Enrico Arrigoni is a Professor at the Institute of Theoretical Physics - Computational Physics at Graz University of Technology (TU Graz). His research focuses on correlated quantum systems, many-body physics, and nonequilibrium dynamics, with applications to Mott insulators, quantum transport, and photovoltaic systems. He teaches courses such as 'Green's functions in Many-Particle Physics' and 'Atom Physics - Quantum Mechanics'. Recent work explores phonon effects in Mott systems, neural network approaches to quantum states, and impact ionization processes in photodriven materials. His methods include auxiliary master equation techniques and variational cluster approaches. Publications span topics like nonequilibrium steady states, quantum impurity models, and disordered systems. While no specific awards are listed, his contributions to theoretical physics and computational methods are evident through his prolific research output. Advising and grants details are not explicitly mentioned, though his involvement in graduate theses and research projects is implied via available master's and bachelor's thesis topics.
Karl Friston is a renowned neuroscientist and Professor at the Institute of Neurology, University College London . As Scientific Director of the Wellcome Trust Centre for Neuroimaging, he has pioneered transformative methodologies in brain imaging, including statistical parametric mapping (SPM) , voxel-based morphometry (VBM) , and dynamic causal modelling (DCM) . His theoretical work on the free-energy principle and active inference has reshaped understanding of brain function. Key Positions : Scientific Director (Wellcome Trust Centre), Fellowships at MRC units, Keck Foundation Fellow Research Focus : Functional integration in the human brain, computational models of neuronal interactions, schizophrenia, and Bayesian brain theory. Scientific Awards : Wiley Young Investigator Award (1996) Golden Brain Award (2003) Fellow of the Royal Society (2006) Weldon Memorial Medal (2013) EMBO Membership (2014)
Edgar Erdfelder is a Full Professor of Psychology at the University of Mannheim, Germany, holding the Chair of Cognitive Psychology and Individual Differences since 2008. He is affiliated with the School of Social Sciences and has made significant contributions to cognitive psychology, statistical modeling, and decision-making research. Previously, he served as Full Professor at the University of Mannheim (2002–2008), Associate Professor at the University of Giessen (2001–2002), and Senior Lecturer at the University of Bonn (1987–2001). Ph.D. in Psychology, University of Trier (1986) Habilitation in Psychology, University of Bonn (2000) Diploma (M.Sc.) in Psychology, University of Göttingen (1980) Erdfelder's research focuses on statistical power analysis, multinomial processing tree (MPT) modeling, sequential statistical inference, and cognitive modeling. His work explores judgment and decision-making through mathematical and computational frameworks, integrating signal-detection theory with threshold models. He developed the widely used GPOWER software for statistical power analysis and advanced MPT models to measure cognitive process speeds. His recent publications with students highlight applications of Bayesian sequential methods, meta-analyses of sleep effects on memory, and theoretical extensions of the recognition heuristic. These studies span subfields like cognitive architecture, decision theory, and experimental design. Martin Irle Award (2020) Fellow of the Association for Psychological Science (2016) Heinz Heckhausen Award (1988) Erdfelder has held leadership roles, including Vice President of Research at the University of Mannheim and Academic Director of the Center of Doctoral Studies in Social and Behavioral Sciences funded by the DFG. He mentored numerous Ph.D. students and led the DFG-funded Research Training Group SMiP, focusing on statistical modeling in psychology.
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research , University of Vienna , and an elected member of the Austrian Young Academy (Junge Akademie) since 2020. Her research bridges rigorous mathematics and cutting-edge applications in finance, machine learning, and stochastic analysis. Education: Christa earned her M.Sc. in 2006 from TU Wien with a thesis on affine interest-rate models, her Ph.D. in 2011 from ETH Zürich on affine and polynomial processes, and completed her Habilitation at the University of Vienna in 2018 on high-dimensional finance beyond classical paradigms. Research Interests: Her work centers on affine and polynomial processes , stochastic portfolio theory , signature methods , and infinite-dimensional stochastic analysis . Recent projects explore signature-based neural SDEs for option calibration, measure-valued diffusions for energy markets, and universal approximation properties of signature transforms. Awards & Recognition: Among her accolades are the FWF START Award 2019 , the Bruti-Liberati Visiting Fellowship 2018 , the ETH Medal 2012 for an outstanding Ph.D. dissertation, and the Prix de l’Institut Europlace de Finance 2017 for the best paper in finance. Contact: christa.cuchiero@univie.ac.at , Kolingasse 14-16, 05.47, 1090 Wien, Austria.
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.
Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.
Joost-Pieter Katoen is a full Professor at RWTH Aachen University and Head of its Computer Science Department since 2012. He also holds a part-time (20%) Professorship at the University of Twente . His research focuses on model checking , probabilistic verification , formal semantics , and software verification , with applications in aerospace systems. His work has led to significant tools like MRMC (probabilistic model checker), COMPASS (AADL analysis tool-set), and libalf (learning automata library). He has authored over 18 international projects (total €5.2 million) and graduated 12 PhD students. Scientific Awards : Member, German National Academy of Sciences (Leopoldina), 2024 ACM Fellow, 2020 ERC Advanced Grant, 2018 Honorary doctorate, Aalborg University, 2017 Teaching Award, RWTH Aachen, 2010 Philips Early Career Development Award, 1988 Research Trends (from articles): His recent work spans probabilistic program verification , quantitative game theory , Markov chain analysis , and parameter synthesis for stochastic systems, with applications in AI, quantum computing, and fault tree analysis. Leadership & Service : Katoen co-founded the QEST conference , chairs ETAPS steering committee, and has led numerous program committees (CONCUR, TACAS, QEST). He has served on editorial boards and organized conferences/seminars globally.
Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.
Bettina Grün is an Associate Professor and Deputy Head of the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on Bayesian mixture models, cluster analysis, and applying statistical methods to sustainability, tourism, and environmental studies. She has led multiple research projects, including studies on environmental behavior in tourism and advanced text modeling in economics. Grün holds a PhD in Technical Mathematics from TU Wien (2006) and a Habilitation in Statistics from Johannes Kepler University Linz (2012). She has authored over 150 publications in top journals like Journal of Environmental Management and Expert Systems with Applications . Her work emphasizes practical applications, such as reducing hotel waste through behavioral interventions and developing R packages like movMF and circlus for statistical clustering. Grün has received awards including the AIEST Best Contribution Award (2019) and the MRS Silver Medal (2016). Grün teaches courses on statistical modeling and leads projects like Analysis of Central Bank Communication (2022–2026) and Environmentally Friendly Behavior in Tourism (2019–2024).
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.
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna. Her research spans Mathematical Finance, Stochastic Processes, and Machine Learning applications in finance. She has over 48 publications, including recent work on signature-based models for SPX/VIX options, polynomial McKean-Vlasov SDEs, and infinite-dimensional Wishart processes. Her projects include 'Dynamic Uncertainty Modeling in Finance' and a long-term study on 'Universelle Strukturen in Finanzmathematik' (2020–2028). Research Interests: Signature methods for financial modeling Affine and polynomial processes Machine learning in finance Measure-valued stochastic differential equations Volterra equations and rough path theory Portfolio optimization and risk management Scientific Awards: Bruti-Liberati Visiting Fellowship (2018) Fellow at the Center for Advanced Study (CAS), Norwegian Academy of Science and Letters (2024) ETH Medal for Ph.D. thesis (2012) Recent Publications (2023–2025) focus on signature methods, stochastic portfolio theory, energy markets, and robust calibration techniques. Her work integrates advanced mathematical theory with practical financial applications, emphasizing nonlinear SPDEs, polynomial models, and machine learning frameworks.
Gerhard Holzapfel is a Professor at TU Graz's Department of Biomechanics. His research focuses on biomechanics of soft biological tissues, cardiovascular systems, and advanced material modeling. He leads the Institute of Biomechanics, conducting experimental and computational studies on tissue mechanics, vascular diseases, and medical device design. Education : Details not explicitly provided in source text. Research Interests : Dr. Holzapfel's work spans constitutive modeling of soft tissues, computational fluid dynamics (CFD) in vascular systems, mechanobiology of cells and tissues, and biomaterials. He emphasizes translating biomechanical insights into clinical applications such as endovascular devices and surgical simulations. His studies often integrate microstructural analysis with macroscopic mechanical behavior to understand pathologies like atherosclerosis and aortic dissection. Publications : Recent work highlights advancements in fiber dispersion models for skin mechanics, Bayesian frameworks for material calibration, and CFD-driven evaluations of TEVAR (thoracic endovascular aortic repair). His articles emphasize predictive modeling of tissue behavior under various loading conditions, with applications in cardiology and regenerative medicine. Awards : No specific honors or fellowships mentioned in the provided text. Advising & Grants : No student/advisor relationships or grant details explicitly listed. The Institute of Biomechanics likely supports collaborative projects in biomechanical engineering and medical research. Labs/Teams : Director of TU Graz's Institute of Biomechanics, leading interdisciplinary teams in biomechanics, material science, and computational modeling. Collaborates with clinical partners on vascular biomechanics and surgical device development.
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.
Ronald Ortner is a Professor and Chair of Information Technology, leading research in reinforcement learning, Markov decision processes, and computational learning theory. His work emphasizes theoretical foundations and practical applications in autonomous systems and optimization. He has published extensively since 2004, with notable contributions to bandit algorithms, regret analysis, and exploration strategies in dynamic environments. Research Focus: Reinforcement Learning, Markov Processes, Optimization Key Contributions: Regret bounds in MDPs, adaptive algorithms, transfer learning quantification Ortner engages in academic activities such as conference presentations and peer reviews, focusing on advancing algorithmic approaches in AI and machine learning. His research spans interdisciplinary areas including robotics, energy systems, and probabilistic modeling.
Maarten Janssen serves as Professor of Microeconomics at the University of Vienna's Department of Economics within the Faculty of Business, Economics and Statistics, where he also holds the position of Vice-Director of Studies for the Directorate of Doctoral Studies. His teaching portfolio includes advanced courses such as the OMDS/VGSE Research Seminar in Microeconomics (MA), Industrial Organization (BA), and Competition and Regulation: Theory (MA), reflecting his deep engagement with both theoretical and applied microeconomic education. His research centers on consumer search behavior, auction theory, and markets characterized by asymmetric information, with particular emphasis on digital platforms and e-commerce dynamics. Janssen investigates how consumers navigate search costs, how platforms leverage big data for ranking algorithms, and how firms strategically design pricing and return policies in competitive environments. His work bridges rigorous theoretical modeling with practical market applications, often focusing on welfare implications for consumers and market efficiency. Analysis of his 2023-2025 publications reveals dominant themes in platform economics, including dual learning mechanisms where platforms optimize rankings based on consumer choices, strategic implications of unobserved wholesale contracts, and welfare effects of influencer marketing in search markets. His research consistently addresses real-world market design challenges in digital commerce and information-rich environments. Professor Janssen's scholarly recognition includes the following distinctions: fellow of the CEPR (London) member of the Royal Holland Society of Sciences and Humanities research associate at ZEW (Mannheim) academic affiliate at CEG Europe He actively supervises PhD students in the Department of Economics and maintains collaborative research relationships through his institutional affiliations. His work frequently examines the intersection of microeconomic theory with industrial organization challenges, particularly in evolving digital markets where information asymmetry and consumer search behavior create unique strategic considerations for firms and platforms. Through his affiliations with CEPR, ZEW, and CEG Europe, Janssen participates in international research networks that facilitate cross-institutional collaboration on industrial organization topics, contributing to both academic discourse and policy-relevant economic analysis in European markets.
