جست‌وجوی استاد و پژوهشگر

Emilia Ellsiepenمشاهده پروفایل

پژوهشگر

• Computational Linguistics
• Eye-tracking Research
• Situated Language Processing
• +۳ مورد دیگر

Dr. Emilia Ellsiepen is a Postdoctoral Researcher and lab manager in Prof. Vera Demberg's group at Saarland University, working in the field of Computer Science and Computational Linguistics. She is associated with the SFB-1102 "Information Density and Linguistic Encoding" and the CRC248 "Center for Perspicuous Computing". Her research interests focus on: Computational Linguistics and language processing Eye-tracking correlates of unknown words Integration of language and visual attention Enhancing communication between cyber-physical systems and human controllers Methodological questions and statistics in linguistic research Dr. Ellsiepen regularly assists with lectures on Statistics with R and Statistics Lab, demonstrating her expertise in statistical methods applied to linguistic research. Her previous work has included experimental research in situated language processing and gradience in grammar. She is actively contributing to two major research collaborations: the SFB-1102 investigating information density in linguistic encoding, and the CRC248 Center for Perspicuous Computing focusing on human-system interaction.

Bastian Bohnمشاهده پروفایل

پژوهشگر

• Mathematics of Machine Learning
• Approximation Theory
• High-dimensional Discretizations
• +۳ مورد دیگر

Bastian Bohn is a Researcher at the Institute for Numerical Simulation (University of Bonn). He focuses on Mathematics of Machine Learning , Approximation Theory , and High-dimensional Discretizations . His work bridges Sparse Grid Methods Numerical Analysis Scientific Computing with modern ML applications. His recent publication Algorithmic Mathematics in Machine Learning (2024) consolidates his work on algorithmic foundations for high-dimensional data analysis. Earlier studies investigate Deep Kernel Learning Explainable AI Dimensionality Reduction using sparse grid frameworks.

• Process Mining
• Data Privacy
• Event Log Analysis
• +۳ مورد دیگر

Martin Kabierski serves as a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin, specializing in process mining and data privacy. His work bridges theoretical frameworks and practical applications in event log analysis and privacy preservation. Education: Master of Science (M.Sc.) Kabierski's research centers on advancing process mining methodologies through species estimation techniques for data quality assessment, privacy-preserving mechanisms, and conformance checking. He has pioneered approaches for log representativeness evaluation, control-flow anonymization, and stream processing optimization. His work consistently addresses critical challenges in balancing analytical utility with data privacy requirements in business process analysis. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research trajectories: (1) Species estimation applications for quantifying process representation completeness and diversity in event logs; (2) Privacy-preserving frameworks for process performance indicators and control-flow anonymization; (3) Stream mining innovations for window sizing and real-time conformance checking. These themes demonstrate his systematic approach to solving data quality and privacy challenges in process mining. No scientific awards are documented in the available information. Kabierski has not publicly listed any doctoral or master's students under his supervision. His research appears to be conducted collaboratively within institutional frameworks without mention of specific grant funding in the provided materials. He operates within the Databases and Information Systems research group at Humboldt University, contributing to the institute's focus on foundational data management and analysis techniques with applications in process mining.

Daria Tischمشاهده پروفایل

پژوهشگر

• Economic Inequality
• Wealth Inequality
• Gender Inequality
• +۳ مورد دیگر

Dr. Daria Tisch is a Senior Researcher at the Research Group on Wealth and Social Inequality at the Max Planck Institute for the Study of Societies in Cologne, Germany. She earned her PhD in Sociology (Summa cum laude) from the University of Cologne (2017–2021), following an M.Sc. in Sociology and Empirical Social Research (2014–2017) and a B.A. in Political Science (2011–2014). Education PhD in Sociology, University of Cologne (Summa cum laude) M.Sc. Sociology and Empirical Social Research, University of Cologne Minor: Statistics and Econometrics B.A. Governance and Public Policy, University of Passau Erasmus Exchange, Cardiff University Her research focuses on wealth inequality , gender disparities , and family dynamics , using survey experiments, tax data, and web scraping. She explores how intergenerational transfers and tax systems perpetuate gender wealth gaps and examines the composition of Germany’s superrich through historical rich lists and Wikidata network analysis. Recent work analyzes the gender tax gap in inheritance and gifts (2023–2024) and the role of family networks in wealth preservation over centuries. She also investigates perceptions of fairness in parental financial gifts and the social implications of joint vs. sole wealth ownership in marriages. Scientific Awards ECSR Network Workshop Grant (2020) Dean’s Award (University of Cologne, 2015) Fellow of the Friedrich Ebert Foundation (2011–2017) She supervises PhD students at the Max Planck Institute and has taught courses on wealth inequality, longitudinal data analysis, and research methods at institutions including the University of Cologne and the Max Planck Institute. Her work has been featured in media outlets such as Zeit Online and Merkur .

Michael Frankeمشاهده پروفایل

استاد

• Experimental Pragmatics
• Cognitive Modeling
• Probabilistic Modeling
• +۷ مورد دیگر

Michael Franke is a Professor in the Department of Linguistics at the University of Tübingen. His research focuses on experimental pragmatics, cognitive modeling, and evolutionary approaches to language use, reasoning, and decision-making processes. He employs probabilistic modeling techniques to investigate higher-order cognition and the relationship between empirical data, abstract theory, and mathematical modeling. His primary research interests include experimental pragmatics, cognitive and evolutionary modeling, probabilistic reasoning, Bayesian statistics, and computational linguistics. Franke's work bridges theoretical linguistics with experimental cognitive science, using both human behavioral data and computational modeling approaches. He has made significant contributions to understanding scalar implicatures, pragmatic reasoning, and the application of game theory to linguistic phenomena. Recent publications reveal a growing focus on the intersection of human cognition and language models, particularly examining how large language models handle pragmatic phenomena like scalar implicatures, quantifier interpretation, and question answering. His work increasingly integrates neural and symbolic approaches to model pragmatic reasoning processes. Professor Franke has developed several important research tools including magpie (a framework for web-based experiments), faintr (an R package for interpretation of categorical predictors in Bayesian regression), and R_eproducible_LaTeX (for writing reproducible research papers). He has also created educational resources including web-books on probabilistic pragmatics, Bayesian regression, and understanding large language models. In his teaching, Franke offers courses on logic, data analysis, and Bayesian regression. His educational materials demonstrate his commitment to making complex statistical and computational methods accessible to students in linguistics and cognitive science.

• Sports Analytics
• Sports Statistics
• Hidden Markov models
• +۲ مورد دیگر

Rouven Michels is a researcher affiliated with the Department of Statistics at the Technical University of Dortmund, located in Germany. He is part of the working group led by Prof. Dr. Andreas Groll, focusing on advanced statistical methodologies for data analysis. His primary research interests include Sports Analytics, particularly applying statistical techniques to sports data, Hidden Markov models for sequential data analysis, State-space models for dynamic systems, and the Modelling of bivariate count data in various applications. As a member of the Faculty of Statistics, Michels contributes to the development and application of statistical methods for big data challenges. His work bridges theoretical statistical modeling with practical implementations in sports science and other domains requiring complex data analysis frameworks. Though no specific articles or awards are listed here, his involvement in the department underscores his active role in advancing statistical research.

Stephan Brosigمشاهده پروفایل

پژوهشگر

• Food consumption
• Nutrition and health
• Socioeconomics of rural development
• +۴ مورد دیگر

Dr. Stephan Brosig is a Researcher at the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) specializing in rural development, food consumption, and statistical methods. He has contributed to projects on agricultural factor markets, sustainability, and data management, with a focus on China and Eastern Europe. Agricultural sustainability metrics Rural labor market dynamics in China Corporate Social Responsibility in German agriculture Research Areas include rural development in China, food security, and socio-economic analysis using statistical models. His work often intersects with public health, examining dietary quality and its economic determinants. Notable Trends in his 15 most recent publications highlight agricultural sustainability, CSR practices, land market regulation, and post-war recovery strategies in Ukraine. Collaborative projects with institutions across Germany, China, and Eastern Europe are central to his work. Advising and Grants involve inter-institutional data management initiatives and projects like DEDIPAC, REAP, and BRIC-related studies. He actively contributes to international conferences and policy briefs.

• Attosecond Physics
• High-Order Harmonic Generation
• Strong-Field Ionization
• +۳ مورد دیگر

Dr. Serguei Patchkovskii is a researcher at the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy in Berlin, leading the Attosecond Theory Group. His work focuses on ultrafast phenomena in atomic and molecular systems, particularly in the context of strong-field laser interactions and attosecond science. His research integrates theoretical and computational methods to study processes such as high-order harmonic generation, photoelectron dynamics, and quantum control. Key research interests include: Attosecond transient interferometry Spin-polarized photoelectron behavior Ultrafast nuclear dynamics in molecules Nonlinear light-matter interactions Quantum coherence in laser-driven systems Recent work highlights include experimental/theoretical studies on entangled photon states, spin polarization control via recollisions, and the development of statistical frameworks for imperfect measurement apparatuses. His contributions bridge fundamental quantum mechanics with applied laser technologies.

Xiangnan Xuمشاهده پروفایل

استاد

• Deep Learning
• Biomedical Data Analysis
• Statistical Modeling
• +۳ مورد دیگر

Xiangnan Xu is a Professor and holds the Chair of Statistics at the School of Business and Economics, Humboldt University of Berlin. Their research focuses on integrating deep learning and statistical methods to analyze structured biomedical data, as highlighted in the DesBi initiative. Contact details include the email xiangnan.xu@hu-berlin.de and office location SPA1, 313. Research Interests: Combining advanced computational techniques with statistical theory to address challenges in biomedical data analysis. The DesBi initiative emphasizes interdisciplinary approaches to understand complex biological systems through data-driven methods. No specific articles are listed in the provided text, but the focus areas suggest contributions to machine learning applications in healthcare and statistical methodologies for structured data. No scientific awards or grants are explicitly mentioned in the available information.

Shakoor Poosehمشاهده پروفایل

پژوهشگر

• Mathematical Psychology
• Computational Neuroscience
• Value-based Decision-making
• +۵ مورد دیگر

Shakoor Pooseh is a Postdoctoral Research Fellow at the Freiburg Center for Data Analysis and Modeling (FDM) , affiliated with the Institute of Physics at the University of Freiburg. His research focuses on computational approaches to decision-making, resilience dynamics, and neuropsychopharmacology. He leads the Value-Based Decision-Making Battery project, a Bayesian adaptive assessment tool for impulsive and risky behavior, and contributes to the EU Horizon 2020 DynaMORE initiative modeling psychological resilience. Education: Completed a PhD in Computational Methods in the Fractional Calculus of Variations at the University of Aveiro, Portugal (2013), with prior work in fractional optimal control problems. His interdisciplinary background bridges mathematics, neuroscience, and clinical psychology. Research Interests: Explores mathematical psychology, computational neuroscience, and statistical modeling of decision processes. Key topics include serotonin/dopamine modulation of impulsivity, resilience dynamics under stress, and translational research in addictive disorders. He develops software tools for behavioral experiments and machine learning models. Grants/Projects: Principal investigator for the DynaM-OBS longitudinal study on resilience, and collaborates on interventions like DynaM-INT . His work integrates clinical data with computational models to understand mental health outcomes. Labs/Teams: Active in the FDM interdisciplinary group and the Institute of Physics’ neuroscience unit. Collaborates with psychiatric research teams at the University Medical Center Freiburg.

Dimitrije Markovićمشاهده پروفایل

پژوهشگر

• Cognitive Neuroscience
• Theoretical Neuroscience
• Machine Learning
• +۴ مورد دیگر

Dimitrije Marković is a Researcher in the Department of Psychology at Technische Universität Dresden, affiliated with the Faculty of Science. His work bridges cognitive neuroscience, theoretical neuroscience, and machine learning, focusing on computational models of human decision-making and adaptive behavior. He employs concepts from information theory and probability to develop and test experimental predictions from these models. Education: PhD in Physics (2013), Goethe University Frankfurt, advised by Prof. Claudius Gros Diploma in Theoretical and Experimental Physics (2007), Belgrade University Undergraduate studies in Physics (2002–2007), Belgrade University Research Focus: His research emphasizes understanding neurophysiological and computational mechanisms underlying human decision-making, particularly in dynamic environments. He explores topics like adaptive learning, Bayesian inference, and neural modeling of uncertainty and agency perception. Publications Trends: Recent work includes advancements in active inference frameworks, machine learning models for decision-making, and applications to mental health (e.g., depression). His 2020–2025 publications highlight innovations in scalable models (e.g., AXIOM), Bayesian methods, and interdisciplinary approaches linking neuroscience with AI. Professional Experience: Postdoc at TU Dresden (2014–present) Guest Researcher, Max-Planck Institute for Human Cognitive and Brain Sciences (2013–2015) Postdoc, University Clinic Jena (2013–2014) Labs/Teams: Affiliated with TU Dresden’s Chair for Neuroimaging, focusing on neuroimaging techniques and collaborative projects in computational psychiatry and AI-driven neuroscience.

Peggy Wehnerمشاهده پروفایل

پژوهشگر

• Cognitive Psychology
• Decision Making
• Computational Modeling
• +۲ مورد دیگر

Peggy Wehner is a doctoral researcher at the Institute of General Psychology, Department of Psychology, Faculty of Science, Dresden University of Technology. Her work integrates empirical process measures and computational modeling to study cognitive sub-processes in intertemporal and probabilistic decision-making. M.Sc. Psychology: Cognitive Affective Neuroscience (2020), TU Dresden B.Sc. Psychology (2017), TU Dresden Her research focuses on executive functions in self-structured environments, exploring how cognitive flexibility and processing strategies influence decision-making. She has developed educational tools for statistics and cognitive modeling, and her publications span topics like task interruption management, social creativity, and phenotyping methodologies. Professional roles include tutoring 'Data Analysis with R' (2020–present) and 'Advanced Statistical Methods' (2018–2020), with prior experience as a student assistant and research assistant in the Chair of Psychological Research Methods and Cognitive Modeling at TU Dresden.

Hannah Lausمشاهده پروفایل

پژوهشگر

• Data Science
• Uncertainty Quantification
• High-Dimensional Learning
• +۳ مورد دیگر

Hannah Laus is a Researcher at the Department of Mathematics within the School of Computation, Information and Technology at the Technical University of Munich. Her work focuses on advanced data science methodologies, particularly in uncertainty quantification, high-dimensional learning, and optimization algorithms. She contributes to research groups including Data Science, Probability Theory, and Numerical Analysis. Her research interests revolve around addressing challenges in inverse problems, machine learning, and statistical learning theory. Notable contributions include studies on uncertainty quantification in undersampled medical imaging and convergence guarantees for deep learning algorithms. Recent publications emphasize interdisciplinary applications of mathematical techniques in high-dimensional settings and iterative thresholding algorithms. No scientific awards are explicitly listed, but her involvement in collaborative projects like the TUM-ICL Mathematical Sciences Hub and the Cluster of Excellence MCQST highlights her active role in academic partnerships. As part of the academic staff, Laus is affiliated with the TUM’s outreach initiatives such as TUM-Entdeckerinnen and the Data Innovation Lab, promoting mathematical education and innovation. No doctoral advisees are listed in the provided information.

• Discrete Geometry
• Differential Geometry
• Mathematical Physics
• +۳ مورد دیگر

Jannik Steinmeier is a Researcher at the Department of Mathematics, Technische Universität München (TUM), affiliated with the Discrete Differential Geometry group led by Tim Hoffmann. He holds an M.Sc. in Mathematics and can be reached at jannik.steinmeier@tum.de in room 02.06.058. His research focuses on discrete and differential geometry, geometric modeling, and mathematical physics, with notable contributions to topics like conjugate semi-geodesic nets and skew parallelogram nets. Education includes a Bachelor's thesis on Koenigs nets (2018) and a Master's thesis on discrete conjugate semi-geodesic nets (2021). He taught exercise sessions for the course 'Geometry' (MA2011) in Summer 2024. His work bridges theoretical geometry with computational methods, emphasizing discrete structures and their applications in modeling.

• Contemporary History
• History of Violence
• History of Knowledge
• +۳ مورد دیگر

Prof. Dr. Kerstin Brückweh is a historian and current Professor for Historical Urban and Spatial Research at the European University Viadrina in Frankfurt (Oder), where she also leads the research focus 'Contemporary History and Archives' at the Leibniz Institute for Research on Society and Space (IRS) . Her career spans multiple institutions including TU Berlin, University of Tübingen, and Leibniz Centre for Contemporary History in Potsdam. Current roles: European University Viadrina (since 2023), IRS Research Focus Head Previous roles: TU Berlin Professor (2021-2023), Fellow at Max Weber Center (2019-2023) Research Interests center on: History of violence and emotions Knowledge production through statistics Transformation of East Germany (1989 onwards) Property rights evolution post-reunification Key Publications & Projects (2020-2024) span topics from census methodology to villa neighborhood mapping. Current projects include: GeStat (2024-2028): History of Federal Statistical Office RichMap (2025-2028): Wealth cultures in 20th-century Germany Scientific Recognition : Göttingen Academy Prize Potsdam Prize for Science Communication Academic Background : PhD from Bielefeld University (dissertation on 20th-century serial murders), Habilitation at University of Tübingen (censuses in knowledge production). She previously worked at German Historical Institute London (2003-2009).