جست‌وجوی استاد و پژوهشگر

Hoang Nguyenمشاهده پروفایل

دانشیار

• Financial Econometrics
• Macroeconomics
• Quantitative Finance
• +۳ مورد دیگر

Hoang Nguyen is an Associate Professor at the Department of Management and Engineering (IEI), Linköping University, Sweden. He holds a PhD in Business and Quantitative Methods from Universidad Carlos III de Madrid. Previously, he served as a postdoctoral researcher at Örebro University. His research focuses on financial econometric models for analyzing high-dimensional time series and interconnections between financial and macroeconomic variables. Education: PhD in Business and Quantitative Methods (Universidad Carlos III de Madrid) and prior academic roles include a postdoctoral position in financial mathematics at Örebro University. Research Interests: Developing advanced econometric frameworks to study systemic risk in financial markets, portfolio optimization under uncertainty, and macro-financial linkages. His work integrates computational methods and Bayesian techniques to address challenges in high-dimensional datasets. His recent publications explore topics such as stock market dynamics, systemic risk modeling, and oil market shocks. Notable contributions include applications of copula models and deep learning in financial volatility forecasting. Affiliations: Member of the Production Economics division within the Department of Management and Engineering, contributing to research on optimizing productive resources and financial decision-making. No scientific awards explicitly mentioned in the provided texts. Grants and Advising: Details on grants are not provided. Current PhD students listed in collaborations include Eva-Lena Eriksson, Stephane Harushakamwe, and Anton Zackrisson, though explicit advising roles are not specified. Labs/Teams: Active within the Production Economics (PEK) division, focusing on industry-relevant research and innovation aligned with Linköping University’s mission to advance industrial and societal development.

• Machine Learning
• Statistical Inference
• Sequential Monte Carlo Methods
• +۳ مورد دیگر

Alessandro Mastrototaro holds a PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology. His research focuses on advancing statistical learning methods, particularly sequential Monte Carlo (SMC) and variational inference techniques for state-space models. He currently teaches Regression Analysis (SF2930) at KTH, serving as course responsible and instructor. His work emphasizes online learning algorithms that enable real-time parameter estimation and particle proposal adaptation in dynamic data environments. Key contributions include the development of the Online Variational Sequential Monte Carlo algorithm and the ALVar estimator for adaptive variance estimation in particle filters. Publications span machine learning conferences (e.g., ICML) and journals like the Journal of the American Statistical Association, showcasing expertise in computational statistics and algorithmic innovation.

• Applied Microeconomics
• Behavioral Economics
• Economics of Education
• +۴ مورد دیگر

Pol Campos is an Assistant Professor of Economics and Tore Browaldh Scholar at Lund University. He holds a PhD from Lund University (2020) and was a postdoctoral researcher at the University of Copenhagen (2020–2022). His research focuses on applied microeconomics, employing field and lab experiments to study education, health, and behavioral economics. He investigates how incentives and reference points influence decision-making, such as vaccination uptake and educational effort. He also examines heterogeneity in preferences and its impact on behavior. Research Interests: Educational incentives and goal mismatch Health economics and vaccination behavior Experimental methods in behavioral economics Social preferences and environmental externalities Recent Research Output Trends: Recent work emphasizes vaccination incentives, environmental externalities, and non-Bayesian decision-making. Publications span working papers and peer-reviewed journals, addressing topics like vaccine hesitancy and education policy. Prizes & Grants: Recipient of the Pontus Roos Memoria (2019) Swedish Research Council funding for vaccine hesitancy research (2023–2026) Professional Activities: Engaged in 41 activities, including 26 invited talks on topics like 'Aversion to Incentives' and 'Social Preferences in Environmental Behavior'.

• Healthcare Logistics
• Operations Research
• Healthcare System Optimization
• +۶ مورد دیگر

Sebastiaan Meijer is a Professor of Health Care Logistics at KTH Royal Institute of Technology. His research focuses on applying logistics principles to healthcare systems, particularly in optimizing resource allocation, patient flow, and integrating technology like sensor data and simulations. He leads a team developing gaming simulations and interactive methods to improve healthcare efficiency, with a focus on smart cities and elderly care. Key areas include home health monitoring platforms, emergency department logistics, and medical waste management. His work addresses challenges in coordinating complex healthcare networks across institutions. Research Interests: Healthcare Logistics Simulation Modeling Patient Flow Optimization Telemedicine Reablement Programs for Older Adults Medical Waste Transportation Recent Articles: Focus on real-world evidence gaps in clinical trials, hospital overcrowding diagnostics, and hybrid modeling for complex systems. These studies highlight interdisciplinary approaches combining data science, simulation, and healthcare policy. Awards: Not explicitly mentioned in provided texts. Advising & Grants: Supervises 16+ students (listed in 'students'). Research projects include the ASSIST reablement program feasibility study and telemedicine evaluation in surgical settings. Labs/Teams: Leads a multidisciplinary team at KTH addressing healthcare logistics challenges through simulation games and data-driven methods.

• Medical Imaging
• Image Processing
• Medical Visualization
• +۳ مورد دیگر

Örjan Smedby is a Professor at KTH Royal Institute of Technology, specializing in Medical Image Processing and Visualization. His research focuses on developing precise and efficient computational methods for medical decision-making, particularly in automated segmentation of medical images and quantitative analysis of bone structure. Key areas include tumor segmentation, bone graft analysis, and improving imaging techniques for clinical applications such as cancer treatment evaluation and osteoporosis assessment. His work integrates advanced machine learning techniques like deep learning and generative models to enhance image quality, reduce radiation exposure, and automate diagnostic processes. Collaborations involve interdisciplinary teams addressing challenges in oncology, neurology, and orthopaedics. Research Interests: - Medical Image Segmentation - Deep Learning Applications in Radiology - Quantitative Bone Microstructure Analysis - AI-Driven Diagnostic Tools - Iterative Reconstruction Algorithms His publications span topics such as optimizing PET/CT imaging protocols, Alzheimer’s disease neuroimaging, and breast cancer diagnostics. The research emphasizes translating computational methods into clinical workflows to improve healthcare efficiency and accuracy.

Zheng Duanمشاهده پروفایل

مدرس ارشد

• Satellite Remote Sensing
• Hydrological Modelling
• Glacier Modelling
• +۷ مورد دیگر

Zheng Duan is a Senior Lecturer and Associate Senior Lecturer in the Department of Physical Geography and Ecosystem Science at Lund University. He holds roles as Principal Investigator in the BECC (Biodiversity and Ecosystem services in a Changing Climate) and MERGE (ModElling the Regional and Global Earth system) research groups. His research focuses on satellite remote sensing, hydrological modelling, glacier dynamics, and machine learning applications in environmental science. Duan leads several international projects, including BioClima (EU Horizon Europe) and initiatives on Tibetan Plateau lake bathymetry and Arctic hydrology. He has advised two students and published over 150 peer-reviewed articles. Notable contributions include advancing soil moisture downscaling, glacier-hydrological model integration, and drought monitoring using satellite data. His work aligns with UN SDGs 6 (Clean Water), 13 (Climate Action), and 15 (Life on Land). Education details are not explicitly provided in the text, but his research career spans institutions like the Swedish National Space Agency and Crafoord Foundation-funded projects. Collaborations include researchers from China, Sweden, and global networks through EGU General Assemblies and media engagements (e.g., interviews on lake color changes). Research interests are anchored in Earth observation technologies and their application to water cycle dynamics, ecosystem resilience, and climate change mitigation. Recent projects emphasize multi-source satellite data fusion, machine learning for hydrological prediction, and quantifying environmental variables like dissolved organic carbon in river systems. His articles collectively address themes such as drought detection, lake bathymetry estimation, and improving hydrological models through data integration. Key methodologies include triple collocation analysis, Bayesian optimization, and deep learning frameworks. Duan’s work bridges theoretical ecology with practical applications, supporting sustainable water management and climate policy. He leads 11 active/ongoing projects, including BioClima (2025–2028), which integrates Earth observations for biodiversity and climate monitoring. Grants include funding from the European Commission, Swedish National Space Agency, and Crafoord Foundation. Media participation highlights public communication of scientific findings, such as lake color changes linked to human activities. Lab affiliations include BECC and MERGE, where interdisciplinary teams tackle global environmental challenges. Future work emphasizes scaling up satellite-derived insights for policy-relevant solutions in water security and ecosystem conservation.

• Machine Learning
• Bayesian Inference
• Deep Learning
• +۵ مورد دیگر

Najmeh Abiri holds roles as a Research Engineer, Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), Postdoc in the Department of Statistics, and Researcher in Computational Science for Health and Environment at Lund University. She is affiliated with eSSENCE: The e-Science Collaboration. Her primary research interests include variational inference, Bayesian inference, deep learning, computational biology, and machine learning, contributing to UN Sustainable Development Goals related to environmental sustainability and health. Abiri has conducted impactful research on probabilistic multivariate time series imputation using diffusion models, disease risk mapping with AI, and quantum mechanics applications of deep learning. She has also pioneered denoising autoencoder techniques for handling missing data across diverse datasets. Notable outputs include a 2022 study predicting West Nile virus outbreaks using eco-climatic drivers and a 2021 paper on inverse problems in quantum mechanics. Her work spans interdisciplinary collaborations, addressing global challenges through data-driven approaches. Abiri organized workshops on AI/ML technologies and GAN applications, fostering academic engagement. She led the 2014-2019 project on statistical inference with deep latent variable models, underscoring her commitment to advancing computational methodologies in science and health.

Fredrik Johanssonمشاهده پروفایل

مدرس ارشد

• Neurosciences
• Cognitive Science
• Neurobiology
• +۳ مورد دیگر

Fredrik Johansson is an Associate Senior Lecturer at Lund University, affiliated with the Department of Associative Learning Cognitive Science. His research focuses on neurobiological mechanisms underlying learning, memory, and timing, particularly within cerebellar systems. He investigates classical conditioning pathways, alcohol's impact on neural learning processes, and Purkinje cell dynamics using electrophysiological and computational methods. Key research interests include cerebellar circuitry, synaptic plasticity, and the neural substrates of temporal processing. Collaborative work spans neurophysiology experiments and computational modeling to understand how cerebellar neurons encode duration and adapt to behavioral changes. His studies often involve in vivo electrophysiology and analysis of neural engram formation. Publications emphasize cerebellar learning mechanisms, with recent work exploring nucleo-olivary stimulation effects on climbing fibers and alcohol's role in disrupting conditioned responses. No scientific awards are explicitly mentioned, though his work contributes to foundational neuroscience knowledge. Advising and grants: No formal advisees or grant details provided in the text. Research is conducted within collaborative teams focusing on cognitive and neurobiological systems.

Ming Zhaoمشاهده پروفایل

مدرس

• Industrial Economics
• Statistics
• Reliability Engineering
• +۲ مورد دیگر

Ming Zhao is a Lecturer at the University of Gävle's Academy of Technology and Environment, teaching industrial economics, statistics, reliability engineering, and logistics simulation. Research focuses on reliability modeling, statistical methods, and quality management, with applications in software, manufacturing, and sensor networks. Key interests include masked data analysis for reliability estimation, copula-based modeling of dependent failures, and Bayesian methods for system reliability. Publications emphasize practical frameworks for software reliability and manufacturing system performance. Article trends show consistent innovation in reliability estimation techniques, particularly for systems with incomplete data. Recent work integrates statistical methods with industrial applications like reconfigurable manufacturing and wireless sensor networks. No awards, supervised students, or lab details are documented.

Sara Pudasمشاهده پروفایل

دانشیار

• cognitive aging
• long-term memory
• brain structure
• +۳ مورد دیگر

Sara Pudas is an Associate Professor at Umeå University's Department of Psychology, specializing in cognitive aging and long-term memory mechanisms. Her research investigates brain structure and function relationships, biological markers of aging, and lifestyle influences on cognitive health. Research focuses on longitudinal predictors of dementia, neuropsychiatric symptom trajectories, and heterogeneity in brain aging patterns. Work integrates neuroimaging, epigenetic biomarkers, and advanced statistical modeling to understand cognitive resilience and decline across the lifespan. Recent publications examine telomere dynamics, DNA methylation biomarkers for Alzheimer's prediction, and educational impacts on cognitive differences across European and US cohorts. Methodological contributions include Bayesian approaches for longitudinal data with dropout and explainable AI for clinical neuroimaging applications.

Jenny Häggströmمشاهده پروفایل

دانشیار

• Register-based research
• Causal inference
• Observational studies
• +۸ مورد دیگر

Jenny Häggström is an Associate Professor at the Umeå School of Business, Economics and Statistics (USBE), affiliated with the Department of Statistics at Umeå University. Her work focuses on method development and applications in register-based research , with expertise in causal inference, observational studies, and statistical modeling for medical and public health applications. Academic Affiliation: Umeå School of Business, Economics and Statistics (USBE), Umeå University Research Pillars: Causal inference, machine learning, health promotion, and high-dimensional data analysis Email: jenny.haggstrom@umu.se Research Themes : Häggström’s research bridges statistical methodology with public health and clinical applications. She leads the Stat4Reg research group, focusing on: Causal inference in observational time-to-event settings Development of risk prediction models for surgical outcomes Evaluation of universal health interventions like the Salut Programme Propensity score methods and bandwidth selection in kernel equating Recent Article Trends : Her recent work emphasizes: Medical statistics for surgical complications and survival analysis Machine learning applications in stroke outcome prediction Public health research on immigrant health disparities and preventive programs Methodological advancements in causal inference with high-dimensional data Projects & Grants : Active research projects include: Methodological development for marginal causal effects in time-to-event data (2019-2022) Evaluating the Salut Programme’s health and economic impact (2018-2020) Improving causal effect estimation in observational studies (2014-2016) Labs & Teams : She leads the Stat4Reg research group, collaborating on register-based studies and statistical methodology.

Maria Josefssonمشاهده پروفایل

دانشیار

• Statistics
• Gerontology
• Cognitive Aging
• +۶ مورد دیگر

Maria Josefsson is an Associate Professor (Senior Lecturer) and Docent in Statistics at Umeå School of Business, Economics and Statistics (USBE), Umeå University. She holds an affiliation as Research Fellow at the Centre for Demographic and Aging Research (CEDAR), also at Umeå University. Her research focuses on statistical methods for studying age-related cognitive change, including dementia risk prediction, longitudinal data analysis, and causal inference in aging cohorts. She leads the Stat4Reg research group and has contributed to projects such as RECO (rehabilitation for cognitive decline in stress-related disorders) and iPARK (Parkinson’s disease interventions). Her work integrates epidemiological approaches with statistical modeling to address cognitive aging mechanisms, including the role of herpesviruses, telomere dynamics, and epigenetic markers. Key areas include causal mediation analysis, sensitivity to non-ignorable dropout, and multivariate outcomes in longitudinal studies. She has also explored sociodemographic factors affecting mental health during pandemics and disparities in violence against indigenous populations. Josefsson has authored/co-authored over 50 peer-reviewed articles spanning statistical methodology, cognitive neuroscience, and public health. Her methodological contributions include Bayesian semiparametric approaches and pattern mixture models. She collaborates with multidisciplinary teams on projects such as the Betula longitudinal cohort study and blood-based biomarker evaluations for Alzheimer’s disease prediction.

Miguel Serras Vascoمشاهده پروفایل

پژوهشگر

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +۴ مورد دیگر

Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).

Stefan Engblomمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Methods
• Fast Multipole Methods
• +۳ مورد دیگر

Stefan Engblom is a Professor in Scientific Computing at Uppsala University, Department of Information Technology. His research focuses on developing computational methods and software for scientific applications, with particular expertise in numerical methods, fast algorithms, and simulation of complex systems. He is affiliated with the Scientific Computing division within the Information Technology department at Uppsala University. Engblom's research spans several key areas including fast multipole methods for efficient computation of long-range interactions, stochastic simulation of reaction-diffusion processes in complex geometries, computational epidemiology for modeling disease spread, and fluid dynamics simulations. His work combines theoretical development with practical implementation, resulting in several widely used software packages. His publications reveal a consistent focus on developing efficient numerical algorithms that address computational challenges in various scientific domains. The research shows a progression from fundamental algorithm development (fast multipole methods) to applications in biology (reaction-diffusion systems) and public health (epidemic modeling), demonstrating the versatility and applicability of his computational approaches. Engblom maintains active software development through several research codes including SimInf for epidemic modeling, URDME for reaction-diffusion processes, FMM2D/FMM3D for fast multipole methods, and other specialized tools for fluid dynamics and fiber simulations. His stenglib provides general-purpose Matlab libraries used in his research and available to the broader scientific community.

Torkel Erhardssonمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Statistical Inference
• +۷ مورد دیگر

Torkel Erhardsson is an Associate Professor (Docent) in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where he conducts research in probability theory, stochastic processes, and statistical inference. His research interests include: Bounds for distances between probability distributions using Stein's method and couplings Applications to normal and compound Poisson approximations Bayesian nonparametric inference and asymptotics of posterior distributions Higher-dimensional autoregressive processes with random coefficients Reciprocal chains and reciprocal random fields on undirected graphs His recent publications (2008–2023) in journals such as Journal of Applied Probability , Advances in Applied Probability , and IEEE Transactions on Automatic Control reflect a strong focus on theoretical probability and its applications, particularly in approximation methods and stochastic modeling. The work shows increasing emphasis on graphical models and reciprocal processes in recent years. There are no listed scientific awards or honors in the provided materials. Torkel Erhardsson actively contributes to the academic life at Linköping University, participating in seminars in statistics and mathematical statistics. There is no public information indicating student advising, grant funding, or leadership of specific research labs or teams.