جست‌وجوی استاد و پژوهشگر

Ingo Scholtesمشاهده پروفایل

استاد

• Network Science
• Graph Neural Networks
• Computational Social Science
• +۴ مورد دیگر

Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.

Yen-Chi Chenمشاهده پروفایل

دانشیار

• Nonparametric statistics
• Topological data analysis
• Missing data
• +۷ مورد دیگر

Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

James Urquhart Allinghamمشاهده پروفایل

پژوهشگر

• Bayesian Deep Learning
• Deep Generative Models
• Probabilistic Methods in Machine Learning
• +۷ مورد دیگر

James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .

• Computational Linguistics
• Historical Linguistics
• Phylogenetic Linguistics
• +۸ مورد دیگر

Prof. Gerhard Jäger holds the Chair of General Linguistics at the Faculty of Humanities, University of Tübingen . He serves as a Principal Investigator (PI) in the Clusters of Excellence Human Origins and Machine Learning for Science , and leads projects like Phylomilia (funded by Volkswagen Foundation) and CrossLingference (ERC Advanced Grant). His career spans multiple institutions, including Bielefeld University (2004-2009) and Stanford University (visiting scholar, 2004). Habilitation (2002) at Humboldt University Berlin with thesis on Anaphora and Type Logical Grammar PhD (1996) at Humboldt University Berlin on Dynamic Semantics His research bridges computational linguistics , phylogenetic analysis , and game theory , focusing on Bayesian models , language evolution , and cross-linguistic typology . Recent work explores phylogenetic inference from acoustic speech data and geographic influences on language trees . Key contributions include 15+ recent publications on topics spanning phylogenetic typology , cognate detection , and Bayesian language modeling . These works employ machine learning , statistical inference , and evolutionary game theory to analyze language change , typological variation , and linguistic stability . Honors include ERC Advanced Grant , Volkswagen Foundation funding , and DFG-Humanities Centre for Advanced Studies participation. He has taught courses in Computational Historical Linguistics , Phylogenetic Methods , and Bayesian Data Analysis across institutions like Tübingen, Bielefeld, and Stanford. He actively contributes to academic communities through workshop organization (e.g., Quantitative Theoretical Linguistics , Game Theory in Pragmatics ) and serves on the faculty council at Tübingen. His team collaborates with institutions like Max Planck Institute for Evolutionary Anthropology , University of Pennsylvania , and LMU Munich .

• Econometrics
• Nonparametric Methods
• Financial Economics
• +۳ مورد دیگر

Joachim Freyberger is a Professor at the University of Bonn , affiliated with the Department of Economics. He is associated with the Institute for Financial Economics & Statistics and the Hausdorff Center for Mathematics, focusing on econometrics and nonparametric methods. Institute for Financial Economics & Statistics Hausdorff Center for Mathematics His research spans econometrics, nonparametric identification, instrumental variables, and financial economics. Key areas include shape restrictions in estimation, interactive fixed effects in panel data, and structural analysis of consumer markets and asset pricing. Recent publications show a focus on nonparametric econometric theory, interactive fixed effects models, and applications to financial panels. Papers address challenges in identification, confidence band construction, and digital market analysis. He teaches advanced courses including Econometrics I and II , Topics in Econometrics and Statistics , and Research Module in Econometrics at Bonn, as well as introductory and graduate econometrics at UW-Madison.

• Statistics
• Sampling Theory
• Quantitative Methods
• +۳ مورد دیگر

Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.

Tyler McCormickمشاهده پروفایل

استاد

• Bayesian Statistics
• Social Network Analysis
• Statistical Modeling
• +۵ مورد دیگر

Tyler McCormick is a Professor in both the Department of Statistics and Department of Sociology at the University of Washington. He also serves as a Senior Data Science Fellow at the eScience Institute and maintains affiliations with the Center for Statistics and the Social Sciences, the Center for Studies in Demography and Ecology, and the Responsible AI Systems & Experiences (RAISE) initiative. Dr. McCormick earned his Ph.D. in Statistics from Columbia University in 2011. His academic journey has established him as a leading researcher at the intersection of statistical methodology and social science applications. McCormick's research program focuses on developing innovative statistical approaches to address complex societal challenges: Bayesian methods for modeling high-dimensional dependence structures in social networks Estimating vital demographic rates from sparse data sources Developing interpretable predictive models with proper uncertainty quantification Creating methodological frameworks for verbal autopsy analysis in global health His publication record reveals a consistent trajectory of methodological innovation with practical impact. Recent work demonstrates increasing sophistication in handling network interference, integrating machine learning with statistical theory, and addressing data scarcity challenges in global health contexts. His research bridges theoretical advances with applications that inform public health policy and social science understanding. McCormick has received significant recognition for his scholarly contributions: NIH Director's New Innovator Award (2019) Election as Fellow of the American Statistical Association (2023) As an educator, McCormick teaches advanced graduate courses including Hierarchical Modeling for the Social Sciences and Quantitative Techniques in Sociology. His research has been supported by competitive grants from NICHD (2015-2020) focused on vital rate estimation in developing countries and NSF (2016-2018) funding for compact Bayesian models of social networks. His work has influenced policy discussions through media coverage in the Wall Street Journal and Washington Post. McCormick leads the OpenVA initiative, providing open-source tools for verbal autopsy analysis, and has developed multiple R packages implementing his methodological contributions to network analysis and causal inference. His research continues to address critical challenges at the intersection of statistical theory, computational methods, and societal impact.

• Econometrics
• Nonparametric Estimation
• Causal Inference
• +۴ مورد دیگر

Christoph Breunig is a Professor in the Department of Economics at the University of Bonn. His work bridges theoretical econometrics with empirical applications, focusing on nonparametric methods, instrumental variable modeling, and causal inference. His research addresses challenges in high-dimensional data, missingness mechanisms, and treatment effect estimation. University: University of Bonn Department: Economics Academic Rank: Professor Email: cbreunig@uni-bonn.de Research Trends: Nonparametric and semiparametric estimation techniques Applications of instrumental variables in causal inference Handling missing data and measurement error High-dimensional statistical models with economic applications Specification testing in complex regression frameworks Connections between microeconomic theory and empirical methods

• Particle Physics
• High-Energy Physics
• Data Science
• +۳ مورد دیگر

Prof. Heinrich Lukas is a Professor of Data Science in Physics at the Technical University of Munich, affiliated with the TUM School of Natural Sciences and the Department of Physics. His research focuses on experimental particle physics, leveraging advanced data science techniques and computational methods in collaboration with the ATLAS experiment at CERN's LHC. He actively contributes to detector calibration, high-energy collision analysis, and machine learning applications in physics. Recent work includes studies on vector-like quarks, Higgs boson properties, and off-shell phenomena, utilizing cutting-edge simulation-based inference and cloud computing infrastructure. His interdisciplinary projects span from collider physics to healthcare-related studies, such as analyzing dermatitis treatments in clinical registries. Key research interests include: Machine learning-driven particle physics analysis Collider data interpretation (ATLAS experiment) Jet physics and flavor tagging Searches for Beyond Standard Model particles Computational infrastructure optimization Publications (2025) highlight contributions to missing transverse momentum reconstruction , vector-like quark searches , and neural simulation-based inference . Collaborations involve international teams working on ATLAS Run-2 datasets and LHC Run 3 computing frameworks. Current efforts emphasize leveraging AI infrastructure for particle physics while maintaining involvement in medical data analysis.

Oskar Engelsمشاهده پروفایل

پژوهشگر

• Educational Measurement
• Data Science
• Item Response Theory
• +۲ مورد دیگر

Oskar Engels is a Researcher and Doctoral Student at the Educational Measurement and Data Science department at the Leibniz Institute for Science and Mathematics Education (IPN) since 2023. His research focuses on educational measurement, data science, item response theory, vertical scaling, and missing data analysis. He previously served as a Scientific Assistant at the Institut für Bildungsmonitoring und Qualitätsentwicklung from 2022 to 2023. Education: Master in Sociology, University of Hamburg (2022) Bachelor in Sociology, University of Hamburg (2018) Research Interests: His work centers on refining statistical methods for educational assessments, including modeling item responses, scaling techniques across grade levels, and handling missing data in educational datasets. These interests align with the IPN’s methodological research and machine learning initiatives. Contact: Email engels@leibniz-ipn.de | Phone: +49-(0)431-880-1315 | Location: Room 35c, IPN, Kiel, Germany.

• Psychometrics
• Educational Measurement
• Statistical Modeling
• +۳ مورد دیگر

Prof. Dr. Steffi Pohl is a Professor of Methods and Evaluation/Quality Assurance at the Department of Methods and Evaluation/Quality Assurance, Faculty of Education and Psychology, Freie Universität Berlin. She holds editorial roles in prominent journals like Psychometrika and Journal of Educational and Behavioral Statistics . Her research focuses on psychometrics, log data analysis, missing data mechanisms, and causal inference in educational assessments. Steffi Pohl's academic journey includes a Diplom in Psychology from Freie Universität Berlin (1998–2004) and a Ph.D. in Psychometrics (2005–2010) from Friedrich-Schiller-Universität Jena. She has held various research positions, including at the National Educational Panel Study (NEPS) and the University of Hertfordshire, UK. Her work emphasizes methodological advancements in testing, missing data, and measurement invariance across populations. Her research interests span psychometric modeling, log data analysis, and addressing challenges in large-scale educational assessments. Key contributions include innovations in response time analysis, disentangling engaged/disengaged test behaviors, and improving cross-cultural comparisons in educational rankings. She has been recognized with the 2020 Early Career Award from the Psychometric Society and the 2011 Gustav A. Lienert Dissertation Prize. In addition to research, Pohl teaches courses on empirical social research methods and multivariate analysis techniques. She actively contributes to university governance as a member of the Academic Senate at Freie Universität Berlin and serves as a trusted advisor for the German National Academic Foundation.

Andreas Zendlerمشاهده پروفایل

استاد

• Software Engineering for Cloud Computing
• Data Science / Data Mining
• Empirical Computer Science Didactics
• +۹ مورد دیگر

Andreas Zendler serves as Director of the Institute of Computer Science at Ludwigsburg University of Education (2023-25), where he has held various leadership positions since 2004, including Head of the Department of Computer Science (2018-2022) and Director of the Institute of Mathematics and Computer Science (2011-2016). As a Professor (C4) for Computer Science and its Didactics since 2004, he has shaped computer science education programs including the BA/MA degree program in Computer Science. Zendler earned dual doctorates - a Dr. rer. nat. in Practical Computer Science from the University of Potsdam (1997) and a Dr. phil. in Experimental Psychology from the University of Regensburg (1988), followed by his habilitation in Practical Computer Science at the University of Potsdam (2000). His academic journey spans computer science, psychology, and education, creating a unique interdisciplinary foundation for his research. His research focuses on empirical computer science didactics, software engineering for cloud computing, data science, and the digitalization of teaching through SaaS models. He investigates content and process concepts for computer science teaching, compares learning effectiveness of instructional methods, and develops research methodologies specifically for educational contexts with small sample sizes. His work bridges theoretical frameworks with practical classroom applications across STEM disciplines. Zendler's publication record reveals a consistent trajectory from foundational software engineering research toward educational applications. His recent work (2018-2025) demonstrates increasing specialization in computer science education, particularly in competence measurement frameworks like cpm.4.CSE/IRT and their adaptations for small sample sizes. His upcoming publications indicate a strategic pivot toward AI applications, exploring how LLMs and GPTs can transform both data science practices and software engineering education. Throughout his career, Zendler has served as a dissertation reviewer for Practical Computer Science since 2001 and contributed to numerous research projects funded by organizations including the European Union, the Bavarian Research Foundation, and the German Aerospace Center (DLR). His project leadership spans bioinformatics, educational informatics, and software engineering domains. His methodological expertise includes experimental design for teaching research (particularly 3-factorial designs with repeated measurements), statistical evaluation approaches for small samples, and handling missing data in educational contexts. This technical proficiency supports his broader mission to establish rigorous empirical foundations for computer science education.

Anna-Carolina Haenschمشاهده پروفایل

استادیار

• Missing Data Analysis
• Synthetic Data Generation
• Big Data in Social Sciences
• +۶ مورد دیگر

Anna-Carolina Haensch is a Lecturer at the University of Munich in the Chair of Statistics and Data Science in Social Sciences and the Humanities and an Assistant Professor at the University of Maryland in the International Program in Survey and Data Science. Her interdisciplinary work bridges statistics, computational social science, and natural language processing, with a focus on methodological innovation in social research. Education: PhD in Sociology, University of Mannheim (2017-2021) M.Sc. in Survey Statistics, University of Bamberg (2014-2017) B.A. in Political Science/Sociology, Ludwig-Maximilians-Universität München (2011-2014) Dr. Haensch's research centers on missing data, synthetic data, and big data applications in social sciences. She develops advanced statistical methods for survey data harmonization, multiple imputation techniques, and leverages natural language processing to analyze complex social phenomena. Her work consistently addresses methodological challenges in social research with practical applications for data collection and analysis. Her recent publications reveal a significant trend toward integrating large language models with traditional survey methodology, exploring how AI can enhance data collection, analysis, and interpretation in social science research. She has made substantial contributions to understanding missing data patterns, developing synthetic data approaches, and examining the societal implications of AI tools across multiple domains including mental health, political science, and housing policy. Scientific Awards: 2022 AAPOR Burns "Bud" Roper Fellow Award 2022 AAPOR Warren J. Mitofsky Innovators Award (as part of CTIS team) 2022 AAPOR Policy Impact Award (as part of CTIS team) 2011-2017 Max-Weber-Programm (undergraduate and graduate stipend) Dr. Haensch actively mentors the next generation of researchers, currently supervising 3 PhD theses on statistical education, machine learning applications in social sciences, and synthetic data generation with LLMs. She has guided approximately 6 master's theses and 15 bachelor's theses at the University of Munich since 2022, with topics primarily related to synthetic data, multiple imputation, and LLM applications. Her teaching spans statistical methods, data science techniques for survey researchers, and specialized topics in big data analysis. She has secured teaching grants including a €20,000 promotion for the RAINER project (R Assistant IN Error Resolution) for 2024-2025 and a €10,000 LMU-NYU Scholarship in 2023. She serves on several important boards including the Eurostat EMOS Board (2024-2026), the Ethics Commission of Faculty 16 at LMU (2023-2025), and as Women's Representative at the Institute for Statistics, LMU (2024-2026). Her collaborative work with the University of Maryland Social Data Science Center and involvement in the Global COVID-19 Trends and Impact Survey demonstrates her commitment to large-scale data collection initiatives and real-time social research.

Junyi Zhuمشاهده پروفایل

استاد پژوهشی

• Labor Economics
• Applied Microeconomics
• Public Economics
• +۳ مورد دیگر

Junyi Zhu is a Research Professor at the Research Centre of the Deutsche Bundesbank. His research focuses on labor economics, applied microeconomics, and public economics with a particular emphasis on household finance, wealth distribution, and policy analysis. He has contributed to significant projects like the Panel on Household Finances (PHF) in Germany, providing microdata insights into household wealth dynamics. His work bridges theoretical economic models with empirical analysis, addressing issues such as credit constraints, tax policy, and wealth inequality. Affiliations: Deutsche Bundesbank Research Centre since 2010 Research interests include the redistribution effects of price level changes, household saving behaviors, and the interplay between inheritance and marital sorting. His publications span journals like the Journal of the European Economic Association and German Economic Review . Zhu has presented at numerous international conferences, including the World Congress of the International Microsimulation Association and the European Meeting of the Econometric Society. His work emphasizes policy-relevant findings, such as solutions to bracket creep and the design of effective economic stimulus measures. Despite extensive contributions, no specific scientific awards are documented in the provided materials.