جست‌وجوی استاد و پژوهشگر

• Causal Analysis
• Statistics
• Econometrics
• +۵ مورد دیگر

Professor Martin Huber is a leading academic in Applied Econometrics and Policy Evaluation at the University of Fribourg within the Faculty of Management, Economics and Social Sciences . He also serves as a Research Associate at the Centre for European Economic Research (ZEW) and collaborates with institutions like Soda Labs (Monash Business School) . PhD in Economics and Finance (2010) Assistant Professor at University of St.Gallen (2010-2014) Research stays at Harvard University (2011/2012) and University of Sydney (2014, 2019) His research focuses on data-based causal analysis , machine learning , and policy evaluation in labor, health, and education economics. He has pioneered the integration of causal machine learning with traditional econometric methods, developing novel algorithms for bid-rigging detection and dynamic treatment effect analysis. Recent publications demonstrate methodological advancements in graph attention networks , doubly robust estimation , and causal mediation analysis . His work addresses practical challenges like sample selection bias , instrument validation , and high-dimensional data in policy evaluation. Huber actively engages in teaching through initiatives like the ZEW summer school and Fribourg Winter School , where he instructs on R , Python , and causal analysis tools . He maintains a strong online presence through @causalhuber.bsky.social and has published Impact Evaluation in Firms and Organizations (MIT Press, 2023).

• Computer Graphics
• Physics-Based Simulation
• Fluid Dynamics
• +۳ مورد دیگر

Prof. Dr. Jan Bender holds a professorship in Computer Animation at RWTH Aachen University's College of Engineering. As a leading researcher in physics-based simulation methods, his work focuses on developing advanced numerical techniques for fluid dynamics, deformable solids, and multi-physics interactions through Smoothed Particle Hydrodynamics (SPH) and Finite Element Methods (FEM). Key Contributions: Invented PF-FLIP for two-phase flows, developed SymX symbolic framework for energy-based simulations, created STARK unified solver for robotics applications, and introduced implicit boundary handling for SPH Methodologies: Specializes in hybrid Eulerian/Lagrangian approaches, differentiable physics, adaptive discretization, and machine learning integration for simulation acceleration Research Impact: 2023 & 2024 Best Paper Awards in VMV and SCA conferences. His work enables billion-particle fluid simulations and realistic multi-body interactions for robotics, with applications in welding, thermal spraying, and soft robotics. Collaborations: Works extensively with robotics institutes (Gazebo Fluids extension) and materials science departments (TIG welding, thermal spray modeling). Maintains open-source code repositories for simulation frameworks.

• Multiple testing / false discovery rate
• Asymptotic statistics / large deviations theory
• Theory of nonparametric tests
• +۳ مورد دیگر

Thorsten Dickhaus is a Professor of Mathematical Statistics at the University of Bremen , affiliated with Faculty 3 - Mathematics and the Institute for Statistics . His research focuses on multiple testing procedures, asymptotic statistics, nonparametric methods, and computational applications in life sciences. He holds a Ph.D. in Mathematics from Heinrich-Heine-University Düsseldorf (2008) and has held academic positions at Humboldt University Berlin and the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin before joining the University of Bremen in 2015. Key professional roles include: Principal Investigator in the Research Training Group (RTG) 2224 π³ (Parameter Identification) Principal Investigator in the Helmholtz School for Marine Data Science (MarDATA) Editorial roles for journals like Journal of Statistical Planning and Inference and Statistics (Berlin) Research projects include funded initiatives on multiple testing methodologies, neuroeconomics, and statistical inference in behavioral genetics. His Erdős number is 3, and he actively contributes to academic service through conference organization and editorial work.

Christian Rohlfingمشاهده پروفایل

پژوهشگر

• Signal Processing
• Data Compression
• Audio Source Separation
• +۳ مورد دیگر

Dr.-Ing. Christian Rohlfing is a Researcher at the Institute of Communications Engineering , RWTH Aachen University . His work spans genomic data compression , audio source separation , and video coding . Rohlfing’s research focuses on efficient genomic sequence compression using entropy coding, context-based methods, and standardization within MPEG-G . He also advances point cloud compression via graph transforms and adaptive entropy coding, and audio/video coding with non-negative matrix factorization (NMF), dictionary learning, and convolutional neural networks (CNNs). His publications (2011–2023) reflect expertise in signal processing for multimedia and bioinformatics , with applications in spatial audio coding , informed source separation , and gene variation compression . Rohlfing contributes to international standards like ITU-T and ISO/IEC MPEG , particularly in Versatile Video Coding (VVC) and MPEG-G for genomic data.

• Econometrics
• Time Series Analysis
• Statistical Learning
• +۶ مورد دیگر

Karsten Reichold is an Assistant Professor at TU Wien, affiliated with the Institute of Statistics and Mathematical Methods in Economics within the Faculty of Mathematics and Geoinformation. His research bridges econometrics, time series analysis, and statistical learning, with a focus on robust inference in cointegrating regressions and forecasting applications in macroeconomics. His research interests include: Econometrics and time series modeling Statistical learning methods in economics Bootstrap and resampling techniques Empirical macroeconomic forecasting Stochastic processes and cointegration Panel data and polynomial cointegration The recent publication trend indicates a strong methodological focus on bootstrap inference, particularly self-normalized test statistics in cointegrating regressions, with implementation provided through open-source MATLAB code. His work emphasizes practical, ready-to-use econometric tools for robust statistical inference. Karsten Reichold has not been mentioned as receiving any scientific awards in the provided material. He is actively involved in teaching, offering courses such as Selected Topics in Econometrics, Stationary Processes and Time Series Analysis, and Introduction to Stochastic Processes. No information is available regarding student advisement or external research grants. He contributes to the academic community by sharing reproducible research code on GitHub. He leads and maintains research software repositories related to cointegration and panel FM-OLS estimation, promoting open science and computational reproducibility in econometrics.

• Biostatistics
• Statistical Methodology
• Medical Statistics
• +۳ مورد دیگر

Frank Schaarschmidt is a researcher affiliated with the Department of Biostatistics and the Institute of Cell Biology and Biophysics at Leibniz University Hannover within the Faculty of Natural Sciences. His work focuses on developing and applying advanced statistical methodologies across diverse fields such as biomedicine, ecology, agriculture, and molecular biology. Key research interests include statistical inference techniques, prediction modeling for overdispersed data, and the design of experiments. He has contributed extensively to methodologies for simultaneous confidence intervals, multiple testing adjustments, and applications in clinical trials and environmental risk assessment. Collaborations span disciplines from cell biology to soil science, reflecting a strong interdisciplinary approach. Publications highlight his expertise in statistical methods for medical monitoring, ecological modeling, and molecular biology. Notable contributions include work on hydrogel platforms for stem cell research and studies on pyrimidine metabolism in plants. His research often bridges theoretical statistical developments with practical applications in health sciences and environmental sustainability. Frank Schaarschmidt holds the title of PD Dr. (Privatdozent) and has been actively involved in academic management within the Department of Biostatistics.

Wolf-Dieter Richterمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Multivariate Analysis
• +۷ مورد دیگر

Wolf-Dieter Richter is a Professor of Probability Theory and Mathematical Statistics at the University of Rostock's Institute of Mathematics. His research focuses on advanced probability theory, statistical distributions, and geometric probability. He has supervised numerous PhD, Master's, and diploma students, with notable advisees including Jana Davids (1992), Uta Boldt (1993), and Klaus Müller (2017). His teaching includes courses on stochastic processes, multivariate statistical methods, and specialized seminars on topics like integral asymptotics and geometric statistics. Research interests: Geometric representations of distributions, generalized complex numbers, large deviation probabilities, and non-Euclidean statistical models. Supervised over 20 students in areas like statistical analysis, stochastic modeling, and geometric probability. Active in curriculum development for mathematics programs, including teacher training and applied statistics. Key contributions include work on (p₁,…,p_k)-spherical distributions, star-shaped distributions, and geometric interpretations of statistical concepts. His research bridges algebraic structures with probabilistic methods, often involving high-dimensional and non-Euclidean frameworks. Recent articles explore complex numbers in higher dimensions, hyperbolic number systems, and advanced distribution theory. He has pioneered methods for analyzing skewed elliptically contoured distributions and their geometric properties.

Frank Konietschkeمشاهده پروفایل

استاد

• Medical biometrics
• Resampling methods
• Nonparametric statistics
• +۴ مورد دیگر

Prof. Dr. Frank Konietschke is Acting Director of the Institute of Biometry and Clinical Epidemiology at Charité – University Medicine Berlin and the Berlin Institute of Health (BIH) . He leads the Working Group Statistical Methods of Translation and Early Clinical Trials . Professor for Statistical Methods of Translation and Early Clinical Trials since 2019 Previous Tenure Track Professor at University of Texas at Dallas (2014-2018) Holds doctorate in Mathematics from Georg-August University Göttingen (2009) Research Expertise: Specializes in nonparametric statistical methods with focus on: Resampling techniques and ranking methods for factorial designs Diagnostic studies and longitudinal data analysis High-dimensional data and statistical translation Applications in cardiology, neurology, and radiology Scientific Contributions: • 121 publications including methodological advances in: Nonparametric Behrens-Fisher problem Group sequential designs Multiple contrast tests under heteroscedasticity • Developed R packages for statistical analysis (rankFD, nparLD) Awards and Recognition: Betty and Gifford Johnson Award (2017) Julia Williams van Ness Award (2017) Gustav-Adolf Lienert Award (2012)

• Vegetation dynamics
• Permafrost ecosystems
• Climate change impacts
• +۲ مورد دیگر

Dr. Stefan Kruse is a Research Fellow at the Alfred Wegener Institute's Polar Terrestrial Environmental Systems group. His research focuses on vegetation development in extreme environments, particularly at the Siberian treeline, and investigates ecosystem responses to climate change using field studies, remote sensing, and computational modeling. His primary research interests include: Vegetation dynamics at ecological boundaries Permafrost-ecosystem interactions Population genetics of boreal species Individual-based ecological modeling Dr. Kruse employs advanced techniques including UAV-based photogrammetry, sedimentary DNA analysis, and satellite data interpretation to study above-ground biomass changes and long-term vegetation patterns across Arctic regions.

• Statistical Inverse Problems
• Applied Statistics in Science and Technology
• Medical Imaging Reconstruction
• +۴ مورد دیگر

Nicolai Bissantz is a Senior Lecturer in the Department of Stochastics at Ruhr University Bochum's Faculty of Mathematics. His research focuses on statistical inverse problems, applied statistics in science and technology, and medical imaging reconstruction. He contributes to interdisciplinary projects in cybersecurity, astrophysics, and biophotonics. PhD supervision: Advises on statistical methods in interdisciplinary applications. Grants: Collaborates on BMBF-funded projects improving diagnostic precision in medical imaging. Recent work includes a 2024 Distinguished Paper Award for advancing software fuzzing evaluation methodologies. His statistical methods address challenges in internet security, medical imaging, and astrophysical modeling.

Jörg Breitungمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Panel Data Analysis
• +۳ مورد دیگر

Jörg Breitung is a Professor of Econometrics and Statistics at the Institute of Econometrics and Statistics within the Faculty of Management, Economics and Social Sciences (WiSo Faculty) at the University of Cologne since 2014. He also serves as a Research Professor of the German Bundesbank in Frankfurt since 2002. Research Focus: Panel Data Analysis Time Series Analysis Forecasting Financial Econometrics Scientific Contributions: Developed advanced GMM estimators for spatial regression models Innovative approaches for assessing causality in frequency domains Created robust tests for slope homogeneity in panel data Pioneered methods for serial correlation testing in fixed effects models Contributed to nonlinear panel data modeling and bootstrap techniques Honors and Editorial Roles: Associate Editor of International Journal of Forecasting (2019-) Associate Editor of Journal of Business and Economic Statistics (2017-) Associate Editor of Econometric Reviews (2014-) Contributed to leading journals like Econometrica and Journal of Econometrics

Christopher Adolphمشاهده پروفایل

استاد

• Comparative Politics
• Computational Methods
• Data Visualization
• +۸ مورد دیگر

Christopher Adolph is a Professor of Political Science at the University of Washington, Seattle, with additional appointments as Adjunct Professor of Statistics and Associate Director of the Center for Statistics and the Social Sciences. His research spans comparative politics, political economy, and political methodology, with a specialization in data visualization and statistical analysis. His substantive research interests include the politics of monetary policy, bureaucratic power, budget trade-offs, domestic impact of international trade, and comparative study of institutions governing health. He has made significant contributions to the visual display of scientific information, particularly illustrating findings from statistical models. His methodological expertise focuses on statistical inference for data with logical bounds, including political rank, compositional data, and ecological inference. Adolph's recent research has focused extensively on policy responses to the COVID-19 pandemic across US states, where he serves as faculty lead for a team collecting and analyzing data on state-level social distancing policies. His publications on pandemic politics have examined partisan patterns in social distancing mandates, mask mandates, and the easing of restrictions. International Political Science Association's Charles H. Levine Prize for best contribution in comparative public policy and administration Ranked 1st globally among political scientists for 'recent impact' (2025) Ranked 3rd globally for overall impact (2025) Former Robert Wood Johnson Scholar in Health Policy Research As an expert witness, he has consulted on statistical methods for contested elections and provides expertise on statistical methodology and data visualization. His book 'Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality' (Cambridge University Press, 2013) received significant recognition in the field.

Ryan Renslowمشاهده پروفایل

دانشیار

• Metabolomics
• Chemical Biology
• Exposure Science
• +۴ مورد دیگر

Ryan Renslow serves as a Chemical Engineer at Pacific Northwest National Laboratory (PNNL) and holds a Research Associate Professor position at Washington State University's Gene and Linda Voiland School of Chemical Engineering and Bioengineering. His interdisciplinary work bridges computational modeling, advanced imaging, and experimental biology to address complex challenges in metabolomics and microbial systems. Education BS in Chemical Engineering, Washington State University MS in Chemical Engineering, Washington State University PhD in Chemical Engineering, Washington State University Linus Pauling Distinguished Postdoctoral Fellowship, Pacific Northwest National Laboratory Renslow's research centers on identifying novel metabolites in complex biological samples, deciphering microbial community structure-function relationships, and understanding emergent properties in multispecies systems. He employs computational mathematics, machine learning, and high-resolution imaging techniques including mass spectrometry and nuclear magnetic resonance. His work spans diverse applications from human health diagnostics and bioenergy production to ecological monitoring and national defense solutions, with particular emphasis on biofilm dynamics and metabolite characterization in challenging environments. Analysis of his recent publications reveals a strong trend toward integrating ion mobility spectrometry with computational modeling for metabolite identification, developing in silico libraries for small molecule annotation, and applying machine vision to biological systems. His work consistently demonstrates cross-cutting applications across energy, environmental science, and biomedical research through sophisticated data analysis frameworks. Scientific Awards: No awards listed in available documentation. Renslow's collaborative research program involves extensive partnerships across PNNL's Environmental Molecular Sciences Laboratory and Washington State University. His work receives institutional support through DOE-funded initiatives focused on chemical biology and exposure science, with emphasis on developing advanced analytical capabilities for complex sample analysis. While specific grant details aren't provided, his publication record indicates consistent funding for interdisciplinary projects combining experimental and computational approaches. At PNNL, Renslow contributes to the Chemical Biology and Exposure Science group within the Biological Sciences division. He leverages specialized facilities including high-field mass spectrometers, nuclear magnetic resonance microimaging systems, and biofilm reactors to investigate microbial community dynamics. His research team integrates expertise from chemical engineering, microbiology, and computational science to develop novel approaches for characterizing complex biological systems at multiple scales.

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.