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Lucrezia Reichlin is a Professor of Economics at the London Business School , where her research bridges macroeconomic policy and advanced econometric techniques. She holds key advisory roles as a non-executive director for major financial institutions like UniCredit Banking Group and Eurobank Ergasias SA, and chairs the Scientific Council at the Brussels-based think-tank Bruegel . Previously, she served as Director General of Research at the European Central Bank (2005-2008) and held academic positions at the Université Libre de Bruxelles and Columbia University. Education: Ph.D. in Economics, New York University Reichlin is a pioneer in now-casting and high-dimensional time series analysis , developing econometric models to process real-time economic data. Her work has been adopted by central banks and private investors globally, focusing on monetary policy evaluation , business cycle dynamics , and forecasting methodologies . She co-founded the firm Now-Casting Economics Ltd in 2011 to commercialize these techniques. Scientific Awards & Honors: 2016 Birgit Grodal Award of the European Economic Association 2016 Isaac Kerstenetzky Scholarly Achievement Award International Economics Award, Chamber of Commerce of Genoa (2015) Grand Ufficiale dell'Ordine della Stella d'Italia (2015) Fellow, British Academy Fellow, European Economic Association Distinguished Fellow, CEPR As a columnist for Corriere della Sera and member of the Commission Economique de la Nation (France), she influences public and policy debates. She also founded the Ortygia Foundation , promoting female education in southern Italy. Her methodological innovations in dynamic factor models , Bayesian VARs , and shrinkage techniques have reshaped modern macroeconomic analysis.

• Monetary Policy
• Applied Macroeconomics
• Econometrics (Time Series)
• +3 more

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
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Massimo Vergassola is a Professor at École Normale Supérieure-PSL (ENS-PSL) in Paris and Director of the ENS-PSL Center for Quantitative Biology. His research bridges physics and life sciences, focusing on the physics of living systems with emphasis on embryonic development , animal behavior , and biological navigation . He holds a joint appointment as CNRS Directeur de Recherche and leads initiatives at the PariSanté Campus Val-de-Grâce, integrating institutions like Inserm and Inria. Education: Laurea in Physics (University La Sapienza, Rome, 1985-1990); PhD in Physics (University Nice-Sophia Antipolis, 1990-1993); Post-doc at Princeton University (1994-1995) His research spans fluid dynamics , statistical physics , and biophysics , addressing problems like olfactory navigation , chemotaxis , cell size control , and mitotic wave dynamics . Recent work explores Bayesian search strategies in turbulent flows and topological interactions in developmental biology. His 15 most recent articles reveal a focus on olfactory search algorithms , embryonic patterning , gene regulation , and turbulent transport . Keywords include Biophysics , Computational Biology , and Nonlinear Dynamics . Scientific Awards : APS Fellow, APS Outstanding Referee, CNRS Bronze Medal, Fondation de France Thérèse Lebrasseur Prize, EADS Grand Prix, and Accademia dei Lincei student award Vergassola contributes to academic governance via editorial boards ( Physics Reports , JSTAT ) and grant committees (Human Frontier Science Project). His lab at ENS-PSL collaborates with institutions like Harvard Center for Quantitative Biology and Pasteur Institute.

• Physics of living systems
• Fluid dynamics
• Continuum mechanics
• +3 more

Univ.Prof. David Preinerstorfer is a Professor at the Department of Statistics and Mathematics at WU Vienna University of Economics and Business. His research focuses on econometric theory, statistical methodology, and high-dimensional testing. He has published extensively in top journals such as Econometrica , Journal of Econometrics , and Annals of Statistics . Key research areas include heteroskedasticity and autocorrelation robust testing, high-dimensional statistical inference, bootstrap methods, and optimal policy learning. His work addresses challenges in econometric testing under complex data conditions and develops methods to enhance statistical power and validity. Recent publications analyze topics like modern Gauss-Markov theorems, superconsistency in high dimensions, and treatment allocation with distributional targets. Preinerstorfer collaborates frequently with researchers such as A. B. Kock and B. M. Pötscher on projects addressing robust testing procedures and statistical methodology advancements.

• Econometrics
• Statistical Theory
• High-Dimensional Testing
• +4 more

Sepp Hochreiter is University Professor ( Univ.-Prof. ) at the Institute for Machine Learning , Johannes Kepler University Linz, and heads the LIT Artificial Intelligence Lab . He is Principal Investigator of several major Austrian and EU projects, including the FWF Cluster of Excellence “Bilateral Artificial Intelligence” and industry collaborations on certified deep learning, generative CFD simulation, and quantum-AI integration. Research interests revolve around the foundations and applications of deep learning: Recurrent neural networks: Inventor of LSTM; recent work on the xLSTM family that scales to billion-parameter language models while outperforming Transformers in efficiency. Uncertainty & reliability: New uncertainty estimation techniques for language generation, conformal prediction, and out-of-distribution detection using modern Hopfield networks. Generative models: Discrete diffusion samplers, generative drug-design pipelines (Bio-xLSTM), masked-image-model refiners, and physics-informed neural samplers. Applied AI: Few-shot learning in robotics, traffic prediction, medical risk modeling, antibody design, and climate downscaling. His 2022-2025 publications reveal a clear trend toward scaling recurrent architectures (xLSTM, FlashRNN, pLSTM) with hardware-aware optimisation, bridging theory and practice through energy-based models, and deploying trustworthy AI in safety-critical domains such as healthcare and autonomous systems. Scientific recognition & service: Keynote & invited speaker at NeurIPS, ICLR, ICML, and leading industry venues (60+ invited talks since 2022). Principal Investigator on 61 funded projects totalling >€50 M, spanning FWF, FFG, EU Horizon, and direct industry contracts. Editorial board and senior programme committee roles for top-tier ML conferences and journals. Supervision & team leadership: Leads a vibrant lab of 30+ PhD students and post-docs across machine learning theory, generative modelling, and AI for science. Active in joint doctoral training programmes with IST Austria and international partner labs.

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +7 more

Prof. Dr. Jochen Mayerl is a Professor of Sociology specializing in Empirical Social Research at the Institute for Sociology , Faculty of Behavioural and Social Sciences, Chemnitz University of Technology. He coordinates the methodological competence center at the university and serves as Associate Editor for the peer-reviewed journal Methods, Data, Analyses (mda) . Expertise: Survey Methodology, Structural Equation Modeling, Attitude-Behavior Research Research Focus: Environmental Sociology, Xenophobia, Societal Change Networks: European Sociological Association (ESA), International Sociological Association (ISA), European Survey Research Association (ESRA) His recent publications analyze ethnocentrism, environmental attitudes, and methodological advancements in survey research. He has received accolades like the Cora Maas Award for teaching excellence in methods courses. Selected Scientific Awards: Cora Maas Award (2013)

• Survey Methodology
• Structural Equation Modeling
• Attitude-Behavior Research
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Hannes Leeb is a full-time Professor at the University of Vienna , leading the Department of Statistics and Operations Research within the Faculty of Business, Economics, and Statistics . He is also affiliated with the Research Network Data Science and co-organizes the ISOR Colloquium . Research Interests : Inference Post Model Selection, Shrinkage-Type Estimators, High-Dimensional Data Analysis, Machine Learning, Random Matrices, Asymptotic Statistics, Linear/Non-Linear Time Series. Recent Research Trends : His 15 most recent articles focus on valid statistical inference in high-dimensional settings, including F-statistics , conformal prediction , and adaptive methods for deep learning. Key subfields include post-model selection uncertainty , conditional coverage , and shrinkage estimator properties . Teaching & Leadership : He teaches core courses like 'Basic Principles of Statistics' and 'Probability Theory', and supervises PhD research privatissimum in econometrics/statistics. As department head, he emphasizes data science integration and methodological rigor .

• Inference Post Model Selection
• Model Selection in Statistics and Econometrics
• Asymptotic Statistics
• +4 more