جستجوی استاد راهنمای دانشگاهی

Tom Lindströmمشاهده پروفایل

مدرس ارشد

• Theoretical Biology
• Ecology
• Epidemiology
• +۷ مورد دیگر

Tom Lindström is a Senior Associate Professor and Head of the Biology Division at the Department of Physics, Chemistry and Biology (IFM), Linköping University. His interdisciplinary research integrates theoretical biology with applied modeling in ecology and epidemiology, supported by collaborations with U.S. agencies such as the USDA and DHS. His research focuses on developing advanced statistical and computational models to address complex biological challenges. Key areas include livestock epidemiology , where he simulates disease outbreaks and evaluates control strategies; movement ecology , particularly reptile movement and invasion dynamics in tropical Australia; and wildlife monitoring , where he develops Bayesian methods to estimate hunting harvests in Sweden using incomplete data. His work emphasizes handling imperfect datasets through structured modeling. Recent publications reveal a strong trend in using Bayesian inference , network modeling , and large-scale simulations to study animal movement and disease spread, particularly in U.S. livestock systems. These studies contribute to policy-making by offering predictive tools for transboundary animal diseases and food security threats. Scientific contributions include: Development of efficient algorithms for nationwide livestock movement simulation Pioneering ensemble modeling approaches in epidemiology Statistical methods for estimating wildlife harvests from area-based reporting Identification of climatic drivers in reptile movement and invasion success Tom Lindström actively mentors research projects and leads collaborative teams across international institutions. He has secured significant research funding from U.S. federal agencies, underscoring the societal impact of his work. His lab focuses on bridging data gaps in animal movement and improving predictive accuracy for disease control policies. Future work continues to advance computational tools for ecological and epidemiological forecasting.

Lars Arvestadمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Evolutionary Genomics
• Comparative Genomics
• +۲ مورد دیگر

Lars Arvestad is a Senior Lecturer at Stockholm University's Department of Mathematics, Faculty of Science. His research focuses on computational biology problems in evolution and comparative genomics, with significant contributions to bioinformatics tool development for genome assembly and phylogenetic analysis. He teaches courses in programming techniques for mathematicians, database technology, and software engineering. Academic Appointments: Senior Lecturer in Mathematics (2013-present) Research Focus: Computational modeling of biological systems, particularly in evolutionary genomics and genome assembly challenges His work includes creating BESST for efficient genome scaffolding, VMCMC for Bayesian phylogeny analysis, and Fastphylo for accelerated phylogenetic tree construction. Key technical innovations involve handling PE-contamination in mate-pair libraries and developing automated burn-in estimation for MCMC methods. Recent publications demonstrate expertise in integrating mathematical modeling with biological data analysis, particularly in solving practical challenges in next-generation sequencing data processing. The research group Computational Mathematics at Stockholm University develops methods applicable across molecular to planetary scale systems.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +۴ مورد دیگر

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Theoretical Physics
• Nuclear Physics
• Particle Physics
• +۷ مورد دیگر

Christian Forssén is a Professor in theoretical physics at Chalmers University of Technology, affiliated with the Department of Physics and the Division of Subatomic, High Energy and Plasma Physics. His research focuses on theoretical nuclear and particle physics, employing advanced computational tools like Bayesian methods and machine learning to study low-energy effective field theories, nuclear forces, and astrophysical connections. He is an elected Fellow of the American Physical Society and a member of The Royal Society of Arts and Sciences in Gothenburg. Key research interests include nuclear many-body systems, symmetry energy in neutron-rich matter, and precision calculations of nuclear observables. His work bridges fundamental symmetries and astrophysical phenomena, such as neutron star properties and dark matter interactions. Recent studies emphasize Bayesian parameter estimation in chiral effective field theory and emulation techniques for large-scale ab initio calculations. Publications highlight breakthroughs in neutron skin predictions for 208Pb, first observations of exotic nuclei like 28O, and rigorous constraints on three-nucleon forces. Awards include recognition for contributions to nuclear theory and computational methods. Ongoing projects involve collaborations on dark matter detection and precision measurements of neutron halo nuclei.

• Forest Ecology
• Biodiversity Conservation
• Climate Change Adaptation
• +۳ مورد دیگر

Bengt-Gunnar Jonsson is a Professor in the Department of Biology at Mid Sweden University's School of Natural Science, Design and Sustainable Development. With over 25 years of research experience, he specializes in boreal forest ecology, deadwood dynamics, and biodiversity conservation. His work focuses on wood-inhabiting fungi , lichen communities , and forest fragmentation effects , particularly in northern Sweden's high conservation value forests . Jonsson has been pivotal in developing green infrastructure models and climate-adaptive forestry strategies . Key research themes: forest conservation, fungal ecology, climate change mitigation, deadwood management Research tools: presence-absence sampling, Bayesian modeling, long-term ecological monitoring Policy contributions: EU Biodiversity Strategy evaluations, IPBES initiatives His recent publications (2025-2020) demonstrate sustained expertise in forest carbon dynamics , saproxylic community analysis , and landscape-scale conservation . Jonsson frequently collaborates with international researchers on temperate and boreal forest biodiversity , with notable work on functional ecology and ecosystem service trade-offs . Current projects include 'Climate - collaborative learning initiative' and 'Functional green infrastructure in boreal forests' . Scientific contributions span multiple systematic reviews in environmental management, including Forest Ecology and Management and Environmental Research Letters . He has co-authored Cambridge University Press 's Biodiversity in Dead Wood and contributed to IPBES policy frameworks. Jonsson's research integrates empirical forest monitoring with theoretical ecological modeling , particularly in wood decomposition processes and fungal community dynamics .

Thomas Schönمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Modeling
• Signal Processing
• +۷ مورد دیگر

Thomas Schön serves as the Beijer Professor of Artificial Intelligence at Uppsala University's Department of Information Technology within the Faculty of Science and Technology. His research focuses on developing probabilistic models and algorithms for extracting knowledge from data, with particular emphasis on dynamical systems. His research interests span multiple disciplines at the intersection of Machine Learning and statistics, signal processing, automatic control, and computer vision . He takes a systematic approach to representing and manipulating uncertainty through probability theory. His work encompasses both basic and applied research, with strong collaborations with industry partners including ABB Crane Systems, Autoliv, Saab, Sectra, and Xsens Technologies. Schön's research output demonstrates significant contributions to probabilistic modeling of dynamical systems , with particular expertise in sequential Monte Carlo methods (particle filters), Markov chain Monte Carlo, Gaussian processes, and deep learning. His recent work includes advancements in incorporating background knowledge into machine learning models and developing flexible probabilistic frameworks. Schön actively supervises numerous PhD students working on diverse topics including Bayesian nonparametric models, deep learning applications, uncertainty-aware systems, and probabilistic computer vision. His research is funded by The Swedish Research Council (VR), The Swedish Foundation for Strategic Research (SSF), and Vinnova.

Tom Brittonمشاهده پروفایل

استاد

• Epidemic modeling
• Biostatistics
• Network analysis
• +۵ مورد دیگر

Tom Britton is a Professor in Mathematical Statistics (Holder of the Cramér chair) at the Department of Mathematics, Stockholm University. He leads the SU-Infection spread modeling research group and serves as chairman of the Stockholm Mathematics Center (SMC). His work primarily focuses on developing and analyzing mathematical/statistical models for infection spread and applying statistical methods to infection spread data. His research interests span epidemic modeling, biostatistics, network analysis, and mathematical statistics with applications toward genetics and molecular biology including phylogenetics. Britton's work encompasses applications of biostochastics and biostatistics, discrete random structures, and finance and insurance. He has made significant contributions to understanding disease transmission dynamics, particularly during the COVID-19 pandemic. Britton's recent publications demonstrate a strong emphasis on practical applications of mathematical models to public health challenges. His work examines contact tracing strategies, vaccine impact analysis, immunity waning dynamics, and optimal intervention strategies. There is a clear pattern of translating theoretical mathematical models into actionable public health insights, with particular focus on the interplay between population heterogeneity, immunity levels, and intervention effectiveness. As an academic leader, Britton serves as academic editor for PLoS Comp Biol and associate editor for Journal of Mathematical Biology and JRSS A (Statistics in Society). He is also a board member of the European Society for Mathematical and Theoretical Biology (ESMTB). Britton actively mentors the next generation of researchers, currently supervising PhD student Fanny Bergström and Post Doc Martina Favero. Throughout his career, he has successfully guided 13 PhD students and multiple postdoctoral researchers. His current research is supported by significant grants from the Swedish Research Council (3.6 MSek, PI 2021-2024) and the KA Wallenberg Foundation (0.7 MSek, PI 2023-2025). The SU-Infection spread modeling research group, which Britton leads, develops and analyzes mathematical/statistical models for the spread of infection and applies statistical methods to adapt these models to infection spread data. This work has been particularly relevant during recent pandemics and continues to inform public health decision-making.

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.

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 .

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.

• Robotics
• Machine Learning
• Tactile Sensing
• +۴ مورد دیگر

Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.

Behnaz Pirzamanbeinمشاهده پروفایل

استادیار

• Bayesian Statistics
• Spatial-Temporal Modeling
• Machine Learning
• +۷ مورد دیگر

Behnaz Pirzamanbein is an Assistant Professor at the Department of Statistics , Lund University. She also serves as a Project Manager , Researcher for MERGE: ModElling the Regional and Global Earth system, and Principal Investigator for eSSENCE: The e-Science Collaboration. Her academic roles bridge statistics with environmental, climate, and medical research. Her research focuses on spatial-temporal models , Bayesian statistics , and efficient computational methods , with applications in image analysis , artificial intelligence , and quantifying high-resolution synchrotron images . She has contributed to understanding the Baltic Sea during the Last Interglacial, Holocene land cover changes, and microstructural changes in liver disease. Recent Publications highlight her interdisciplinary work: 2025 : Morphological insights into Baltic Sea environmental conditions via foraminifera. 2025 : Deep learning for 3D microstructural analysis in fibrotic liver disease. 2025 : Holocene land cover trends in North America and vegetation-atmosphere feedbacks. 2024 : Comparative analysis of MASH in mouse models and humans using micro-CT. Scientific Awards include: Outstanding Bayesian research applied to climate science (SBSS, ASA, 2016) Honorary Mention in Statistics and the Environment (ENVR, ASA, 2016) She teaches Bayesian Methods and Data Visualization courses, and has supervised masters and doctoral students. Her work intersects with the UN Sustainable Development Goals in Probability Theory, Climate Science, Environmental Sciences, and Computational Mathematics.

• Probability Theory
• Statistics
• Gaussian Processes
• +۵ مورد دیگر

Jonas Wallin is a Senior Lecturer and Project Manager at the Department of Statistics, Lund University. His research bridges probability theory, statistics, and computational methods, with applications to public health, climate modeling, and spatial data analysis. He actively contributes to interdisciplinary projects addressing global challenges. Key Research Areas : Gaussian processes, Bayesian inference, disease risk mapping, climate-health interactions, statistical modeling on metric graphs. Projects : Leading Horizon Europe and Swedish Research Council-funded initiatives on antimicrobial resistance, spatio-temporal modeling, and protein degradation in immunity. Supervision : Primary supervisor for PhD students Erik Karlsson Strandh and Mattias Jönsson, focusing on climate-driven disease dynamics and Gaussian processes. Collaborations : Engaged in international partnerships and peer-review activities for journals and conferences. Wallin’s work aligns with UN Sustainable Development Goals, particularly in health and environmental resilience. Recent publications highlight methodological innovations in Markov random fields and regularization techniques, while his expertise in data quality control impacts disease surveillance practices.

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.