جست‌وجوی استاد و پژوهشگر

Francesco Locatelloمشاهده پروفایل

استادیار

• Causal Learning
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Francesco Locatello is a tenure-track Assistant Professor at the Institute of Science and Technology Austria (ISTA), leading the Causal Learning and Artificial Intelligence lab. He is also an AI Resident at the Chan Zuckerberg Initiative. He holds a PhD from ETH Zürich, co-advised by Gunnar Rätsch and Bernhard Schölkopf. His research focuses on causal representation learning, score matching, and object-centric learning, with applications in machine learning and AI. His work has been recognized with prestigious awards, including the ICML 2019 Best Paper Award and the Hector Foundation Award (2023). Education: PhD in Machine Learning, ETH Zürich (advisors: Gunnar Rätsch, Bernhard Schölkopf) Research Interests: Causal Learning, Causal Representation Discovery, Score Matching Algorithms, Object-Centric Learning, Robust Generalization in AI, and Applications in Vision and Reinforcement Learning. Recent Work Trends: His publications emphasize causal mechanisms in neural representations, scalable causal discovery methods, and improving model generalization through latent space analysis. Recent studies explore geometric representations, mechanistic neural networks, and OOD detection using relative angles. Awards: ICML 2019 Best Paper Award Hector Foundation Award for Outstanding Achievements in Machine Learning (2023) Google Research Scholar Award (2024) Advising & Teams: Supervises a dynamic lab with students and postdocs across ISTA, ELLIS, and partner institutions. Notable advisees include Dingling Yao (ISTA), Riccardo Cadei (co-advised with Cordelia Schmid), and Marco Fumero (now a postdoc at ISTA). Collaborates with leading researchers like Arthur Gretton, Max Welling, and Volkan Cevher. Labs & Initiatives: Leads the Causal Learning and AI lab at ISTA, contributing to ELLIS programs and fostering interdisciplinary collaborations in causal AI.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۶ مورد دیگر

Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.

Karl Fristonمشاهده پروفایل

استاد

• Imaging neuroscience
• Computational neuroscience
• Theoretical neurobiology
• +۳ مورد دیگر

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)

• Mixed Methods Research
• Education Science
• Research Methodology
• +۲ مورد دیگر

Judith Schoonenboom is a Professor at the University of Vienna and Deputy Head of the Department of Education. She teaches courses in quantitative and interpretive methodologies, research design, and PhD/master's thesis supervision, including seminars like 'Methodology and Research Design' and 'Quantitative Methodologies in Education Science'. Her academic responsibilities reflect a focus on advanced research methodologies in educational contexts. Schoonenboom's research centers on mixed methods and multimethod approaches in education science, emphasizing methodological innovation, data integration, and theoretical development. Key interests include the interplay between qualitative and quantitative research, design patterns in mixed methods, and strategies for enhancing inferential rigor in social science studies. Her work bridges epistemological frameworks with practical research applications. Her scholarly publications demonstrate a consistent focus on advancing mixed methods research, particularly through innovations in design, integration techniques, and theoretical reflection. Recent works explore causal inference in qualitative research, visualization of methodological interactions, and performative approaches, highlighting trends toward interdisciplinary synthesis and practical methodology refinement.

Yuriy Gorodnichenkoمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Economics
• Public Finance
• +۷ مورد دیگر

Yuriy Gorodnichenko serves as the Quantedge Presidential Professor in the Department of Economics at the University of California, Berkeley since 2018. His extensive academic affiliations include being a Faculty Research Associate at the National Bureau of Economic Research (2014-present), Research Fellow at the Institute for the Study of Labor (2007-present), International Fellow at the Kiel Institute for the World Economy (2011-present), and Research Consultant for both the European Central Bank (2018-present) and European Investment Bank (2017-present). He also serves as Editor of the Journal of Monetary Economics (2018-present) and Member of the Executive Committee of the Association for Comparative Economic Studies (2019-present). Education: Ph.D., Economics, University of Michigan, 2007 M.A., Statistics, University of Michigan, 2004 M.A., Economics (high honors; valedictorian), Economics Education and Research Consortium at National University of Kyiv-Mohyla Academy, Kiev, Ukraine, 2001 B.A., Economics (honors; valedictorian), National University of Kyiv-Mohyla Academy, Kiev, Ukraine, 1999 Gorodnichenko's research spans multiple subfields of economics with particular emphasis on monetary economics, public finance, international economics, and macroeconomics . His scholarly approach typically combines both macroeconomic and microeconomic data with rigorous theoretical and statistical analyses. His work is organized into five major categories: monetary economics, aggregate implications of informational frictions, business cycles, development/productivity/income differences, and inequality. As an applied macroeconomist, he frequently bridges methodological approaches across different economic subdisciplines. His publication record demonstrates consistent high-impact research across top economics journals including American Economic Review, Journal of Political Economy, and Review of Economic Studies. The trajectory of his work shows increasing focus on the microfoundations of macroeconomic phenomena, particularly how information frictions affect economic behavior and policy transmission mechanisms. His recent publications have increasingly addressed the distributional consequences of monetary policy and the role of cultural factors in economic development. Scientific Awards and Honors: Fellow, Econometric Society (2021) Highly Cited Researcher, Clarivate (2021) Distinguished Teaching Award, Social Science Division, UC Berkeley (2020) World Junior Prize in Monetary Economics and Finance (2018) NSF CAREER award (2012) Sloan Research Fellowship (2013) Multiple #1 rankings among young economists by RePEc (2014-2021) Best paper award, American Economic Journal: Economic Policy (2015) Gorodnichenko has received consistent recognition for his teaching and advising from UC Berkeley's Economics Department, including multiple runner-up positions and a win for the Best Advisor Award (2014). His research has been supported by prestigious grants including the NSF CAREER award and Sloan Research Fellowship. His impact metrics are substantial with over 19,000 citations and an h-index of 54, reflecting significant influence in the economics profession. While the scraped text doesn't specify dedicated research laboratories, Gorodnichenko maintains active research collaborations through his affiliations with major economic research institutions including NBER, IZA, and Kiel Institute. His editorial roles at leading journals position him at the center of contemporary macroeconomic research discourse.

Leitgeb Hannesمشاهده پروفایل

استاد

• Logic
• Philosophy of language
• Philosophy of science
• +۴ مورد دیگر

Hannes Leitgeb is the Chair of Logic and Philosophy of Language and an Alexander von Humboldt Professor at Ludwig-Maximilians University Munich, where he also founded and directs the Munich Center for Mathematical Philosophy . His research spans logic, epistemology, philosophy of science, and philosophy of mathematics, with a focus on formal models of belief, Bayesianism, and Carnapian foundations. Education: Dual PhDs in Philosophy and Mathematics from the University of Salzburg. Research Trends: Integrates mathematical rigor into philosophical analysis, particularly in belief stability, counterfactuals, and identity criteria. His work bridges classical epistemology with computational approaches. Editorial Leadership: Served as Editor-in-Chief of Erkenntnis , Coordinating Editor of Review of Symbolic Logic , and Subject Editor for the Stanford Encyclopedia of Philosophy . Scientific Awards: Elected member of L'Académie Internationale de Philosophie des Sciences (2011) Alexander von Humboldt Professorship (2010) Friedrich Wilhelm Bessel Research Award (2007) Philip Leverhulme Prize (2007) Leitgeb has delivered over 168 invited talks since 2005 and published 74 articles, including in top journals like Mind , The Philosophical Review , and Nous . His declined offers from prestigious institutions (Stanford, UC Irvine) underscore his academic prominence.

Robert Peharzمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Tractable Probabilistic Models
• Artificial Intelligence
• +۶ مورد دیگر

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.

• Visual Analytics
• Information Visualization
• Human-Computer Interaction
• +۵ مورد دیگر

Alexander Rind is a full-time Researcher at the University of Applied Sciences St. Poelten , affiliated with the School of Media and Digital Technologies and the Media Computing Research Group . He holds an MSc in Business Informatics from Vienna University of Technology, University of Vienna, and Lund University. Education : 1998-2004 MSc in Business Informatics His research interests span Visual Analytics , Information Visualization , and Human-Computer Interaction , with specialization in time-oriented data analysis , electronic health records visualization , and multimodal analytics . Recent projects focus on integrating sonification with visualization for enhanced data exploration. Key publication trends reveal expertise in temporal data modeling , multimodal analytics , and domain-knowledge integration for healthcare and social work applications. His work includes TimeBench (open-source time-oriented data library) and tools like easyBiograph / easyNWK for social diagnostics. He actively contributes to scientific communities as program committee member for IEEE VIS, EuroVis, and journals like TVCG, while developing visual analytics systems for clinical gait analysis, media transparency, and industrial manufacturing.

Samuli Ripattiمشاهده پروفایل

استاد

• Genetic epidemiology
• Statistical genetics
• Human genetics
• +۵ مورد دیگر

Samuli Ripatti is a Professor of Biometry in the Department of Public Health at the University of Helsinki's Faculty of Medicine. He serves as Director of the Institute for Molecular Medicine Finland (FIMM) since 2023 and leads the Academy of Finland's Center of Excellence in Complex Disease Genetics. His research integrates genetics, statistics, and clinical medicine to advance understanding of cardiometabolic diseases and genomic risk prediction. Education: Statistics major, University of Helsinki PhD in Mathematical Statistics, Stockholm University (2002) Docent in Statistics, University of Helsinki (2007) Research interests focus on cardiometabolic diseases, polygenic risk scores (PRS), and genetic epidemiology. He has pioneered PRS for disease prevention and developed clinical risk algorithms, with applications in precision medicine and global health initiatives. Recent publications highlight trends in polygenic risk modeling across diverse diseases, lipidomics, sleep disorders, and trauma outcomes. His work spans genomics, bioinformatics, and translational medicine. Scientific awards: Knight, First Class, of the Order of the White Rose of Finland (2021) Highly Cited Researcher (2018, 2017, 2016) Ripatti chairs doctoral programs in life sciences and the EU-funded INTERVENE project, which develops genomic risk prediction methods. He has led research groups at FIMM-EMBL, the Broad Institute, and the Wellcome Trust Sanger Institute. As Director of FIMM and member of scientific advisory boards (e.g., NMRC Singapore, EXCEED), he shapes global genomic research collaborations and biobank infrastructure.

Marcel Weberمشاهده پروفایل

استاد

• Philosophy of Biology
• Philosophy of Evolutionary Linguistics
• General Philosophy of Science
• +۳ مورد دیگر

Marcel Weber is a Full Professor of Philosophy of Science at the Department of Philosophy, University of Geneva, with a focus on the philosophy of life sciences and general philosophy of science. He also teaches general epistemology and modern philosophy, particularly Kant. PhD in Philosophy (1996, University of Konstanz) Habilitation (2002, Leibniz University Hannover) Previous roles: SNSF Professor at University of Basel (2004–2009), Full Professor at University of Konstanz (2009–2011), and visiting appointments at institutions like University of California, Irvine, and Max Planck-Institute for the History of Science. His research spans the intersection of biology and philosophy, emphasizing evolvability, causality in biological systems, and the ontology of biological functions. Key projects include collaborations with the Institute of Genetics and Genomics of Geneva (iGE3), NCCR Evolving Language, and the DACH project on Inferentialism and Bayesianism. Marcel Weber's recent publications explore evolutionary developmental biology, causal specificity, and the metaphysics of biological modalities. His work often integrates experimental practices across levels of biological organization and critiques deterministic versus probabilistic interpretations in evolutionary theory. 2012: Elected to the German National Academy of Sciences Leopoldina 1996: Science Prize of the District of Constance He has edited volumes on probabilities in science, philosophical confirmation, and collective epistemology. His laboratory affiliations include interdisciplinary groups like 'What if? On Counterfactual Claims' and lgBig (Lake Geneva Biological Interest Group).

• Probability Theory
• Financial Mathematics
• Stochastic Processes

Paul Eisenberg is an Assistant Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. He holds a doctorate in Mathematics and has held positions at the University of Liverpool, Vienna University of Technology, and Technical University of Dortmund. His research focuses on probability theory and financial mathematics, particularly stochastic processes, term structure modeling, and applications to financial markets. He teaches courses in Probability, Quantitative Methods, and Business Mathematics. Research outputs concentrate on stochastic processes in finance, including term structure modeling, stochastic optimization, market microstructure, and applications to cryptocurrency markets. Recent work shows emphasis on regime-switching models and high-dimensional stochastic systems.

Vincent Jaddoeمشاهده پروفایل

استاد

• Pediatrics
• Epidemiology
• Developmental Biology
• +۵ مورد دیگر

Vincent Jaddoe is a Professor of Pediatric Epidemiology at Erasmus University Rotterdam and an Adjunct Professor at Harvard T.H. Chan School of Public Health . He is Chair of the Division of General Pediatrics at Erasmus MC, Sophia Children’s Hospital, and leads the Generation R Study , a major population-based cohort involving 10,000 pregnant women and their children. His work bridges epidemiology, pediatrics, and epigenomics, focusing on developmental origins of health and disease. Current Positions Chair, Division of General Pediatrics, Erasmus MC, Netherlands Principal Investigator, Generation R Research Core Facility Project Coordinator, EU LifeCycle Project Jaddoe’s research explores how genetic, epigenetic, and environmental factors during pregnancy influence lifelong health. His studies span fetal development , childhood obesity , and preconception health . He utilizes advanced epidemiological methods, imaging, and omics technologies, collaborating with over 20 departments and 150 PhD students. Key publications address fetal growth restriction , maternal smoking , and genetic heritability in child health. Articles highlight cross-disciplinary approaches integrating epidemiology , neuroscience , and genomics . Scientific Awards Lifetime member of Royal Dutch Academy of Sciences (KNAW) European Research Council (ERC) Consolidator Grant NWO VIDI and VENI grants Jaddoe has received over 900 publications with an H-index of 92 and significant citations. He coordinates the Academic Rotterdam Center for Pregnancy & Childhood , fostering collaboration between researchers, healthcare providers, and policymakers.

Aad van der Vaartمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +۵ مورد دیگر

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.

• Pediatrics
• Obesity Management
• Metabolic Disorders
• +۲ مورد دیگر

Daniel Weghuber is Professor and Department Chair at Paracelsus Medical University's Department of Pediatrics. His research focuses on pediatric obesity management, metabolic disorders, and pharmacotherapy. Research areas include: Anti-obesity medications for youth Neonatal laboratory diagnostics Clinical guideline development Metabolic function in inflammatory bowel disease Publications emphasize evidence-based obesity management, novel therapeutic approaches, and optimization of pediatric care protocols. Recent works address medication efficacy in adolescents and minimally invasive diagnostics. He leads projects on mitochondrial function in IBD and EURAS (epilepsy research). Current research examines climate impacts on migration through machine learning models.

Elsje van Bergenمشاهده پروفایل

دانشیار

• Educational genetics
• Neurodevelopmental conditions
• Mental health
• +۱۷ مورد دیگر

Dr. Elsje van Bergen is an Associate Professor at Vrije Universiteit Amsterdam's Department of Biological Psychology and a Visiting Professor at the University of Oslo (2024-25 academic year). She leads an interdisciplinary research team investigating why neurodevelopmental conditions and educational outcomes run in families, bridging genetics, psychology, psychiatry, education, and public health through the Netherlands Twin Register. Her academic background includes: PhD in Child Development and Education from University of Amsterdam (2013) Postdoctoral Fellowship at University of Oxford (2012-2015) Bachelor's and Master's in Human Movement Sciences from VU Amsterdam ( cum laude ) Van Bergen's research disentangles genetic and environmental influences on learning differences across language, reading, and mathematics, with focus on dyslexia, dyscalculia, ADHD, and autism. Her work examines how these conditions relate to mental health and educational outcomes through twin studies, polygenic scores, and large cohorts like Norway's MoBa study. Her recent publications reveal dominant genetic influences in educational inequality and neurodevelopmental condition comorbidity. Methodologically innovative works leverage polygenic scores to resolve cultural transmission pathways, while meta-analyses quantify parent-offspring resemblance in reading. These studies, published in Nature Human Behaviour and Psychological Science , demonstrate interdisciplinary approaches to gene-environment interplay. Major recognitions include: ERC Starting Grant (2023) NWO VIDI Talent Grant (2021) Jacobs Foundation Research Fellowship (2020) NWO Rubicon (2012) and VENI (2016) Talent Grants Early-career awards from Society for Scientific Study of Reading, Association for Psychological Science, and Federation of Associations in Behavioral & Brain Sciences Fellow of Young Academy of Europe (2019) She mentors Master's and PhD students while securing major grants to investigate intergenerational educational cycles. Her ERC-funded project examines how parental genotypes influence offspring education through dynastic social processes. She co-leads Amsterdam Young Academy initiatives supporting early-career researchers and interdisciplinary collaboration. Based in VU Amsterdam's Department of Biological Psychology, she collaborates with LEARN! and Amsterdam Public Health research institutes. Her Oslo-based work with CREATE and PROMENTA centres leverages Norwegian registry data to study education-mental health intergenerational transmission across 114,000 families.