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Manuel Arellano is Professor of Economics at the Center for Monetary and Financial Studies (CEMFI) in Madrid since 1991, with prior appointments at the University of Oxford (1985-89) and London School of Economics (1989-91). A leading econometrician specializing in panel data analysis, his work bridges theoretical econometrics and labor economics applications. He earned his undergraduate degree from the University of Barcelona and Ph.D. from the London School of Economics. Arellano's research focuses on econometric methodology for panel data, particularly dynamic models with heterogeneity. His seminal book Panel Data Econometrics (2003) established foundational frameworks for nonlinear and dynamic panel estimation. Current work extends to distributional analysis of random coefficients and robust inference under uncertainty, maintaining consistent emphasis on labor market applications like unemployment duration and policy evaluation. His publication history reveals a 30-year trajectory advancing panel data econometrics, evolving from specification testing (1987-1995) to sophisticated dynamic and nonlinear models (2003-2014), with persistent focus on practical implementation and labor economics applications. Major honors include: President of the Econometric Society (2014) Foreign Honorary Member of the American Academy of Arts and Sciences (2014) Rey Jaime I Prize in Economics (2012) ISI Highly Cited Researcher status (2010) Fellow of the Econometric Society (2002) No information on student advising or research grants appears in the source materials. Similarly, details about research laboratories or collaborative teams are not documented in the provided texts.

• Econometrics
• Panel Data Analysis
• Labour Economics
• +1 more

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +5 more

Jan Greve is a Researcher at the Institute for Statistics and Mathematics at Wirtschaftsuniversität Wien (Vienna University of Economics and Business). His research focuses on Bayesian statistical methods, including cluster analysis, mixture models, and metadata anonymization. He has contributed to both theoretical advancements and practical software tools in computational statistics. Greve's recent work includes developing novel approaches for partition structures in Bayesian nonparametrics and creating anonymization techniques for text corpora. He has authored/co-authored peer-reviewed articles and preprints, alongside software contributions like the 'fipp' package for Bayesian mixture models. He has actively participated in academic activities such as presenting on topics like 'Mixtures of finite mixtures and the telescoping sampler' at international conferences. His research bridges theoretical statistics with applied data science challenges.

• Bayesian Statistics
• Machine Learning
• Data Clustering
• +3 more

Florian Huber is a Professor of Economics and Vice-Head of the Department of Economics at the University of Salzburg. His research focuses on Bayesian macroeconometrics, particularly large-scale non-linear multivariate time series modeling. He has published in top journals such as the Journal of Econometrics and the Journal of Applied Econometrics. His work integrates machine learning and nonlinear methods to analyze macroeconomic dynamics and uncertainty. Huber serves as a Scientific Consultant to the Oesterreichische Nationalbank (OeNB), European Central Bank (ECB), and European Commission. He holds roles as Associate Editor of Macroeconomic Dynamics, Senior Scientist at the International Institute for Applied Systems Analysis (IIASA), and Research Fellow of Bocconi University’s Baffi Center. His accolades include the 2024 Kurt-Zopf-Förderpreis and Fellow status in the Society for Economic Measurement. Research interests span Bayesian econometrics, state space modeling, forecasting, and financial spillovers. His recent work addresses topics like growth-at-risk, nonlinear VAR models, and real-time inflation forecasting. Huber’s contributions bridge theoretical econometrics with policy-relevant analysis, emphasizing the integration of big data and structural models.

• Bayesian Econometrics
• Macroeconomics
• State Space Modeling
• +3 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Michael Pfarrhofer is an Assistant Professor at WU Vienna University of Economics and Business, Department of Economics, specializing in econometrics and macroeconomics. He previously held positions at the Universities of Vienna and Salzburg and contributes to the European Commission’s Joint Research Centre (JRC) in Ispra. His research focuses on Bayesian econometrics, time series analysis, forecasting, and empirical macroeconomics, with applications to business cycles and policy evaluation. Current Role: Tenure-track Assistant Professor Primary Affiliation: WU Vienna University of Economics and Business Collaborations: JRC (European Commission) Research interests include macroeconomic forecasting using advanced Bayesian methods, nonlinear time series analysis, and the impact of climate shocks on financial markets. His work bridges econometric theory and empirical applications, often involving large datasets and machine learning techniques. Publications span topics like tail-risk prediction, international financial spillovers, and nowcasting methodologies. He teaches courses such as Econometrics II and Economic Modeling, emphasizing practical applications of econometric tools. While no specific scientific awards are listed, his contributions to leading journals (e.g., Journal of Econometrics , Journal of Applied Econometrics ) highlight his scholarly impact. Advising and grant activities are not detailed in the provided texts, though his research often involves collaborative projects with institutions like the JRC.

• Bayesian Econometrics
• Time Series Analysis
• Forecasting
• +3 more

Elahe Ghalebi is a researcher affiliated with TU Wien's Cyber-Physical Systems Research Area (E191-01). She holds a PhD from TU Wien, awarded in 2020 for her thesis on time-evolving sparse networks. Her work focuses on advanced statistical techniques and machine learning applied to network dynamics, temporal graphs, and distributed systems. She collaborates internationally with experts like Prof. Radu Grosu and Jure Leskovec. Research interests include modeling sparse networks, Bayesian nonparametric methods, compressive sensing for network analysis, and dynamic network reconstruction from partial data. Her contributions bridge theoretical network science with practical applications in cyber-physical systems and social networks. Publications span 2018–2020, emphasizing temporal multigraph analysis, edge clustering, and wafermap pattern recognition via variational autoencoders. Her work often combines computational modeling with signal processing techniques to address challenges in distributed systems and network topology inference. Affiliated with TU Wien's Network Lab, her research aims to develop scalable algorithms for analyzing evolving networks under resource constraints. No notable awards are listed, though her active collaboration network suggests strong academic engagement.

• Network Science
• Machine Learning
• Data Analysis
• +3 more

Samuele Tosatto is an Assistant Professor in the Computer Science Department at the University of Innsbruck and affiliated with the Digital Science Center. His research focuses on enabling robots to learn in real-world environments through reinforcement learning and abstraction techniques, addressing challenges in scalability and efficiency. Ph.D. in Computer Science from Technical University of Darmstadt (2020) M.Sc. and B.Sc. in Software Engineering from Polytechnic University of Milan His work emphasizes theoretical aspects of reinforcement learning, particularly policy gradient estimation and temporal-difference methods. Recent publications highlight innovations in data-driven teleoperation, movement primitives, and off-policy optimization, aligning with his goal of bridging simulation-to-reality gaps. He teaches courses including Introduction to Robotics, Optimization and Numerical Computation, Machine Learning, and Data Analysis II. His research is disseminated via platforms like Google Scholar and samueletosatto.com.

• Reinforcement Learning
• Robotics
• Machine Learning
• +6 more

Albert N. Shiryaev is a distinguished Professor at Moscow State University's Faculty of Mechanics and Mathematics, where he has served as Head of the Probability Theory Department since 1996. He has been a Professor at the university since 1970 and a member of the Steklov Mathematical Institute of the Russian Academy of Sciences since 1957. His research spans Probability theory , Mathematical statistics , Stochastic processes , and Financial mathematics , with particular expertise in sequential analysis and optimal stopping rules. His theoretical work has significantly influenced both pure and applied mathematics, especially in financial modeling where his monographs have become standard references. Shiryaev's publication record includes over 170 scientific papers and numerous influential monographs such as Statistics of Random Processes (with Liptser), Limit Theorems for Stochastic Processes (with Jacod), and Essentials of Stochastic Finance . His research demonstrates consistent focus on foundational probability theory while expanding into financial applications in later decades. Markov Prize of Academy of Sciences of the USSR (1974) Honorary Fellow of the Royal Statistical Society (1985) Member of the Academia Europea (1990) Kolmogorov Prize of the Russian Academy of Sciences (1994) Humboldt Research Award (1996) Doctor Rerum Naturalium Honoris Causa of Albert Ludwigs University (2000) Professor Honoris Causa of Amsterdam University (2002) Distinguished Professor of Moscow State University (2003) Shiryaev has advised 52 PhD dissertations across multiple countries and held significant academic leadership positions. He served as President of the Bernoulli Society (1989-1991), President of the Actuarial Society of Russia (1994-1998), and President of the Bachelier Finance Society (1998-1999). He currently serves as Deputy Head of the journal Theory of Probability and its Applications and Co-editor of Finance and Stochastics , while also sitting on multiple editorial boards including Markov Processes and Related Fields and Quantitative Finance . His research group at the Steklov Mathematical Institute and Moscow State University has maintained continuous activity in probability theory research for decades, contributing to both theoretical advances and practical applications in finance and statistics.

• Probability theory
• Mathematical statistics
• Stochastic processes
• +3 more

Gertraud Malsiner-Walli is an Associate Professor at the Institute of Statistics and Mathematics at WU Vienna University of Business and Economics. Her research focuses on Bayesian mixture models , model-based clustering , and computational statistics , with applications in medical imaging, neurology, and psychiatry. She has contributed to advancements in sparse finite Gaussian mixtures and telescoping sampling algorithms , enabling efficient Bayesian inference in mixture models. Her work bridges theoretical developments and practical applications, addressing challenges in identifying data clusters and handling high-dimensional datasets. Notable contributions include methodologies for effect fusion in regression models and multivariate regression analysis of mixed-type panel data. She has published in top journals such as Statistics and Computing , Advances in Data Analysis and Classification , and Bayesian Analysis . Her research also extends to medical applications, including studies on Alzheimer’s disease biomarkers and glioma diagnosis using combined imaging techniques. She has received funding from the Austrian Science Fund (FWF) and WU Projects grants. Collaborations with experts like Sylvia Frühwirth-Schnatter and Bettina Grün highlight her active role in advancing Bayesian statistical methodologies globally.

• Bayesian Statistics
• Mixture Models
• Computational Statistics
• +3 more

Jeremy Oakley is Professor of Statistics and Head of the School of Mathematical and Physical Sciences at the University of Sheffield. His work spans Bayesian statistics, uncertainty quantification for complex computer models, expert elicitation of probability distributions, and health-economic applications. Research Interests Bayesian statistics and inference Uncertainty quantification (UQ) for computer models Expert elicitation of probability distributions Health-economic modelling He co-developed the Sheffield Elicitation Framework (SHELF) , a widely-used set of protocols and software tools for structured expert judgement. Teaching & Supervision Professor Oakley teaches undergraduate and postgraduate statistics modules and supervises PhD students within SoMaS and in collaboration with other departments, focusing on UQ and expert-elicitation topics. Contact Email: j.oakley@sheffield.ac.uk

• Bayesian statistics
• Uncertainty quantification
• Expert elicitation
• +2 more

Dr. Lucas Kook is a Researcher at the Department of Statistics and Mathematics, WU Vienna University of Economics and Business. His research focuses on causal inference, machine learning, and statistical modeling, with applications in sports analytics, medical data analysis, and healthcare outcomes. He actively develops R packages such as deeptrafo for neural network-based statistical inference. His work bridges theoretical advancements in distributional regression and practical implementations in healthcare and sports domains. Key research areas include algorithm development for significance testing in supervised learning, causal feature selection, and nonparametric methods for conditional independence. Recent studies explore the efficacy of thrombolytic therapies in retinal artery occlusion and the predictive power of deep learning models in stroke patient outcomes. Collaborations span interdisciplinary fields, emphasizing reproducibility and methodological rigor in simulation studies.

• Causal Inference
• Machine Learning
• Statistical Modeling
• +4 more