fundamental challenges in predictability. Analysis of his recent publications reveals a dominant trend toward developing hybrid physics-machine learning models for atmospheric and oceanic prediction beyond conventional medium-range limits. These studies focus on capturing complex dynamical processes, improving subseasonal forecasting, and enhancing model error correction through innovative data assimilation techniques. His scientific leadership has been recognized with prestigious awards: College of Geosciences 2017 Distinguished Achievement Award for Faculty Excellence in Research Certificate of Recognition from U.S. THORPEX Executive Committee (2015) for international leadership in predictability research Certificate of Appreciation from WMO WWRP (2014) for outstanding contributions to the THORPEX program Szunyogh actively mentors the next generation of atmospheric scientists, having advised numerous graduate students including J. Pathak, A. Wikner, E. Forinash, T. Arcomano, M. J. Kavulich, E. Satterfield, A. V. Zimin, and M. Corazza. His research group maintains strong collaborations with national weather prediction centers including NCEP and international programs under the World Meteorological Organization, securing consistent funding for cutting-edge atmospheric research. He leads a dynamic research team at Texas A&M that operates at the intersection of traditional atmospheric science and modern computational techniques, fostering an environment where theoretical exploration directly informs practical forecasting improvements for complex Earth system phenomena.
Dr. Angela Meyer is an Assistant Professor of Energy Meteorology and Artificial Intelligence at TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing since October 2023. She concurrently leads the Energy Weather & AI Lab at the Bern University of Applied Sciences (BFH), School of Engineering and Computer Science. She earned her PhD in atmospheric physics from ETH Zurich (2015) and a master’s degree in mathematics from the University of Cambridge (2009). Research Focus: Intersection of data science, atmospheric science, and renewable energy applications. Machine learning for solar and wind energy forecasting. Federated learning for privacy-preserving wind turbine condition monitoring. Satellite-based solar radiation retrieval and bias correction. Probabilistic intraday and sub-seasonal forecasting. Her research is supported by major grants from the Swiss National Science Foundation (SNSF) and Innosuisse , and she is a project partner in the Horizon Europe UrbanAIR initiative. Scientific Contributions: Over 40 peer-reviewed publications since 2015 in journals such as Applied Energy , Solar Energy , Energy and AI , and Journal of Climate . Key publications include advances in deep generative models for solar forecasting, federated learning in renewable energy, and AI-based satellite retrieval of solar radiation. Active reviewer for Applied Energy , Energies , and program committee member for ECML PKDD and LOD conferences. Research Team & Supervision: Dr. Meyer currently supervises six PhD candidates and six postdoctoral researchers across her labs at TU Delft and BFH. Her group focuses on AI-driven solutions for renewable energy reliability and resilience. Laboratories & Collaborations: Energy Weather & AI Lab – Bern University of Applied Sciences. GRS Lab – TU Delft, Department of Geoscience and Remote Sensing. Active collaborations with ETH Zurich, Siemens Smart Infrastructure, Hexagon AB, and NVIDIA. For more information, visit her personal website or ResearchGate profile .
Dr. Aubrey Poon is a Senior Lecturer in Econometrics at the School of Economics, University of Kent, with affiliated researcher positions at Örebro University (Sweden), Centre for Applied Macroeconomic Analysis (Australian National University), and UK Economic Statistics Centre of Excellence. He completed his PhD in Economics from the Australian National University in 2017. His primary research focuses on Applied Macroeconometrics , specializing in Bayesian estimation methodologies including: Mixed-frequency analysis techniques Non-linear state-space modeling Quantile regression frameworks Macroeconomic forecasting systems His work has been featured in prominent outlets including The Economist and New York Times. Publication analysis reveals strong thematic consistency in econometric innovation, with recent works emphasizing: Advanced Bayesian VAR methodologies International financial market interconnections Macroeconomic tail risk quantification Regional economic nowcasting techniques The research demonstrates growing sophistication in handling high-dimensional data structures and missing data problems across global economies. Dr. Poon maintains active research collaborations across multiple international institutions focusing on macroeconomic measurement and policy analysis.
Marina Astitha is an Associate Professor at the University of Connecticut's College of Engineering , specifically within the School of Civil and Environmental Engineering . Her research bridges atmospheric science with practical applications in energy systems and environmental management. Ph.D. in Physics from the University of Athens (2007) Specializes in high-resolution weather modeling and machine learning integration Research Interests include: Atmospheric Physics, Dynamics, and Chemistry Extreme Weather Event Prediction Multi-Media Modeling Systems Uncertainty Quantification in Atmospheric Models Climate Change Impacts on Wind Energy Resources Real-Time Weather and Air Quality Forecasting Scientific Awards : No awards explicitly mentioned in the provided data, but her publications and research activities indicate significant contributions to meteorology and environmental engineering fields. Advising and Grants : Specific details about grants and students are not provided in the available information, though her research focus suggests involvement in funded projects related to climate change, renewable energy, and environmental modeling. Labs and Teams : Leads the Atmospheric Modeling Group at UConn, integrating numerical weather prediction with machine learning techniques for environmental and energy applications.
Dr. Yang Du is a Senior Lecturer at James Cook University (JCU) in Cairns, Australia, specializing in Electronic Systems and IoT Engineering. He holds a Ph.D. in Electrical Engineering from The University of Sydney (2013) and has held academic positions at Xi'an Jiaotong-Liverpool University (2014–2018) and a visiting scientist role at MIT (2018). His research focuses on renewable energy systems, solar forecasting, and smart grid technologies. He has been recognized in the World’s Top 2% Scientists List by Stanford University and has authored numerous publications in top-tier journals. Education: Ph.D. in Electrical Engineering, The University of Sydney, Australia (2013) Postdoctoral Research Fellow at Masdar Institute of Science and Technology, UAE (2013–2014) Dr. Du’s research interests revolve around optimizing renewable energy integration, particularly in photovoltaic systems, energy storage, and advanced control strategies using machine learning. His work emphasizes predictive modeling for solar power ramp-rate control, federated learning frameworks for distributed energy systems, and the application of AI in IoT-driven energy management. Recent projects include ADMM-LSTM frameworks for load forecasting and thermal analysis of power devices. His publications highlight contributions to energy forecasting, grid stability, and adaptive control mechanisms. Notable achievements include developing solar forecasting models using GANs and sky images, and exploring peer-to-peer energy trading mechanisms with penalty adaptations. He actively collaborates with institutions like MIT and maintains an honorary position at Xi’an Jiaotong-Liverpool University. Dr. Du’s work addresses challenges in energy systems, such as mitigating PV power fluctuations and enhancing grid resilience through predictive analytics. His research has practical applications in microgrids, energy storage optimization, and sustainable power distribution. He has been awarded 33 academic accolades, including recognition for his impactful contributions to renewable energy research.
