جست‌وجوی استاد و پژوهشگر

Johannes Pohlمشاهده پروفایل

پژوهشگر

• Wind Turbine Noise Effects
• Environmental Psychology
• Biopsychology
• +۸ مورد دیگر

Johannes Pohl is a researcher at the Martin Luther University Halle-Wittenberg , affiliated with the Faculty of Philosophy I and the Department of Clinical Psychology . Since October 2017, he has focused on environmental psychology, particularly the stress effects of wind turbines and social acceptance of renewable energy . His work spans interdisciplinary collaborations with institutions like the Karlsruhe Institute of Technology and the University of Bielefeld. Education: Dr. phil. in Psychology (1993, Julius-Maximilians-University Würzburg) Diploma in Psychology (1985, Heinrich Heine University Düsseldorf) Pohl's research explores: Environmental stressors from wind turbines (noise, shadow flicker, obstruction lighting) Biopsychology of stress hormones, emotions, and interoception Neuropsychological diagnostics in schizophrenia and depression Religiosity and spirituality in health contexts Renewable energy acceptance across cultural and geographic boundaries His recent publications focus on community responses to wind turbine noise and visual pollution , employing multivariate data analysis and cross-country comparisons . He has contributed to frameworks like the Integrated Acceptance Model and evaluated interventions for noise mitigation. Pohl's work also includes pharmacopsychology studies on stress modulation and botulinum toxin therapy effectiveness in movement disorders. Selected projects include: BMU-funded studies on wind turbine obstruction lighting impacts KfW Bank wind turbine shadow flicker analysis Multi-institutional TremAc project on inland wind turbine emissions Wind turbine noise annoyance modeling with acoustic and meteorological data Participatory approaches for offshore wind park planning Pohl has extensive experience in environmental and health psychology , having led research at the University of Kiel (1992-2000) and Würzburg (1985-1991). His Health and Environmental Psychology Working Group (since 2007) integrates psychological and physiological measurements to assess renewable energy impacts.

Elena A. Eroshevaمشاهده پروفایل

استاد

• Statistical Methodology
• Discrete Data Analysis
• Latent Variable Models
• +۷ مورد دیگر

Elena A. Erosheva is a Professor of Statistics at the University of Washington with a joint appointment in the School of Social Work. She serves as Associate Director of the Center for Statistics and the Social Sciences (CSSS) and is an Associate Editor for the Journal of the American Statistical Association and the Annals of Applied Statistics. Education PhD in Statistics from Carnegie Mellon University (2002) Elena Erosheva is a statistician whose research focuses on the development and application of modern statistical methods for addressing complex substantive questions in the social, medical and health sciences. Her ongoing methodological research focuses on latent variable and mixed membership models, multivariate and longitudinal data analysis, and text and network analysis. Her work has important applications in understanding disability in elderly populations, criminal desistance patterns, and scientific collaboration networks. Dr. Erosheva's research bridges statistical theory with practical applications, making significant contributions to both methodology and substantive fields. Analysis of Dr. Erosheva's recent publications reveals a strong focus on mixed membership models and their applications across diverse domains. Her work spans from theoretical developments in statistical methodology (such as variational inference techniques and bootstrap methods) to applied research in health sciences (particularly aging and disability) and social sciences (including criminology and scientific collaboration patterns). A notable trend in her recent work is the application of advanced statistical techniques to understand gender dynamics in scholarly publishing and to improve peer review processes. Scientific Awards 2013 Mitchell Prize from the International Society of Bayesian Analysis First Prize from the National Institutes of Health's Peer Review Challenge Dr. Erosheva has successfully advised numerous graduate students across multiple departments including Statistics, Sociology, Health Services, and Educational Psychology. Her research has been supported by grants from the National Institute on Aging (funding her work on operational definition of disability in the National Long Term Care Survey) and NIH (funding the COAP - Cognitive Outcomes with Advanced Psychometrics project). She has collaborated extensively with researchers across disciplines, demonstrating the interdisciplinary nature of her work. Dr. Erosheva is actively involved in the Center for Statistics and the Social Sciences (CSSS) at the University of Washington, where she serves as Associate Director. She leads research projects focused on measuring gender-based homophily in scientific collaborations, modeling life course transitions, developing partial mastery cognitive diagnosis models, and identifying commensuration bias in grant review.

• Multivariate research methods
• Longitudinal data analysis
• Inter- and intra-individual variability in cognitive and affective functioning
• +۱ مورد دیگر

Prof. Dr. Manuel Völkle is a full Professor at the Institute of Psychology, Humboldt University, within the Faculty of Life Sciences. He serves as Deputy Director of the Executive Directorate at the Institute of Psychology, based at Rudower Chaussee 18, Room 3'103. His methodological expertise and contact information are available through the institute's website and physical postal address at Unter den Linden 6, 10099 Berlin. Diploma in Psychology (Dipl. Psych.), 2004, University of Mannheim Dr. rer. soc. (PhD) in Psychology, 2008, University of Mannheim Völkle specializes in advanced psychological methodology and longitudinal data analysis, focusing on: Multivariate research techniques Modeling individual and group-level variability in cognitive/affective processes Evaluation research frameworks He actively provides methodological consultation through the Methods-berlin platform, with contact available via email, phone (030 2093-98703), or in-person at his office.

Torben Kuhlenkasperمشاهده پروفایل

استاد

• Econometrics
• Statistics
• Quantitative Methods
• +۴ مورد دیگر

Prof. Dr. Torben Kuhlenkasper is a Professor of Quantitative Methods in Economics at Pforzheim University's School of Business, leading the Quantitative Methods department. He holds a doctorate in Statistics and Econometrics from the University of Bielefeld (2011) and has extensive academic and professional experience across institutions like Goethe University Frankfurt and the Hamburg Institute of International Economics (HWWI). His research focuses on semi-parametric econometric methods applied to economic and business problems, including nonlinear economic interactions and policy analysis. Education: PhD in Statistics & Econometrics, Universität Bielefeld (2011) Diplom in Economics (2007) and Business Administration (2006), Universität Bielefeld Research Interests span Econometrics , Quantitative Methods , Regression Analysis , and Data Science . He develops advanced statistical techniques to analyze complex economic phenomena such as presidential popularity dynamics, fiscal policy impacts, and immigration patterns. His work has been published in journals like Journal of Applied Econometrics and Computers in Human Behavior . Notable Awards include teaching excellence awards from Pforzheim University (2016) and recognition for outstanding teaching in Goethe University's Master program (2013-2014). He has authored three influential textbooks on statistical methods using R. Academic Service includes editorial roles ( Review of Economics ), conference organization (Deutsche Statistische Gesellschaft), and media engagements (NDR Info Interview on presidential popularity). He also leads the interdisciplinary team at KuCADU , focusing on academic consulting and innovation in quantitative methods.

• Agricultural Economics
• Productivity Analysis
• Technical Efficiency
• +۴ مورد دیگر

Eva Scheichel , Senior Lecturer at the Institute of Production and Logistics within the Department for Economics and Social Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU), specializes in Agricultural Economics with a focus on productivity analysis, technical efficiency, and eco-efficiency in dairy and crop farming systems. Her work applies advanced methodologies like Data Envelopment Analysis (DEA) and stochastic frontier analysis to investigate efficiency determinants across Austrian agricultural sectors. Education MSc in Economics & Social Sciences, Bocconi University Milan (2007-2009) BSc in Agricultural Sciences, University of Natural Resources and Life Sciences, Vienna (2014-2017) BA in Business Consultancy International, University of Applied Sciences Wiener Neustadt (2004-2007) Research Interests Productivity dynamics in dairy farming Technical vs. eco-efficiency trade-offs Land use efficiency and rental impacts Scale economies in agricultural production Wine grape grower inefficiency analysis Policy implications for agricultural sustainability Publication Trends : Her 2018-2021 work spans dairy productivity analysis, land use efficiency, and eco-technical efficiency linkages, with geographic focus on Austria and methodological emphasis on DEA and longitudinal modeling. Research addresses both micro-level farm operations and macro-level policy impacts.

Dennis Doblerمشاهده پروفایل

استادیار

• Resampling methods
• Survival and event time analysis
• Multivariate analysis
• +۱ مورد دیگر

Dennis Dobler is an Assistant Professor in Statistics at TU Dortmund University, appointed in September 2023. His academic foundation includes a doctorate from Ulm University (2016) and dual bachelor's degrees in Mathematics and Computer Science from Heinrich-Heine-Universität Düsseldorf (2011). Research expertise centers on Resampling methods, Survival analysis, Multivariate techniques, and Asymptotic statistics, with applications spanning clinical trials, risk modeling, and nonparametric inference. His methodological work frequently addresses censored data challenges in survival contexts. Prior roles include Assistant Professorship at Vrije Universiteit Amsterdam, postdoctoral research at Ulm University, and a research stay at the University of Copenhagen. Current publications reflect sustained focus on survival analysis innovations, particularly in competing risks and resampling methodologies.

Sarah Genonمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Science
• Machine Learning in Healthcare
• +۳ مورد دیگر

Prof. Dr. Sarah Genon is a Professor and Group Leader at the Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) of Forschungszentrum Jülich . Her research focuses on understanding brain-behavior relationships through neuroimaging, machine learning, and large-scale data analysis. She leads interdisciplinary projects investigating hippocampal organization, predictive modeling of mental health outcomes, and replicability in neuroscience. Key areas include: Brain structural health prediction using exposome data Hippocampal morphological networks linked to self-regulation Generalizable machine learning approaches for depression and Alzheimer's biomarkers Cross-cultural generalization failures in behavioral prediction models She emphasizes open science practices, leading initiatives like the Individual Brain Charting dataset and promoting diversity in neuroscience research. Her work bridges basic research with clinical applications, particularly in psychiatry and neurodegenerative diseases. Recent efforts include: Developing connectivity-based brain atlases Studying sleep-cardiometabolic brain interactions Addressing gender biases in neuroscience methodologies Her team collaborates internationally to advance reproducible brain-behavior mapping and improve translational neuroscience outcomes.

Alexandra Reichenbachمشاهده پروفایل

استاد پژوهشی

• Cognitive Neuroscience
• Neuroinformatics
• Artificial Intelligence in Mental Health
• +۶ مورد دیگر

Prof. Dr. rer. nat. Alexandra Reichenbach is a Research Professor for Neuroinformatics at Heilbronn University of Applied Sciences and an Associate Professor at Heidelberg University's Medical Faculty . As Director of the Center for Machine Learning (ZML) , she leads the "Motor Control & Artificial Intelligence in Mental Health" research group and chairs HHN's Ethics Board. Her research bridges cognitive neuroscience and artificial intelligence , focusing on: Human sensorimotor function and its neural basis AI-driven biomarker development for psychiatric disorders (depression, schizophrenia) EEG and fMRI-TMS multimodal neuroimaging Robot-assisted motor rehabilitation technologies Neuroethics of AI in clinical diagnostics Recent work includes GAN-based schizophrenia classification , EEG biomarker optimization , and Alzheimer's disease stratification models . She advocates for interdisciplinary AI education , co-developing HHN's Applied AI bachelor's program and planning its master's/doctoral expansion. Key collaborations include: Heidelberg Medical Faculty Human Robotics Group at Imperial College National Hospital for Neurology and Neurosurgery Medical Informatics Master Seminar (Heidelberg/HHN partnership) Her lab (ZML) integrates virtual AI experimentation environments for both research and public education, including interactive AI & Society installations.

• Stochastic Modeling
• Statistical Inference for Dynamic Processes
• Bayesian Statistics
• +۵ مورد دیگر

Prof. Dr. Christiane Fuchs is a full professor at the Faculty of Economics of Bielefeld University and heads the Data Science Group . She is also leading the Biostatistics Research Group and Core Facility Statistical Consulting at Helmholtz Munich . Her academic affiliations include the Bielefeld Graduate School of Economics and Management and the Bielefeld Center for Data Science (BiCDaS) . Education : MSc in Computational Modeling, Brunel University West London (2003) Diploma in Mathematics with Computer Science, University of Hanover (2005) PhD in Statistics, Ludwig Maximilian University of Munich (2010) Research interests span stochastic modeling , Bayesian inference , uncertainty quantification , and statistical applications in economics, medicine, and epidemiology. She specializes in diffusion processes , high-dimensional data analysis , and integrated statistical methods for cross-domain data (genomics, clinical, environmental). Recent publications focus on AI-driven clinical decision support systems , spatial epidemiology , fractional diffusion modeling , and statistical serology validation . Her work bridges methodological innovation in Bayesian statistics with real-world applications in infectious disease dynamics and hematological malignancies . Principal investigator in third-party funded projects from DFG , BMBF , NIH , and Helmholtz Association , including the UQ Consortium (2019-2024) and KoCo19 prospective COVID-19 cohort (2020-2024). She has developed statistical software packages like stochprofML and adaSC3 , and contributed to network-regularized regression methods. As Vice Rector for Research and Networking at Bielefeld University since 2023, she drives institutional research strategy while maintaining active roles in scientific societies including the International Society for Bayesian Analysis and Deutsche Statistische Gesellschaft .

• Developmental Psychobiology
• Stress and Child Abuse Effects
• Multivariate Longitudinal Data Analysis
• +۳ مورد دیگر

Dr. Claudia Lazarides is a postdoctoral researcher at the Professorship for Psychological Methodology , Institute of Psychology, Humboldt University of Berlin since 2023. She earned her PhD in 2021 with a thesis on maternal stress during pregnancy and its biological impacts, supervised by Prof. Voelkle and Prof. Entringer. Her research focuses on Multivariate analysis of intensive longitudinal data Ecological Momentary Assessment (EMA) methods Early stress and child abuse experiences Developmental psychobiology Previously, she worked as a postdoctoral fellow (2020-2022) and research associate (2016-2020) at Charité – Universitätsmedizin Berlin. She completed a master’s degree in Clinical Psychology and Psychotherapy (2016) and bachelor’s in Psychology (2013).

• Copula regression and regularization
• Sports statistics
• Mixed models in medicine
• +۳ مورد دیگر

Dr. Hendrik van der Wurp was a member of the Faculty of Statistics at the Technical University of Dortmund , Department of Statistics. His academic focus included Copula regression with regularization , Sports statistics , and Mixed models in medicine . He contributed to statistical methodologies in competitive settings, particularly in sports analytics and healthcare applications. His research emphasized count data analysis, penalized regression techniques, and applications in neurology and sports outcomes. Administrative Roles: While at TU Dortmund, he held key pandemic-related roles including Pandemic Officer, Safety Officer, and Risk Management Officer, reflecting his institutional responsibilities during the 2020–2023 period. Teaching: Taught courses such as Computational Statistics, Probability and Mathematical Statistics for Computer Scientists, and Descriptive Multivariate Statistics from 2016–2022. His teaching spanned statistical methods for data scientists, multivariate analysis, and sports-related statistical modeling. Research Trends: His articles spanned sports analytics (football/table tennis prediction), neurodegenerative disease modeling (Parkinson's/Alzheimer's), and methodological innovations in count data regression. Recent work highlighted applications of copula models and regularization techniques in competitive scenarios. Grants & Labs: While specific grants aren't listed, his research indicates involvement in interdisciplinary projects at the intersection of statistics, sports science, and biomedicine. No dedicated lab teams were explicitly mentioned in the provided texts.

Casey Paquolaمشاهده پروفایل

پژوهشگر

• Neurodevelopment
• Neuroimaging
• Computational Neuroscience
• +۳ مورد دیگر

Dr. Casey Paquola leads the Multiscale Neurodevelopment Team at the Institute of Neuroscience and Medicine (INM-7), Research Center Jülich. His research focuses on understanding neurodevelopmental processes through multiscale frameworks that integrate postmortem histology, transcriptomics, and in vivo neuroimaging (e.g., MRI, EEG). The team studies cytoarchitecture maturation, connectivity, and functional dynamics across early life to elucidate mechanisms of neurodevelopmental disorders. Their work emphasizes microstructure as a bridge between postmortem and in vivo data, identifying principles like large-scale cortical gradients. Research methods include microscopy, neuroimaging, and computational modeling to construct brain models. Key areas of study include the default mode network, epilepsy-related brain changes, and the interplay between genetics and brain structure. The team develops tools like BrainSpace and Micapipe for analyzing structural-functional relationships in neuroimaging datasets. Led initiatives: ENIGMA-Toolbox for transdiagnostic analysis, BrainStat for statistical neuroimaging analysis. Focus areas: Cortical development gradients, functional resilience, and translational neuroimaging. Collaborations: Multidisciplinary teams combining computational, clinical, and experimental neuroscience. Future work aims to advance precision medicine through personalized brain mapping and understanding neurodevelopmental disorders' multiscale origins.

Angela Carolloمشاهده پروفایل

پژوهشگر

• Demography
• Statistics
• Survival Analysis
• +۴ مورد دیگر

Angela Carollo is a Researcher at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, affiliated with the Laboratory of Fertility and Well-Being. Her work focuses on developing advanced statistical methodologies for demographic analysis, particularly in survival and event-history models with multiple time scales. Her research interests span demography, statistics, and population health, with emphasis on: Survival analysis and competing risks modeling Event-history frameworks with multidimensional time Fertility dynamics and family transitions Mortality patterns and health outcomes Statistical software development for demographic applications Carollo's publication record demonstrates consistent innovation in handling complex demographic data structures. Her recent work shows strong trends toward interdisciplinary collaboration (spanning statistics, gerontology, and public health) and methodological rigor in modeling time-dependent phenomena. Key contributions include the TwoTimeScales R package and novel approaches to hazard smoothing across multiple temporal dimensions, applied to critical demographic questions like partnership transitions and mortality prediction. No scientific awards were documented in the source material. Her collaborative research involves extensive work with international teams across Europe, though no formal student advising or grant management details were provided. Current projects include dissertation work on "Multiple Time Scales in Survival and Event-History Models" within the Laboratory of Fertility and Well-Being. Carollo operates within MPIDR's Laboratory of Fertility and Well-Being, which investigates how demographic processes like fertility and partnership transitions interact with individual well-being across the life course, leveraging advanced statistical techniques for population-level insights.

Elena A. Eroshevaمشاهده پروفایل

استاد

• Statistical methodology for social sciences
• Bayesian inference
• Discrete data analysis
• +۷ مورد دیگر

Elena A. Erosheva is a Professor of Statistics and Social Work at the University of Washington , where she serves as the Director of the Center for Statistics and the Social Sciences (CSSS). She is also affiliated with the School of Social Work and has made significant contributions to statistical methodology for the social, behavioral, and health sciences. Professor, University of Washington (Statistics & Social Work) Director, Center for Statistics and the Social Sciences (CSSS) Joint appointment with School of Social Work Editorial board member for top journals including Annals of Applied Statistics and Psychometrika Her research focuses on: Bayesian inference and hierarchical models Mixed membership and latent variable models for complex data Statistical approaches for longitudinal and multivariate categorical data Applications in social sciences, health, and medical research Gender homophily in academic collaborations Measurement of chronic disability and cognitive outcomes Recent publications analyze: Statistical learning models for rankings and scores Gender-based homophily in scholarly collaborations Variational inference techniques Mixed outcome latent variable models for neuroscience Methodology for detecting bias in peer review Major awards include: 2021 Fellow, American Statistical Association 2024 Elected Member, International Statistical Institute 2013 Mitchell Prize for Bayesian contributions 2014 America Competes Act Challenge winner She has advised numerous research projects and served as a PI for NIH-funded work on cognitive outcomes with advanced psychometrics.

• Gambling Research
• Consumer Behavior
• Data Science
• +۳ مورد دیگر

Steffen Otterbach serves as Academic Senior Councilor (Akademischer Oberrat) and Director of the Gambling Research Center, affiliated with the Field of Household and Consumer Economics and the Institute for Health Care and Public Management. He teaches Consumer Behavior, Multivariate Data Analysis, and Sustainability & Change for the Winter Semester 2025/26. Research Interests: His work centers on gambling behavior in digital environments—including in-game elements and social media advertising—with strong emphasis on consumer decision-making, public health implications, and labor economics. He employs advanced methodologies like machine learning, large language models, and multivariate analysis to investigate behavioral patterns, regulatory impacts, and health outcomes. Publication Trends: Recent publications (2023-2024) demonstrate intense focus on gambling research using computational linguistics for youth perspectives and systematic reviews of advertising strategies. His interdisciplinary approach bridges computer science, psychology, and public policy while extending to cardiovascular health, workplace dynamics, and sustainability through data-driven social science. Scientific Awards: No awards documented in available sources. Advising and Grants: Student supervision and grant details not specified in source materials. Labs and Teams: Leads the Gambling Research Center (Forschungsstelle Glücksspiel) and contributes to the Field of Household and Consumer Economics, driving empirical studies on digital gambling ecosystems and consumer protection frameworks.