جست‌وجوی استاد و پژوهشگر

Oliver Arránz Beckerمشاهده پروفایل

استاد

• Quantitative Methods
• Family Sociology
• Demographic Processes
• +۴ مورد دیگر

Oliver Arránz Becker serves as Professor of Quantitative Methods in the Social Sciences at the Institute of Sociology, Faculty of Philosophy I, Martin Luther University Halle-Wittenberg since 2015. His academic career includes professorships at the University of Cologne (2014-2015), University of Mannheim (2013-2014), and TU Chemnitz (2012-2013), where he completed his habilitation in Sociology. His research focuses on quantitative methodology (specializing in dyad and panel analysis), family sociology (lifestyle choices, fertility, partnership dynamics), and health inequality (medical sociology, health disparities). His methodological expertise drives innovative approaches to studying demographic processes and social homogenization. Recent publications reveal strong thematic consistency in applying advanced quantitative techniques to family formation, migration, and health outcomes. His work frequently utilizes German longitudinal datasets like GSOEP, demonstrating expertise in complex survey methodology and causal inference in social research. Prof. Arránz Becker actively contributes to academic governance as Scientific Board Member of 'Aufwachsen in Deutschland', Chair of Zentrum für Sozialforschung Halle, and External Fellow at MZES. He serves as co-editor of the 'Familienforschung' book series and regularly reviews for top sociology journals including Journal of Marriage and Family and Demographic Research . His teaching portfolio includes core methodology courses: 'Methods of Descriptive Data Analysis', 'Data Analysis with Stata', and 'Inferential Statistics and Regression Analysis' at both BA and MA levels, alongside empirical research colloquia.

• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +۷ مورد دیگر

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

• Structural Biology
• Computational Biology
• Cryo-Electron Microscopy
• +۳ مورد دیگر

Prof. Michael Habeck is a faculty member at the University of Göttingen, specializing in structural biology and computational methods. His research focuses on cryo-EM data analysis, molecular modeling, and integrating machine learning for biomedical applications. Advisor to 10+ PhD theses (2016–2023) Expertise in cryo-EM structure determination and validation Develops probabilistic models for dynamic proteins and change-point detection algorithms His work bridges structural biology with computational innovation, enabling high-resolution analysis of macromolecular complexes and advancing diagnostic tools via machine learning. Recent projects include CRISPR-edited cardiomyocyte models and super-resolution microscopy techniques. Key research areas span: Structural Biology: Cryo-EM, protein conformational changes Computational Methods: Bayesian modeling, tomography algorithms Machine Learning: Cardiac disease prediction, genomic data analysis

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +۶ مورد دیگر

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

Adeel Raziمشاهده پروفایل

استاد

• Computational Neuroscience
• NeuroAI
• Causality
• +۷ مورد دیگر

Professor Adeel Razi serves as Principal Investigator of the Computational Neuroscience Laboratory at Monash University's Turner Institute for Brain and Mental Health within the School of Psychological Sciences. He holds prestigious fellowships including ARC Future Fellow, NHMRC Investigator (Emerging Leadership), and CIFAR Azrieli Global Scholar in the Brain, Mind & Consciousness program. His research focuses on developing next-generation generative models of brain function through three integrated themes: 1) Creating multi-modal Bayesian methods (Dynamic Causal Modeling) to characterize brain network dynamics in pathologies; 2) Using biological neural network principles to inform artificial neural network design via active inference; 3) Employing classical psychedelics with computational modeling to understand consciousness mechanisms and therapeutic applications for psychiatric conditions. Razi's recent work includes the largest neuroimaging study of psilocybin to date, revealing how context structures psychedelic brain states, and novel Bayesian approaches for training binary and spiking neural networks. His research bridges engineering, physics, machine learning, and neurobiology to advance understanding of brain computation. ARC Future Fellow NHMRC Investigator (Emerging Leadership) CIFAR Azrieli Global Scholar, Brain, Mind & Consciousness program Professor Razi leads multiple clinical trials exploring psychedelic therapeutics and maintains active collaborations with Wellcome Leap on addiction research. His laboratory develops computational frameworks that integrate multimodal neuroimaging data to model brain network dynamics across various states of consciousness and pathological conditions. Current projects include psilocybin clinical trials for mental health conditions and developing neuroAI approaches based on active inference principles.

Oscar Corchoمشاهده پروفایل

استاد

• Ontology-based Data Integration
• Semantics in Open Science
• Semantic Web
• +۶ مورد دیگر

Oscar Corcho is a Full Professor at the Department of Artificial Intelligence within the School of Computer Science at the Technical University of Madrid. He is a prominent member of the Ontology Engineering Group and has been serving as a Professor since May 2007, with his current position as Full Professor reflecting his significant contributions to the field. Professor Corcho's research primarily focuses on Ontology-based Data Integration and the application of Semantics in Open Science. His expertise extends across the broader domains of Semantic Web, Linked Data, Knowledge Graphs, and Ontological Engineering. His work bridges theoretical research with practical applications, particularly in data integration systems and knowledge representation frameworks. With over 460 publications and more than 12,000 citations, he has established himself as a leading researcher in semantic technologies. His recent publications show a strong emphasis on virtual knowledge graph architectures, RDF-star generation, semantic labeling techniques for tabular data, and applications of semantic technologies in scientific literature analysis. His research demonstrates a clear trajectory toward solving real-world data integration challenges across heterogeneous sources while advancing the theoretical foundations of knowledge representation. Third Spanish Award on Computer Science (2001) Beyond his academic role, Professor Corcho is a co-founder of LocaliData Spain, demonstrating his commitment to translating research into practical applications. He has supervised numerous research projects and has been instrumental in developing tools like Morph-KGC for knowledge graph construction. His work has significant implications for public procurement systems, transportation data integration, citizen science initiatives, and scientific research infrastructure. As leader of the Ontology Engineering Group, he oversees a research program that develops innovative methodologies for ontology engineering and semantic data integration across diverse domains. His group's work has influenced both academic research and industry practices in knowledge representation and semantic technologies.

• Cognitive Neuroscience
• Computational Modeling
• Predictive Coding
• +۴ مورد دیگر

Ryszard Auksztulewicz serves as Visiting Professor and Group Leader of the Prediction and Memory unit within the Department of Education and Psychology at Freie Universität Berlin. Based at Habelschwerdter Allee 45, Room JK 25/220 in Berlin, he leads research at the intersection of cognitive and systems neuroscience. His research focuses on neural mechanisms of prediction, attention, and memory , employing multimodal approaches including EEG, fMRI, MEG, ECoG, TMS, electrophysiology, and calcium imaging. He integrates these with computational modeling (Bayesian models of neural/behavioral data) and multivariate decoding techniques. Key research themes include: Predictive coding frameworks for understanding sensory processing Neural gain modulation in temporal expectation Auditory cortex representations of memory and prediction Task-dependent modulation of sensory predictions Recurrent processing in perceptual awareness Analysis of his 34 publications (h-index: 18) reveals consistent focus on computational neuroscience approaches to memory systems , with recent work emphasizing cross-species auditory processing and neural representation dynamics. His 2022-2018 publications particularly highlight mechanistic distinctions between "what" and "when" predictions in sensory cortex. Teaching responsibilities include: Probabilistic and Statistical Modeling (Lecture) Introduction to Programming (Seminar) Research Colloquium for Qualification Theses

Yang Liمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +۵ مورد دیگر

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

Nuttida Rungratsameetaweemanaمشاهده پروفایل

استادیار

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +۶ مورد دیگر

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

Johannes Krebsمشاهده پروفایل

استاد

• Topological Data Analysis
• Persistent Homology
• Spatial Statistics
• +۳ مورد دیگر

Prof. Dr. Johannes Krebs holds the Chair of Mathematics - Statistics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt since 2021. His academic service includes membership on the editorial board of Statistics , the tenure track board, examination committees for B.Sc. Mathematics and Data Science programs, and the faculty council. His research focuses on topological data analysis, persistent homology, and spatial statistics with applications in stochastic geometry. Funded by Deutsche Forschungsgemeinschaft (KR 4977/2-1 and KR 4977/1-1), his work bridges theoretical statistics with computational topology to develop novel methods for analyzing complex data structures. His methodological contributions span wavelet analysis, functional data, and bootstrap techniques for dependent data. Analysis of his recent publications reveals strong emphasis on topological inference (persistent homology, Betti numbers), spatial statistics, and wavelet methods across multiple high-impact journals including Bernoulli , Annals of Statistics , and Stochastic Processes and their Applications . His work demonstrates consistent innovation in applying topological concepts to statistical problems with theoretical guarantees. Academic service highlights include: Editorial Board Member, Statistics journal Member of Tenure Track Board Examination Committee for B.Sc. Mathematics & Data Science Faculty Council Member His research program is supported by significant DFG funding for projects on Topological Data Analysis (2021-2023) and Dynamic Objects on Random Fields (2017-2020), reflecting sustained contribution to advancing statistical methodology through topological frameworks. He maintains active collaborations with researchers at UC Davis, KU Leuven, and Heidelberg University.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Marina Nordمشاهده پروفایل

پژوهشگر ارشد

• Political Economy
• Democratization Processes
• Autocratization
• +۴ مورد دیگر

Marina Nord is a Postdoctoral Research Fellow at the V-Dem Institute, affiliated with the Department of Political Science at the University of Gothenburg. Her work focuses on the intersection of international political economy and regime transformation, with particular emphasis on the economic foundations of autocratic regime stability and change. Her educational background includes: PhD in Political Economy from the Hertie School, Berlin Master of Public Policy from the Hertie School, Berlin MSc in Economics from Humboldt University of Berlin MSc in Applied Mathematics and Computer Science from Moscow State University Dr. Nord's research examines how economic factors influence regime trajectories, with methodological expertise in statistical causal inference and machine learning applications for social science. Her work bridges academic research and policy domains, investigating democratic backsliding, electoral integrity, and institutional resilience against authoritarian challenges. She employs both historical analysis and contemporary case studies to identify patterns in regime change across the 20th and 21st centuries. Her recent publications through the V-Dem Institute's Democracy Reports reveal critical trends: increasing global autocratization over the past 25 years, the vulnerability of electoral institutions, and rare but significant episodes where autocratization has been reversed. These works combine quantitative analysis of the V-Dem dataset with theoretical insights about economic drivers of regime stability. Dr. Nord collaborates extensively with the international V-Dem team on comprehensive democracy assessments used by scholars and policymakers worldwide. Her current research continues to explore the economic underpinnings of regime (in)stability while developing innovative methodological approaches to studying political change.