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Christian B. Hansen is the Wallace W. Booth Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. He serves as the academic coordinator for Booth's Sokolov Executive MBA Program and is co-editor of the Journal of Political Economy - Microeconomics. Hansen joined the Chicago Booth faculty in 2004 after completing his PhD at MIT. Education: PhD in Economics, Massachusetts Institute of Technology (2004) Bachelor's degree in Economics, Brigham Young University (2000) Professor Hansen specializes in applied and theoretical econometrics, with research focusing on high-dimensional statistical methods in economic applications, panel data models, clustered standard errors, quantile regression, and weak instruments. His most recent work explores the application of machine learning and artificial intelligence techniques to estimate causal and policy effects. Hansen teaches courses including Applied Econometrics, Machine Learning, and Statistics at Chicago Booth. Scientific Awards and Honors: Neubauer Family Faculty Fellow at Booth NSF research grant recipient National Science Foundation graduate research fellow during PhD studies Hansen has published in leading journals including the American Economic Review, Annals of Statistics, Econometrica, Journal of Business and Economic Statistics, Journal of Econometrics, Review of Economics and Statistics, and Review of Economic Studies. He is currently working on a book titled "Applied Causal Inference Powered by ML and AI" with Victor Chernozhukov, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis.

• applied and theoretical econometrics
• high-dimensional data analysis
• causal inference using machine learning
• +۴ مورد دیگر

Gustavo Deco is a Research Professor at the Institució Catalana de Recerca i Estudis Avançats (ICREA) and holds a Professorship (Catedrático) at Pompeu Fabra University (UPF). He leads the Computational Neuroscience Group and directs the Center of Brain and Cognition at UPF. His research focuses on computational models of brain dynamics, integrating biophysics, neuroimaging, and complex systems principles. Deco’s academic journey includes a PhD in Physics (1987, thesis on Relativistic Atomic Collisions), postdoctoral work at the University of Bordeaux (France) and University of Giessen (Germany), and a Habilitation in Computer Science (1997, Technical University of Munich). He has led computational neuroscience research at Siemens Corporate Research Center (1990–2003) and pioneered whole-brain modeling frameworks like The Virtual Brain (TVB). His research interests span critical brain dynamics, non-equilibrium thermodynamics in neural systems, psychedelics’ effects on brain hierarchy, and clinical applications of computational models in disorders such as Alzheimer’s and depression. Recent work emphasizes turbulence-like dynamics in healthy and diseased brains, and biomarker discovery using AI-driven simulations. Deco’s articles (2024–2025) explore topics like entropy production in brain networks, psychedelics-induced flattening of functional hierarchies, sleep-like dynamics post-stroke, and the role of long-range connections in global brain communication. His work bridges theoretical models with clinical insights, aiming to advance personalized neurology and digital brain research.

• Computational Neuroscience
• Neuroscience
• Biophysics
• +۳ مورد دیگر

Jordi is an Associate Professor at the University of Valencia, specializing in interdisciplinary research at the intersection of artificial intelligence, environmental science, and education technology. His work focuses on applying machine learning to climate forecasting, vegetation mapping, and improving educational tools through AI-driven methodologies. Research interests include AI applications in hurricane and medicane prediction, causal inference for environmental displacement, satellite data analysis for crop yield monitoring, and leveraging social media for climate studies. He also explores technology adoption in higher education, particularly using short videos and learning management systems. His recent publications highlight advancements in GPT-4 for educational assessment tools, Gaussian processes for vegetation trait mapping, and physics-aware ML models for carbon flux analysis. Jordi has contributed to initiatives like enhancing Sentinel-2 data processing and developing hybrid predictive tools for extreme weather events. His work emphasizes interdisciplinary collaboration, blending signal processing, remote sensing, and statistical learning to address global challenges in agriculture, climate resilience, and sustainable development.

• Artificial Intelligence
• Remote Sensing
• Climate Science
• +۳ مورد دیگر

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +۹ مورد دیگر

Jordi Muñoz is an Associate Professor at the University of Valencia's Department of Electronic Engineering, part of the Faculty of Electronics Engineering. He has been affiliated with the university since 1998 and is a member of the Image and Signal Processing Group (ISP) and Image Processing Laboratory (IPL). His research focuses on signal processing, machine learning, and remote sensing applications in Earth observation, climate science, and vegetation monitoring. He has authored over 170 publications and holds leadership roles in interdisciplinary projects involving AI-driven environmental modeling and educational technology. Research Interests: Through advanced machine learning techniques, Muñoz explores topics such as medicanes forecasting, drought displacement patterns, crop yield estimation, and vegetation parameter retrieval using satellite data. His work bridges signal processing fundamentals with practical applications in climate science and education innovation. Key Projects: Recent contributions include AI models for medicanes monitoring (2024), causal discovery in environmental displacement studies (2024), and GPT-4-based educational tools for automated question generation (2024). He collaborates extensively with institutions like the European Space Agency (ESA) and global research networks. Labs/Teams: Active member of the Image and Signal Processing Group (ISP), leading projects on Gaussian processes for biophysical parameter estimation and cloud detection algorithms. His team develops algorithms for Earth observation data fusion and interpretable machine learning models in geosciences.

• Signal Processing
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Adriano Jose Carmona serves as Full Professor at Universitat Politècnica de Catalunya (UPC-BarcelonaTech) within the Department of Signal Theory and Communications, College of Telecommunications Engineering. He holds dual affiliations with the Remote Sensing Lab (RSLAB) and NanoSat Lab, while also maintaining a visiting professorship at UAE University since 2022. His research spans microwave remote sensing , GNSS-reflectometry , RF interference mitigation , and nanosatellite development . Notable projects include the 3Cat series of CubeSats (3Cat-1 through FSSCAT), with FSSCAT producing the first scientific-quality Arctic soil moisture and sea ice maps using CubeSat technology. His work integrates signal processing innovations with practical Earth observation applications. Analysis of his 15 most recent publications reveals dominant trends in High-resolution soil moisture mapping using neural networks Earthquake precursor detection through multi-sensor thermal anomalies Advanced RFI mitigation techniques for radiometers CubeSat constellation architectures for 6G networks Wildfire burned area prediction using causal models Honors include IEEE Fellow (2011), ICREA Academia Award (2009, 2015), and the IEEE GRSS Education Award (2021), alongside multiple technology transfer prizes like the ESA Sentinel Small Satellite Challenge (2017). As research advisor, he has supervised 29 completed PhD theses (with 9 ongoing), producing professionals now at NASA/JPL, University of Michigan, and founders of startups BALAMIS and MITICS. His leadership extends to directing the CommSensLab Research Center (2016-2020) and serving as past President of the IEEE Geoscience and Remote Sensing Society.

• Microwave Remote Sensing
• GNSS-R
• Radio Frequency Interference Detection
• +۴ مورد دیگر

Dr. Elisa Sicuri is an Associate Research Professor at the Barcelona Institute for Global Health (ISGlobal), where she leads the Global Health Economics Group. She also holds positions as Associate Professor at the University of Barcelona, Visiting Senior Fellow at LSE Health (London School of Economics and Political Science), and Associated Researcher at the Manhiça Health Research Centre (CISM) in Mozambique. Her research focuses on health economics in low- and middle-income countries, with a special emphasis on infectious diseases such as malaria, Chagas disease, schistosomiasis, and neglected tropical diseases. She conducts economic evaluations, cost-effectiveness analyses, and impact assessments of public health interventions, particularly those related to disease elimination and vaccine implementation. Dr. Sicuri leads health economics workstreams in several major international projects funded by EC H2020, EDCTP, and Inserm-ANRS—including ORCHESTRA, Cruzivax, REVERSE, and EBO-PEP. Her recent publications demonstrate strong methodological rigor and policy relevance, with a growing focus on diagnostics, antimicrobial resistance, and integrated screening programs. She frequently employs advanced statistical and modeling techniques, including privacy-preserving analytics and geospatial modeling. Scientific Expertise: Global Health Impact Evaluation Economic Evaluation Infectious Diseases She advises on major health policy initiatives and has contributed to investment cases for hepatitis B and lymphatic filariasis elimination. Her work often involves collaboration across Africa, Latin America, and Europe, with field sites in Mozambique, Madagascar, Guyana, and Spain. Dr. Sicuri is actively involved in research teams and centers including ISGlobal, CISM, and LSE Health, contributing to interdisciplinary efforts in disease control and health systems strengthening.

• Health Economics
• Global Health
• Infectious Diseases
• +۱۲ مورد دیگر

Miguel Ángel Fernández Torres is an Assistant Professor in the Department of Signal Theory and Communications at the Polytechnic School of Universidad Carlos III de Madrid, Spain. He also holds a Senior Researcher position at the University of Valencia within the Image and Signal Processing Group, where he contributes to major climate and Earth science projects such as USMILE, XAIDA, and ESA DeepExtremes. His educational background includes a Ph.D. in Multimedia and Communications (2019), a Master's in the same field (2014), and a B.S. in Audiovisual System Engineering (2013), all from Universidad Carlos III de Madrid, where he received several academic excellence awards. He completed research stays at Purdue University and Technische Universität München, broadening his international research experience. His research focuses on Machine Learning for Earth and Climate Sciences, particularly in designing explainable deep generative models and attention mechanisms for anomaly and extreme event detection. His work spans computer vision, biomedical image analysis, and climate modeling, with a strong emphasis on interpretability and real-world impact. His recent publications reflect a clear trend toward applying deep learning to environmental challenges such as drought, wildfire, and climate extremes, often incorporating causal inference and model explainability. His scientific achievements include being an ELLIS member since 2022 and receiving teaching certifications from AVAP and ANECA. He has been awarded the FPU grant from the Spanish Ministry of Education and multiple excellence awards throughout his academic career. He has co-supervised several Ph.D. students at the University of Valencia and supervised numerous B.S. and M.S. theses at Carlos III University, covering topics like autism diagnosis, melanoma detection, and autonomous driving perception. He has secured competitive grants from ESA, H2020, and Climate Change AI, and actively serves as a reviewer for top journals and conferences in his field. He is involved in significant research groups and projects, including the Image and Signal Processing Group at the University of Valencia and the Multimedia Processing Group at Carlos III University, working on funded initiatives like H2020 XAIDA and ESA DeepExtremes, which aim to advance climate services and hazard prediction using AI.

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Panagiotis Toulis (Panos) serves as Associate Professor of Econometrics and Statistics and John E. Jeuck Faculty Fellow at the University of Chicago Booth School of Business, where his research focuses on model-agnostic causal inference methods for complex networked environments using randomization techniques that provide robustness beyond classical statistical approaches. His academic credentials include: Ph.D. in Statistics from Harvard University (2016) under Edo Airoldi, David Parkes, and Don Rubin Master’s in Computer Science from Harvard University (2011) under David Parkes M.Eng. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece (2006) Toulis pioneers randomization and permutation tests for causal inference under network interference, developing methods applicable to experimental design in complex domains where traditional model-based approaches fail. His work bridges statistics , optimization , and economics , with significant contributions to invariance-based inference for observational studies and machine learning integration through stochastic gradient descent frameworks. This research has practical applications in business analytics, vaccine distribution, and economic policy. Analysis of his 15 most recent publications (2023-2025) reveals dominant trends in extending randomization tests to handle network spillover effects, two-sided market experiments, and temporal dynamics with treatment habituation. A growing emphasis on machine learning assistance for complex treatment effect detection demonstrates his innovative fusion of statistical rigor with computational scalability, consistently providing finite-sample theoretical guarantees uncommon in asymptotic-focused literature. Toulis has earned recognition through prestigious awards: Arthur P. Dempster Award from Harvard University’s Department of Statistics LinkedIn Economic Graph Challenge award 2012 Google United States/Canada PhD Fellowship in statistics He teaches core Booth courses including Applied Regression Analysis (41100), Causal Inference for Business Applications (41207), and the Econometrics and Statistics Colloquium (41600), while maintaining active industry connections through prior software engineering roles at Google and Greek startups. His research appears in top journals including Journal of the Royal Statistical Society and Annals of Statistics, addressing critical methodological challenges in modern data science. No dedicated laboratory structure is documented, but his work inherently involves collaborative research networks across statistics, economics, and computer science disciplines through the Booth School’s academic infrastructure.

• Causal Inference
• Experimental Design
• Randomization Methods
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Gustau Camps-Valls is a Full Professor in Electrical Engineering at the University of Valencia and Group Leader of the Image and Signal Processing (ISP) group at the Image Processing Laboratory (IPL). His research focuses on advancing artificial intelligence (AI) and machine learning for Earth observation, climate science, and environmental applications. He has held prominent roles including IEEE Distinguished Lecturer (2017-2020) and IEEE Fellow (2018), and serves as an ELLIS Fellow and Program Coordinator. His work addresses challenges like climate risk prediction, drought detection, and sustainable development through interdisciplinary approaches combining causal inference and geospatial data analysis. Research interests span AI-driven climate modeling, causal discovery in environmental systems, and geoscience applications of deep learning. Notable contributions include developing frameworks for extreme weather prediction, soil organic carbon inference, and food insecurity modeling. His recent articles explore variational autoencoders for heatwave analysis, geospatial foundation models for sustainability, and robust machine learning for noisy Earth observation data. Awards: IEEE Fellow (2018), IEEE Distinguished Lecturer (2017-2020), ELLIS Fellow Labs/Teams: Image Processing Laboratory (IPL), Image and Signal Processing (ISP) group Grants and collaborations focus on bridging AI with Earth sciences, including projects on climate extremes, digital twins, and humanitarian aid impact analysis. His work emphasizes explainable AI and causal reasoning to ensure scientific rigor in environmental decision-making.

• Artificial Intelligence
• Climate Science
• Earth Observation
• +۵ 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.

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