جست‌وجوی استاد و پژوهشگر

• Migration Studies
• Computational Demography
• Digital Trace Data Analysis
• +۳ مورد دیگر

Tom Theile is a Research Scientist at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, affiliated with the Laboratory of Migration and Mobility and the Laboratory of Population Dynamics and Sustainable Well-Being. His work focuses on computational approaches to migration studies using large-scale digital trace data from platforms like LinkedIn and Twitter. His research spans migration studies, computational demography, and gender analysis, with specialization in scholarly mobility, professional migration patterns, and methodological innovation through digital trace data. He develops computational tools like the rsocsim R package for demographic microsimulation and leverages unconventional data sources to address traditional demographic questions at unprecedented scales. Analysis of his publications reveals consistent focus on international migration of high-skilled workers, with particular emphasis on gender disparities, economic development impacts, and methodological advances using LinkedIn and bibliometric data. His work demonstrates how digital footprints enable new migration flow estimation techniques and granular analysis of professional mobility. No scientific awards were mentioned in the provided text. The available information does not indicate any student supervision or grant management activities. At MPIDR, Theile contributes to the Laboratory of Migration and Mobility and Laboratory of Population Dynamics and Sustainable Well-Being through three major projects: Migration and Mobility of Scholars, Scholarly Migration Database, and Studying International Migration of High-Skilled Professionals using Digital Trace Data. His work involves close collaboration with Emilio Zagheni's Digital and Computational Demography group and international partners across demography and data science fields.

Antonia Zapfمشاهده پروفایل

استاد

• Statistical Methods for Diagnostic Studies
• Sample Size Calculation
• Meta-analysis
• +۲ مورد دیگر

Prof. Dr. Antonia Zapf is a Professor at the Department of Medical Biometry and Epidemiology within the School of Medicine at the University of Hamburg. Her research focuses on advanced statistical methodologies for diagnostic studies, sample size calculation, and meta-analyses in clinical and epidemiological contexts. Her primary research interests include Statistical Methods for Diagnostic Studies , Sample Size Calculation , and Meta-analysis . She has developed innovative approaches for handling missing data in diagnostic accuracy studies, created seamless designs for diagnostic test evaluation, and contributed to frameworks for diagnostic development during infectious disease outbreaks. Her work bridges biostatistics with practical clinical applications across cardiology, oncology, mental health, and rare diseases. Prof. Zapf leads multiple major research projects including EpiAdaptDiag (adaptive designs for diagnostic studies), ClusterDiag (evaluation of diagnostic tests with clustered data), and FlexDiag (flexible designs for diagnostic studies). Her publications demonstrate consistent contributions to methodological advancements in clinical epidemiology and biostatistics. Her research collaborations span multiple clinical domains with significant work in cardiovascular research (EAST-AFNET 4, NOAH-AFNET 6), mental health (COMET Study, i2TransHealth), and rare diseases (CARE-FAM-NET). She serves as principal investigator for numerous DFG-funded projects and international clinical trials.

• Psychometrics
• Structural Equation Modeling
• Bayesian Statistics
• +۵ مورد دیگر

Prof. Dr. Holger Brandt serves as Professor of Psychometrics at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, Eberhard Karls University of Tübingen since August 2021. Previously, he held Assistant Professor positions at the University of Zurich (2019-2021) and University of Kansas (2016-2019), following a postdoctoral fellowship at Tübingen's Hector Institute for Empirical Educational Research (2013-2016). His educational background includes a PhD (Promotion) from Goethe University Frankfurt's Institute of Psychology in 2013. Brandt's research pioneers advanced methodological frameworks at the intersection of psychometrics, statistics, and machine learning. He specializes in developing dynamic models for intensive longitudinal data, Bayesian estimation techniques, causal mediator analysis, and identification of inattentive response behaviors in surveys. His work rigorously addresses challenges in measurement invariance, structural equation modeling, and handling complex dependencies in social science data. Analysis of his recent publications reveals a dominant focus on Bayesian approaches for latent variable modeling, particularly spike-and-slab priors and latent class methods. His research consistently targets data quality issues in survey methodology while advancing causal inference techniques that relax traditional no-unmeasured-confounder assumptions. Applications span educational research, psychological assessment, and therapeutic alliance dynamics. As a core member of Tübingen's Methods Center, Brandt provides critical methodological infrastructure for social science research across the university, supporting researchers through statistical consulting and advanced methodology development.

• Quantum Information
• Quantum Computing
• Mathematical Optimization
• +۴ مورد دیگر

Prof. Dr. Martin Kliesch is a faculty member at Hamburg University of Technology (TUHH), where he leads research in Quantum Inspired and Quantum Optimization within the Faculty of Electrical Engineering, Computer Science and Mathematics. His work bridges theoretical informatics and quantum technologies, focusing on optimization methods derived from or inspired by quantum mechanics. His research interests lie primarily in quantum information theory , quantum computing , and mathematical optimization . These areas integrate theoretical physics, computer science, and applied mathematics to develop novel algorithms and computational frameworks. His work has implications for both foundational science and practical applications in quantum technology. The absence of listed publications in the current data does not reflect on his research output but indicates missing metadata. However, his active affiliation, professional identifiers (ORCID, Scopus), and leadership of a dedicated research group suggest ongoing scholarly contributions. There are no listed scientific awards available in the provided text. There is no public information on student advising or research grants in the current dataset. However, as a full professor leading a specialized research group, it is likely that he supervises graduate students and participates in funded research projects, though specifics are not disclosed here. He is associated with the research group Quantum Inspired and Quantum Optimization at TUHH, which focuses on developing classical and quantum methods for solving complex optimization problems. The group operates at the intersection of theoretical computer science, quantum information, and applied mathematics, contributing to both academic and industrial advancements in computation.

• Machine Learning
• Molecular Medicine
• Electronic Health Records Mining
• +۳ مورد دیگر

Vida Abedi is an interdisciplinary researcher with expertise spanning biochemistry, molecular/genetic medicine, and engineering. Their work integrates computational methods with clinical applications, focusing on machine learning, multi-resolution data analytics, and electronic health record mining.

Mette Louise Bergمشاهده پروفایل

مدرس

• Anthropology of Migration
• Diversity Studies
• Transnationalism
• +۴ مورد دیگر

Mette Louise Berg is a Lecturer in Anthropology of Migration at the University of Oxford, affiliated with the Centre on Migration, Policy and Society (COMPAS). She holds a Masters degree from Copenhagen and a DPhil from St Antony's College, Oxford. Her research focuses on: Anthropology of migration and diaspora studies Diversity as a conceptual framework replacing multiculturalism Transnational dimensions of migration and urban diversity Visual methods in social research Combining qualitative and quantitative approaches to studying diversity Dr. Berg approaches diversity research from her background in transnationalism and diaspora studies, arguing against purely neighborhood-based analyses. She emphasizes how visual methods can capture non-narrative aspects of social life like gestures and visual textures in diverse urban environments that traditional textual analysis misses. Her work critically examines diversity's relationship with neoliberal individualism and questions whether it enables political mobilization or encourages depoliticization. Currently, she collaborates with COMPAS colleagues on London's Elephant & Castle area, analyzing 2011 UK census data while documenting urban transformation through professional photography. This research examines intersections between diversity markers (ethnicity, language, migration trajectory) and social variables like deprivation and occupational status in rapidly changing urban spaces. She has edited a special issue of Identities journal (2013) with Ben Gidley and Nando Sigona, and co-organized Oxford conferences on Ethnography, Diversity and Urban Space, advocating for more globally inclusive and historically grounded diversity research beyond current Eurocentric tendencies.

Bobbie-Jo Webb-Robertsonمشاهده پروفایل

استاد بالینی

• Statistics
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Bobbie-Jo Webb-Robertson serves as Division Director and Chief Scientist of computational biology in the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). She holds multiple academic appointments including Clinical Volunteer Professor at the University of Colorado Anschutz Medical Campus, Courtesy Assistant Professor at the University of Florida, and Clinical Associate Professor at Oregon Health and Sciences University. Dr. Webb-Robertson earned her PhD in Engineering Systems from Rensselaer Polytechnic Institute in 2002, an ME in Statistics from the same institution in 2000, and a BA in Mathematics from Oregon State University in 1997. With over 20 years of experience in statistics and data science, she has established herself as a leader in computational biology with an h-index of 30 and more than 100 publications. Her research focuses on developing and applying advanced statistical and machine learning methods to address challenges associated with large and complex omics data. She specializes in mass spectrometry-based omics data analysis, statistical data integration, predictive modeling, and biomarker discovery. Her work has significant applications in biomedical research, particularly in understanding type 1 diabetes through multi-omics approaches. Dr. Webb-Robertson's recent publications demonstrate a strong trajectory in type 1 diabetes biomarker discovery, multi-omics data integration, and the application of machine learning to biological problems. Her work spans proteomics, metabolomics, and transcriptomics with a focus on developing computational tools that can integrate multiple data types for improved biological insights. Chauncey and Doris Starr Graduate Fellowship, 1997 GE Future Faculty Scholarship, 1999-2000 National Science Foundation Program in Mathematics and Molecular Biology Fellowship, 1999-2001 HPC Analytics challenge in biology first place team member, 2007 Spirit of nPOD award from the Network for Pancreatic Organ Donors with Diabetes Dr. Webb-Robertson serves in various editorial and advisory roles including Executive Editor for the Journal of Proteomics and Genomics Research, Review Editor for Frontiers in Artificial Intelligence, Scientific Advisory Board Member for the Juvenile Diabetes Research Fund IBM Machine Learning Project, and member of the American Statistical Association. She actively contributes to the scientific community through these service roles while maintaining a productive research program focused on computational methods for biological data analysis. Her laboratory develops computational tools for multi-omics data analysis with particular emphasis on applications in type 1 diabetes research. She leads a team that integrates expertise in statistics, machine learning, and biological domain knowledge to tackle complex biomedical problems, with a focus on biomarker discovery and early disease prediction.