جستجوی استاد راهنمای دانشگاهی

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.

Björn Forsbergمشاهده پروفایل

استادیار

• Structural Bioinformatics
• Cryo-Electron Microscopy (Cryo-EM)
• Computational Biology
• +۷ مورد دیگر

Björn Forsberg is an Assistant Professor in the Department of Physics, Chemistry and Biology (IFM) at Linköping University, where he leads a research group in structural bioinformatics. He is affiliated with SciLifeLab Linköping and the National Supercomputer Centre (NSC), and is part of the Wallenberg National Program for Data-Driven Life Science (DDLS), supported by the Knut and Alice Wallenberg Foundation. His work bridges computational science and molecular biology, focusing on the development of novel methods for analyzing cryo-electron microscopy (cryo-EM) data. His research centers on understanding molecular life through data-driven approaches. He develops computational tools to analyze ensemble cryo-EM data, using spatial filtering and local correlation metrics to identify sources of structural variation and attribute them to biological mechanisms. His lab leverages high-performance computing resources, including the Berzelius supercomputer, to process large-scale datasets and improve the resolution and interpretability of molecular models. This work has broad implications for understanding diseases like Alzheimer's and for drug discovery. The recent publications highlight a strong trend in advancing cryo-EM methodology, particularly through software development (e.g., RELION) and the integration of machine learning and GPU computing. His work spans fungal metabolism, ion channel dynamics, chloroplast ribosomes, and foundational algorithmic improvements in 3D reconstruction. The research combines structural biology, bioinformatics, and computational physics to extract biological meaning from complex data. Scientific Awards and Recognition: Selected for the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) Supported by the Knut and Alice Wallenberg Foundation through DDLS and the PALS (Program for Academic Leaders in Life Science) network Björn Forsberg is actively building his research team, advising PhD and master’s students, and seeking postdoctoral researchers with backgrounds in computational biology, computer science, or related fields. His lab collaborates with leading experts in structural biology and uses national research infrastructures to push the boundaries of data-driven life science. He has no listed formal grants yet, but his program is funded through major foundation support. His research is conducted at the intersection of the Department of Physics, Chemistry and Biology (IFM), SciLifeLab Linköping, and the National Supercomputer Centre (NSC), forming a multidisciplinary environment for innovation in computational structural biology.

• Smooth Transition Models
• State-Space Models
• Macroeconometrics
• +۸ مورد دیگر

Yukai Yang is a Senior Lecturer at the Department of Statistics, School of Economics and Business, Uppsala University. His research bridges econometric theory with applied statistical methods. Education : B.Eng, Shanghai Jiao Tong University Cand. Polit, University of Copenhagen PhD, Aarhus University (2012) His research spans smooth transition models , state-space models , Bayesian VAR , panel data analysis , and directional statistics , with recent interdisciplinary work in medical data science . Articles highlight methodological innovation in time series , machine learning , and mobile biometrics . Scientific Awards : None listed in provided data. His work includes software development (e.g., mfbvar package) and collaborations in multimedia systems and health economics .

• Dementia Research
• Geriatric Neurology
• Registry-Based Epidemiology
• +۵ مورد دیگر

Sara Garcia Ptacek is an Assistant Professor and Docent at Karolinska Institutet, affiliated with the Department of Neurobiology, Care Sciences and Society and the Maria Eriksdotter research group. She maintains a dual role as a neurologist at the Cognitive Clinic, Karolinska University Hospital in Solna, with 50% research activity. Her research spans dementia epidemiology , geriatric neurology , and registry-based studies . Key interests include medication repurposing to slow cognitive decline, the impact of COVID-19 on dementia care, and dementia with Lewy bodies through collaboration with the European Dementia with Lewy bodies consortium. She utilizes the Swedish Dementia Registry (SveDem) with over 100,000 patients for longitudinal cognitive assessment studies. Her recent publications demonstrate strong focus on health disparities in dementia care , comorbidity effects on cognitive trajectories , and methodological innovations combining traditional cohort studies with machine learning. Her work frequently appears in high-impact journals including Nature Communications , Neurology , and JAMA Network Open . Scientific recognition includes: Docent in Neurosciences at Karolinska Institutet (2023) Fellow of the European Board of Neurology (FEBN) (2019) Prize for Clinical Thesis from Spanish National Royal Academy of Medicine (2017) Excellence prize for PhD thesis from Universidad Complutense de Madrid (2015) Current research initiatives include "Innovative ways to fight Alzheimer's disease" (funded by Leif Lundblad Family donation, 2023-2026) and "Causal inference using Dynamic Bayesian Networks" (Loo and Hans Osterman Foundation, 2022-2023). Her work bridges clinical neurology with population health research, focusing on improving dementia diagnosis, care quality, and treatment outcomes through registry science. She contributes to major international collaborations including the European Dementia with Lewy bodies (E-DLB) consortium and maintains active research on pandemic impacts on dementia care and health disparities in dementia diagnostics.

Joshua Tabhمشاهده پروفایل

پژوهشگر ارشد

• Ecophysiology
• Climate Change
• Evolutionary Biology
• +۶ مورد دیگر

Joshua Tabh is a Research Fellow at Lund University , affiliated with the Ecological and Evolutionary Physiology and Evolutionary Ecology and Infection Biology groups. His research integrates ecophysiology , climate change , and evolutionary biology to study how thermal environments drive avian phenotypes via plasticity and selection. He employs Bayesian hierarchical modeling and computational tools (e.g., Julia , C++ ) to analyze large datasets, focusing on climate-adaptive responses and conservation implications. Current projects include testing traditional ecological theories to explain avian morphological shifts under global warming, with contributions to Communications Biology (2025) and Nature Ecology and Evolution (2024). His work aligns with UN Sustainable Development Goals for biodiversity and climate action. Scientific Contributions: 2025: 20+ research outputs, including Communications Biology and Journal of Avian Biology publications. 2022: Recipient of the Uncovering the mechanisms driving Allen’s Rule in birds prize. Conservation Engagement: Board member of the Nova Scotia Nature Trust , advocating for private land preservation.

• Variational inference
• Bayesian inference
• Deep learning
• +۴ مورد دیگر

Najmeh Abiri is a researcher with multiple affiliations at Lund University, holding positions as a Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), a Visiting Research Fellow at Computational Science for Health and Environment, a Postdoc at the Department of Statistics, and a Researcher at eSSENCE: The e-Science Collaboration. Her research focuses on the intersection of statistics and machine learning with practical applications. Key areas include: Variational inference Bayesian inference Deep learning architectures Missing data imputation techniques Computational biology applications Abiri's publication record shows a clear trajectory toward increasingly sophisticated applications of machine learning in scientific domains. Her recent work demonstrates expertise in diffusion models for time series analysis, AI-driven disease outbreak prediction, and quantum mechanical applications of deep learning. Her 2019 paper on denoising autoencoders for missing data problems has been particularly influential with 62 citation indexes. She has received notable attention for her interdisciplinary work, with publications featured in high-impact journals like The Lancet Regional Health - Europe and covered by multiple news outlets. Her research on West Nile virus prediction has garnered policy citations, indicating real-world impact. Abiri actively participates in the academic community through workshops and seminars, particularly those focused on AI technologies and their applications in scientific research. Her collaborative work spans multiple institutions and research domains, reflecting the interdisciplinary nature of her expertise.

Behnaz Pirzamanbeinمشاهده پروفایل

مدرس ارشد

• Spatial-Temporal Modeling
• Bayesian Statistics
• Computational Statistics
• +۶ مورد دیگر

Behnaz Pirzamanbein is a Senior Lecturer in the Department of Statistics at Lund University, Sweden, serving as Project Manager for MERGE: ModElling the Regional and Global Earth system and Principal Investigator for eSSENCE: The e-Science Collaboration. She teaches Bayesian Methods (STAE02) using RStan and Data Visualization courses (STAE04/DABN19) for students and industry professionals, integrating theoretical statistics with practical applications. Her research specializes in spatial-temporal modeling, Bayesian statistics, and computational methods applied to environmental and medical challenges. She develops advanced techniques for synchrotron image analysis, AI-driven environmental reconstruction, and liver disease diagnostics, bridging statistics with climate science, paleoecology, and computational medicine. Recent work focuses on Baltic Sea paleoenvironments, Holocene land cover dynamics, and 3D microstructural analysis of fibrotic liver tissue. Analysis of her recent publications reveals strong interdisciplinary trends: environmental reconstruction using pollen and ancient DNA data (40%), medical imaging/AI applications (30%), and climate-statistics integration (30%). Key methodologies include Bayesian hierarchical modeling, deep learning for 3D tomography, and high-resolution spatiotemporal analysis. Her scientific recognition includes: Outstanding Bayesian Research Applied to Climate Science award (ASA Section on Bayesian Statistical Science, 2016) Honorary Mention in Statistics and the Environment (ASA Section on Statistics and the Environment, 2016) Dr. Pirzamanbein actively supervises master's and PhD students while leading major projects including POLLENOMICS (decoding European farming history through ancient DNA/pollen integration) and Digital Transformation of Flood Prediction in Trelleborg. Her collaborations span 12 research projects with international institutions, supported by FORMAS and Swedish research councils, focusing on climate modeling, environmental archives, and AI-driven medical diagnostics.

• Machine Learning
• Visual Analytics
• Explainable AI
• +۷ مورد دیگر

Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.

• Information Network Analytics
• Decentralized Machine Learning
• Data Mining
• +۵ مورد دیگر

Sarunas Girdzijauskas is a Professor at the Department of Computer Science within KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), where he leads the Graph Analytics and Network Learning group (GALE) . He also holds a Senior Researcher position at RISE (Research Institutes of Sweden) and has served as Program Director for EECS's doctoral program in Information and Communication Technology (2019-2022) and Deputy Director of Third-Cycle Education at EECS. PhD from Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2016 Docent in Computer Science at KTH (2016) Coordinated multiple large-scale projects: H2020 Marie Curie RAIS, Marie Curie ITN iSocial Research Focus : His work spans decentralized machine learning systems, graph analytics, and blockchain infrastructure. Key areas include: Information network analysis for social and technical systems Federated learning with adaptive personalization Graph partitioning and distributed algorithms Privacy-preserving models with differential privacy Knowledge distillation for evolving graph representations Scalable solutions for big data and IoT botnet detection Article Trends : Recent work demonstrates convergence of graph neural networks with federated learning, emphasizing privacy (differential privacy in 5/15 articles), robustness (out-of-distribution detection in 3 articles), and decentralized infrastructure (blockchain, gossip protocols, and P2P overlays). The articles frequently address temporal modeling, knowledge distillation, and heterogeneous graph representations. Scientific Leadership : Coordinated H2020 Marie Curie European Training Network 'RAIS' (2018-2023) Coordinated Marie Curie ITN 'iSocial' (2012-2017) Activity Leader for EIT Digital Innovation Activities Teaching & Advising : Has supervised/examined over 60 master theses and currently advises 8 PhD students, including Adam Orucu (Main Supervisor, 2024-), Filip Cornell (Main Supervisor, 2020-), and Ahmed Emad Samy Youssif (Main Supervisor, 2019-). Former supervisees include 9 PhDs like Dr. Edvin Listo Zec (2025) and Dr. Debaditya Roy (2024).

Johan Alenlövمشاهده پروفایل

مدرس

• Machine Learning
• Statistical Inference
• Particle Filtering
• +۵ مورد دیگر

Johan Alenlöv is a University Lecturer in the Statistics and Machine Learning (STIMA) group at the Department of Computer Science, Linköping University. His work is situated at the intersection of computational statistics and machine learning, with a focus on sequential Monte Carlo methods and state-space modeling. His research interests include Machine Learning , Statistical Inference , Particle Filtering , Sequential Monte Carlo , Bayesian Computation , and Data Science . These areas are central to modern data analysis and AI-driven modeling in dynamic systems. The recent publications highlight a consistent focus on online smoothing, marginal inference, and Hamiltonian Monte Carlo methods. Collectively, the articles reflect a strong trend in developing scalable, numerically stable algorithms for inference in complex, nonlinear state-space models—critical for applications in signal processing, robotics, and time-series analysis. Johan Alenlöv has not been mentioned as receiving any scientific awards in the provided text. There is no information available about students advised by Johan Alenlöv or any grants he may have received. However, his collaborative work with researchers like Jimmy Olsson and Fredrik Lindsten suggests active participation in the statistical learning research community. He is affiliated with the Statistics and Machine Learning (STIMA) research group at Linköping University, which conducts research in modern data analysis and hosts both a Bachelor's and an international Master's program in Statistics and Machine Learning.

Martin Sahlénمشاهده پروفایل

دانشیار

• astrophysics
• astronomy
• cosmology
• +۹ مورد دیگر

Martin Sahlén is an Associate Professor in the Department of Physics and Astronomy at Uppsala University, specializing in astronomy with a focus on astrophysics. His research spans theoretical and observational studies of the large-scale structure of the universe through galaxy clusters, voids, and the universe's first galaxies to understand galaxy physics, star formation, dark energy, dark matter, cosmic inflation, and the reionization process in the early universe. Dr. Sahlén's research interests center on cosmology, particularly the intersection of theoretical models and observational data. He employs Bayesian inference techniques to analyze cosmological datasets, with special attention to the philosophy of cosmology, including model inference, fine-tuning of natural constants, and multiverse explanations. His work bridges observational astronomy with theoretical physics to address fundamental questions about the universe's structure and evolution. His recent publications (2024-2025) predominantly focus on the Euclid space mission, representing a major portion of his scholarly output. These works span instrumentation, data analysis techniques, cosmological simulations, and theoretical interpretations. Key themes include constraints on modified gravity models, galaxy cluster analyses, weak lensing studies, and machine learning applications for extracting physical parameters from astronomical data. His research demonstrates a consistent focus on leveraging next-generation observational data to test fundamental cosmological theories. Dr. Sahlén serves as Principal Investigator for multiple significant research grants, including: (1) 'The Galaxy – Dark Matter Connection with HST, JWST and EUCLID' (2021-2026, 8 MSEK); (2) 'Searching for New Physics in the Early Universe' (2022-2024, 1 MSEK); and (3) 'Astronomical object classification and anomaly detection with complex spectroscopic 4MOST data sets' (2023-2027, 4 MSEK). He actively participates in major scientific collaborations including 4MOST, BUFFALO, Euclid and SKA Observatory, and contributes externally to the Dark Energy Survey. As an educator, Dr. Sahlén is responsible for and teaches the interdisciplinary course 'Three Approaches to Cosmology' (10 credits), which likely reflects his broad expertise spanning observational, theoretical, and philosophical aspects of cosmology.

• Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +۳ مورد دیگر

Jemima M. Tabeart is an Assistant Professor at TU Eindhoven, affiliated with the Centre for Analysis, Scientific Computing and Applications . Her research focuses on numerical linear algebra, data assimilation, and covariance matrices. She actively contributes to outreach and scientific crafting, and advises students on final projects in computational science. Education: PhD (2016–2019): University of Reading (supervised by Prof. Sarah Dance and others) MRes (2015–2016): Mathematics of Planet Earth CDT (Imperial College London/Reading) MMath (2011–2015): University of Bath with study abroad at Université Grenoble Alpes Research Interests: Large-scale numerical linear algebra and its applications Development of efficient preconditioners for 4D-Var and data assimilation systems Treatment of covariance matrices in computational models Interdisciplinary applications in environmental and meteorological modeling Teaching & Leadership: Organizing a 2025 Research Semester Programme at CWI on numerical analysis and machine learning Teaching courses on calculus, linear algebra, and optimization at TU Eindhoven Co-lecturing the MasterMath course on Advanced Numerical Linear Algebra Professional Activities: Member of the 4TU.AMI SRI in Bridging Numerical Analysis and Machine Learning Principal investigator in a network funded by the INI Network Support for Mathematical Sciences Advocate for low-carbon travel, documenting her journeys on her blog

• Machine Learning
• Deep Learning
• Deep Generative Modeling
• +۴ مورد دیگر

Najmeh Abiri is a Senior Lecturer at Halmstad University's School of Information Technology. Her research focuses on machine learning, deep learning, and Bayesian inference with applications in image/text processing and life sciences. Research Interests She specializes in: Deep Generative Modeling (GenAI) Probabilistic machine learning Quantum mechanics applications Epidemiological forecasting Publications Her recent work spans tick classification challenges in citizen science (2025), time series imputation (2025), disease risk mapping (2024), and quantum mechanics modeling (2020). Key themes include data quality control, robust probabilistic methods, and ecological informatics applications. Affiliations School of Information Technology Halmstad University

Hossein Azizpourمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Hossein Azizpour is an Associate Professor in Machine Learning at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning (RPL). He holds associated roles at the Science for Life Laboratory, Swedish e-Science Research Centre, WASP, and Digital Futures. His research focuses on deep learning under uncertainty, model interpretability, feature selection, and applications in life sciences, physics, and earth observation. Research Themes : (1) Deep learning under uncertainty (Bayesian methods, semi-supervised learning), (2) Interpretable AI (feature attribution, understanding neural networks), (3) Generative & foundation models (Vision Transformers, diffusion models), (4) Applications in medical imaging, fluid dynamics, and earth observation. Recent Work Trends : His publications emphasize vision transformers, uncertainty quantification, and cross-domain applications. Key areas include model optimization (e.g., Patchdropout), medical AI (e.g., breast cancer risk prediction), and physics-informed deep learning. Scientific Awards : Best Paper Award, CVPR DeepVision Workshop (2015) Google Travel Grant, CVPR DeepVision Workshop (2015) Best Paper Runner-Up, CVPR DeepVision Workshop (2014) Best Industry Paper Prize, BMVC (2013) Outstanding Reviewer, CVPR (2013) Advising & Collaborations : Supervises PhD students in AI for physics applications (e.g., fluid dynamics), medical imaging, and computer vision. Collaborates with institutions like AstraZeneca, Alan Turing Institute, and Meta. Labs & Teams : Leads the Robotics, Perception and Learning (RPL) division’s research and co-organizes the Computer Vision Reading Group at KTH.

Eliel Camargo-Molinaمشاهده پروفایل

مدرس ارشد

• Cosmology
• Early Universe Physics
• Artificial Intelligence in Physics
• +۳ مورد دیگر

Eliel Camargo-Molina holds dual roles at Uppsala University as a Senior Lecturer in Game Design and a postdoctoral researcher in High Energy Physics . He bridges theoretical physics with artificial intelligence through Interdisciplinary AI methodologies Early universe phase transitions Transdisciplinary collaborations His research spans cosmology , particle physics , and AI applications , with recent work on Transformer-based Lagrangian generation Electroweak phase transitions Quantum corrections in de Sitter spacetime Diversity-enhanced optimization algorithms Scientific recognition includes Natural Sciences Fellowship at SCAS (2022-2023) Co-founder of Art & Science Initiative