جستجوی استاد راهنمای دانشگاهی

• Compositional Data Analysis
• Probability Theory
• Multivariate Analysis
• +۵ مورد دیگر

Jakob Bergman serves as Senior Lecturer, Associate Professor, and Director of Studies at Lund University's Department of Statistics. His academic profile demonstrates significant expertise in compositional data analysis, where he studies vectors of proportions (compositions) that arise across diverse fields including geochemistry, household economics, and political science. His research focuses on developing innovative statistical methodologies, particularly his compositional loess method for smoothing compositional time series data. Bergman maintains active interdisciplinary collaborations with archaeologists like Mikael Larsson (on cereal grain analysis and early farming practices) and philosophers like Martin Jönsson (on post-hoc interventions and gender bias in research funding systems). Bergman's scholarly output reveals strong interdisciplinary trends, with recent publications bridging statistics with archaeology, political science, and philosophy. His work consistently addresses practical methodological challenges while maintaining theoretical rigor, particularly in analyzing party shares over time and archaeological specimen compositions. As an educator, Bergman teaches theoretical and applied statistics across undergraduate, advanced, and PhD programs, with special emphasis on sampling/survey research and regression analysis. His outreach extends to providing statistical expertise to numerous NGOs, authorities, and companies. His research contributes to UN Sustainable Development Goals related to quality education and gender equality. Bergman has led significant projects including 'Post-hoc Interventions' at Pufendorf IAS and 'Manure matters' investigating early farming practices through nitrogen analysis of archaeological crop assemblages.

• Control Charts
• Measurement Error
• Performance Measure
• +۵ مورد دیگر

Hamed Sabahno serves as a Senior Lecturer in the Department of Statistics at Lund University, Sweden. His academic profile is centered on advanced statistical methodologies with applications in engineering and quality control systems. His research spans statistical process control , speckle pattern analysis , and multivariate monitoring systems . Key focus areas include adaptive control charts, measurement error correction, and image-based displacement measurement techniques. His work bridges theoretical statistics with practical industrial applications, particularly in non-destructive testing and quality engineering. Recent publications demonstrate strong emphasis on Real-time process monitoring using optical methods Multivariate regression profile control Statistical refinement in speckle-based measurements Adaptive parameter systems for quality assurance His fingerprint analysis confirms dominance in Control Charts (100%) and Measurement Error (55%) research domains. Professional engagement includes ORCID registration (0000-0002-5618-887X) and active publication in high-impact journals including Scientific Reports and Quality and Reliability Engineering International . His research has attracted attention from 5 Mendeley readers and coverage in 1 news outlet.

Ali Mansourianمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +۷ مورد دیگر

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

Simon Reeseمشاهده پروفایل

مدرس ارشد

• Econometrics
• Panel Data Analysis
• Estimation Theory
• +۳ مورد دیگر

Simon Reese is an Associate Senior Lecturer in the Department of Economics at Lund University. His research focuses on econometrics, panel data models, and estimation theory, with expertise in probability theory and statistical methods. He has contributed to areas such as factor analysis in time series and panel data, detection of pervasive effects in large datasets, and the incidental parameters problem in econometric testing. His work has been published in journals like the Stata Journal and the Journal of Econometrics. Research interests include panel data modeling, factor estimation, and methodological advancements in econometric analysis. His recent studies explore topics such as school choice incentives and cross-sectional correlation testing in panel frameworks.

Fan Yang Wallentinمشاهده پروفایل

استاد

• Structural Equation Modeling
• Multivariate Statistical Analysis
• Psychometrics
• +۳ مورد دیگر

Fan Yang Wallentin is a Professor in Statistics at Uppsala University, Sweden, where she works in the Department of Statistics. She serves as the Coordinator for International Exchange Programs and has been responsible for the statistical consultant service at the department since 2009. Her academic career is deeply rooted at Uppsala University, where she earned her PhD in Statistics in 1997. Her educational background includes: PhD in Statistics from Uppsala University (1997) Professor Wallentin's research primarily focuses on structural equation modeling and multivariate statistical analysis, with particular applications in social and behavioral sciences. Her work bridges theoretical statistical advancements with practical applications across diverse fields including public health, psychology, economics, and education. She has made significant contributions to psychometrics, particularly in the development and validation of measurement instruments used in healthcare and education settings. Her methodology work addresses specification issues, robustness properties, and computational aspects of statistical models. Her publication record demonstrates an evolving research trajectory that has recently incorporated pressing global issues such as the statistical analysis of the COVID-19 pandemic, while maintaining her core expertise in structural equation modeling and related methodologies. Her work often involves cross-disciplinary collaborations, reflecting the broad applicability of her statistical expertise across healthcare, energy policy, and development economics. Among her notable recognitions: Arnberg Prize from the Swedish Royal Academy of Sciences (2000) for her PhD thesis "Non-linear structural equation models: Simulation studies of the Kenny-Judd model" Professor Wallentin has extensive experience providing statistical consultation to researchers in social and behavioral sciences. Her role as Coordinator for International Exchange Programs suggests active engagement in global academic networks. Her research has been applied in diverse contexts including pandemic response analysis, women's empowerment through microfinance, healthcare quality assessment, and educational statistics, demonstrating the versatility and impact of her methodological contributions.

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +۴ مورد دیگر

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

• Biostatistics
• Epidemiology
• Medical Statistics
• +۷ مورد دیگر

Matteo Bottai is Professor of Biostatistics and Head of the Department of Biostatistics at Karolinska Institutet, where he also serves as Scientific Director of the Biostatistics Core Facility. He holds additional academic appointments as Visiting Professor in the Department of Mathematical Statistics at Stockholm University and Adjunct Professor in the Department of Biostatistics at the University of South Carolina. Education: Doctor of Science, Harvard University, USA His research focuses on advanced biostatistical methods, particularly in quantile regression, modeling of bounded and censored outcomes, longitudinal data analysis, and methods for handling missing data in distributed networks. His work bridges theoretical statistics and practical applications in epidemiology, environmental medicine, and public health. He has contributed to methodological developments in Laplace regression, parametric quantile modeling, and optimal probability weighting. His recent publications span a wide range of topics, including statistical methodology, cancer outcomes, respiratory health, cardiovascular disease, and mental health interventions. The articles reflect a strong emphasis on population-based cohort studies, clinical trials, and the use of complex statistical models to derive meaningful clinical and public health insights. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis Effect of internet-delivered cognitive behavioral therapy in pediatric OCD and Tourette syndrome Risk of thromboembolic events after cancer surgery Impact of air pollution on infant lung function Statistical methods for missing data and quantile regression Matteo Bottai actively collaborates across disciplines, working with researchers in oncology, neurology, cardiology, psychiatry, and environmental health. His role as Scientific Director of the Biostatistics Core Facility underscores his leadership in supporting high-quality statistical analysis in biomedical research. He has been involved in numerous clinical trials and large-scale cohort studies, contributing statistical expertise to study design, analysis, and interpretation. He leads the Biostatistics Unit at the Institute of Environmental Medicine, Karolinska Institutet, which provides statistical support and fosters methodological research in environmental and occupational health.

Tatjana Pavlenkoمشاهده پروفایل

استاد

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +۴ مورد دیگر

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

Jan Helmdagمشاهده پروفایل

استادیار

• Social Policy
• Political Economy
• Welfare State Dynamics
• +۴ مورد دیگر

Jan Helmdag is an Assistant Professor at the Swedish Institute for Social Research (SOFI) and Managing Director of the Social Policy Indicators (SPIN) database . His work focuses on taxation, social policy design, and political economy , with particular emphasis on income inequality mitigation through institutional data analysis. SPIN's collaboration with the Democracy, Environment, Migration, Social Policy, Conflict, and Representation (DEMSCORE) infrastructure highlights his cross-domain engagement. Research Interests : Helmdag investigates how social policy architecture affects income inequality , notably through his Benefit Dualization Index (BDI) which quantifies welfare state disparities between labor market insiders and outsiders . His EU pension reform analysis reveals systemic prioritization of financial sustainability over benefit adequacy and gender modernization in socio-economic governance. Helmdag has conducted comparative studies across OECD countries on unemployment benefit dynamics and active labor market policies , demonstrating conditioned policy learning among welfare regimes . His 2024-26 FORTE-funded project on Swedish social rights dualization builds upon his extensive work in benefit replacement rate analysis and policy indicator development .

Bruna G. Palmمشاهده پروفایل

مدرس

• Statistical Modeling
• Signal Processing
• Remote Sensing
• +۴ مورد دیگر

Bruna G. Palm is a Lecturer at the Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), Sweden. She holds a BA (2014) and PhD (2020) in Statistics from Brazilian institutions and has held visiting and research fellow positions at BTH and the Aeronautics Institute of Technology (ITA). Education: BA in Statistics (2014), PhD in Statistics (2020) Current Role: Lecturer at BTH Collaborations: Saab AB, Sweden Her research bridges statistical modeling with remote sensing and signal processing , focusing on SAR image analysis , change detection , and machine learning applications. She has also contributed to environmental data forecasting and educational pedagogy in STEM. Key trends include Bayesian inference , autoregressive models , and Rayleigh distribution techniques for SAR imagery. Her work on resin composite masking extends into dentistry , highlighting interdisciplinary reach. She has no listed scientific awards but maintains active profiles on LinkedIn , Google Scholar , and ORCID .

• Machine Learning
• Deep Learning
• Software Engineering
• +۷ مورد دیگر

Sebastian Hönel is a postdoctoral researcher at Linnaeus University, affiliated with the Faculty of Technology and the Department of Computer Science and Media Technology. He is an active member of the Data Intensive Software Technologies and Applications (DISTA) research group and currently serves as a co-Principal Investigator in the project "In-line visual inspection using unsupervised learning" focused on manufacturing defect detection using machine learning techniques. Hönel completed his Doctoral Thesis in 2023 titled "Quantifying Process Quality: The Role of Effective Organizational Learning in Software Evolution" and earned his Licentiate Thesis in 2020 on "Efficient Automatic Change Detection in Software Maintenance and Evolutionary Processes," both from Linnaeus University. His educational background demonstrates a strong foundation in software engineering and data analysis. Hönel's research spans software engineering, machine learning, and data science. Initially focusing on applying Machine Learning and Deep Learning to software evolutionary processes and organizational learning, his current work emphasizes unsupervised and zero/few-shot learning techniques for industrial anomaly detection. He has particular expertise in Deep Density Estimation (especially Normalizing Flows) and Representation Learning, with applications in manufacturing quality assessment and software maintenance. His research interests include anomaly detection methodologies, architectural innovations in autoencoders, and methodological considerations for evaluation metrics in machine learning applications. An analysis of his publication record reveals a consistent focus on bridging software engineering with advanced machine learning techniques. His most recent work shows a strategic shift toward industrial applications of unsupervised learning, particularly in manufacturing defect detection, while maintaining his foundational work in software metrics and quality assessment. The publications demonstrate progression from theoretical software metrics to practical applications of deep learning in quality inspection systems. Hönel actively contributes to academic education by teaching (Deep) Machine Learning courses (4DV652, 4DV660, 4DV661) and previously served as a teaching assistant for agile product development courses (1DV508, 4DV611). His role as co-PI on the visual inspection project indicates successful research funding and leadership capabilities. While specific grant details aren't provided in the available information, his position suggests ongoing research support. As part of the DISTA research group, Hönel collaborates extensively with colleagues including Ericsson, Löwe, and Wingkvist on projects that combine software engineering with advanced data analysis techniques. His work environment supports interdisciplinary research at the intersection of computer science, software engineering, and machine learning applications, with particular emphasis on practical implementations in both software development contexts and manufacturing quality control systems.

• Functional Time Series
• Time Series Forecasting
• Actuarial Science
• +۴ مورد دیگر

Han Lin Shang is a Professor of Business Analytics at Macquarie University's Department of Actuarial Studies and Business Analytics , with a secondary affiliation as visiting scholar at the ESRC Centre for Population Change, University of Southampton. His career includes prior academic roles at Australian National University (Associate Professor, Senior Lecturer, Lecturer) and research positions at Monash University and University of Southampton. Ph.D. in Econometrics and Business Statistics (Monash University) First-class Honours in Statistics (La Trobe University) Undergraduate in Mathematics and Statistics (La Trobe University) Research Interests : Functional time series analysis, mortality forecasting, high-dimensional data modeling, and financial time series prediction. His work bridges statistical theory with applications in actuarial science, population studies, and climate risk assessment. Scientific Awards : Australian Research Council Future Fellowship Grants : AU$240,100,338 (ARC Future Fellowship, 2024-2028) AU$230,102,250 (ARC Discovery Project, 2023-2026) AU$170,102,468 (ARC Discovery Project, 2017-2022)

Aron Laxdal, Sveinn Þorgeirsson, Jose M Saavedraمشاهده پروفایل

پژوهشگر

• Sports Analytics
• Handball Performance Analysis
• Tactical Decision-Making in Sports
• +۳ مورد دیگر

Aron Laxdal, Sveinn Þorgeirsson, and Jose M Saavedra are researchers at the University of Iceland's School of Education, Department of Sport Science. Their collaborative work focuses on applying advanced statistical methods to analyze handball performance metrics in semi-professional leagues. These researchers specialize in sports analytics with particular emphasis on tactical decision-making in handball. Their recent work examines the relationship between shot selection strategies and game outcomes, analyzing six seasons of data from the Icelandic elite handball divisions (2018-2024) involving 1,410 games and 133,447 shots. They employ sophisticated mixed-effects logistic regression models to evaluate how high-probability shots (6-meter shots) versus low-probability shots (9-meter shots) correlate with winning probabilities, with special attention to gender differences between men's and women's leagues. Their research demonstrates that high-probability shots significantly increase winning probability, with a notably stronger effect in women's handball (77% increase in winning odds per 10% increase in 6-meter shots) compared to men's handball (20% increase). This finding challenges conventional handball strategies and suggests potential tactical adjustments that could enhance team performance. These researchers work closely with HBStatz, the official statistics partner of the Icelandic Handball Association, utilizing comprehensive in-situ data collection methods that have been validated through previous studies. Their interdisciplinary approach bridges sports science, statistics, and practical coaching applications.

Natalya Pya Arnqvistمشاهده پروفایل

دانشیار

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +۷ مورد دیگر

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.