جست‌وجوی استاد و پژوهشگر

• 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.

• 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.

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.

Fredrik Janssonمشاهده پروفایل

دانشیار

• Cultural Evolution
• Social Psychology
• Group Dynamics
• +۴ مورد دیگر

Fredrik Jansson is an Associate Professor and Guest Researcher at Stockholm University's Department of Psychology, affiliated with the Centre for Cultural Evolution. His work focuses on cultural evolution mechanisms, group dynamics, and mathematical modelling of societal phenomena. Key research areas include cultural systems interdependencies, segregation patterns, norm change driven by moral foundations, and democratic transitions. He develops agent-based simulations to study how cultural traits spread and interact, emphasizing combinatorial properties and structural dependencies. Publications highlight innovative methodologies like analyzing ordinal data sequences and using register data to infer social clustering. His research bridges evolutionary theory with social sciences, addressing questions like how cultural traits form systems and how social consensus shapes diversity preferences. Recent work examines rapid democratic transitions and the role of gender rights in institutional shifts. Fredrik collaborates internationally, contributing to interdisciplinary projects on creole language evolution and the evolutionary basis of ethnocentrism. His advisory work and grants focus on computational social science and policy implications of cultural dynamics. The Centre for Cultural Evolution serves as his primary research hub, fostering collaborations across psychology, anthropology, and political science.

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.

Joakim Edsjöمشاهده پروفایل

استاد

• Theoretical Physics
• Astroparticle Physics
• Dark Matter
• +۳ مورد دیگر

Joakim Edsjö is a Professor of Theoretical Physics at the Department of Physics (Fysikum) , Stockholm University , focusing on astroparticle physics and dark matter research. He actively participates in both research and education, serving as section dean for the mathematical-physical section at the Faculty of Science since 2024. Research Interests: Dark Matter, Supersymmetry, Neutrino Detection, Gamma-Ray Astronomy, Computational Physics Teaching: Quantum Mechanics Summer Course (FK5033) Projects: Co-developer of DarkSUSY , WimpSim , and GAMBIT software packages Research Trends: His publications focus on dark matter annihilation signals across multiple astrophysical contexts, including solar neutrino analysis, gamma-ray phenomenology, and computational tool development for beyond-standard-model physics. Key methodologies involve Monte Carlo simulations, cross-experiment data fitting, and neutrino oscillation modeling. Leadership: Previously chaired the Natural Sciences Area's undergraduate education committee (2016-2023) and leads pandemic-era teaching transformation studies comparing global university responses.

• Cognitive Psychology
• Judgment and Decision Making
• Probability Judgment
• +۵ مورد دیگر

Peter Juslin is a Professor at Uppsala University, affiliated with the Department of Psychology and the Division of Perception and Cognitive Psychology. His research focuses on cognitive psychology, judgment, and decision-making, particularly on phenomena like overconfidence, the naivety of intuitive statistics, and additive probability integration. He collaborates with interdisciplinary teams across psychology, law, and energy systems. PhD in Psychology (1993), Uppsala University Bachelor of Arts (1989) in Psychology and Philosophy, Uppsala University His research explores how humans make probability judgments, the role of similarity-based processes, and applications to real-world domains like legal decision-making and energy consumption. He critiques traditional heuristics-and-biases approaches, proposing instead that naive sampling models explain judgment errors more effectively. Recent work examines cognitive processes in demand response, environmental psychology, and associative models in probability estimation. Key themes include cognitive constraints, sequential decision-making, and the interplay between intuitive and analytic reasoning. Scientific awards include the Torgny Segerstedt Medal (2008), Oscar Award (1998), and New Scientist Award (1994). He was elected to the Royal Swedish Academy of Sciences in 2013. He teaches advanced courses in cognitive psychology, supervises students at all levels, and engages in public science through articles and lectures to governmental agencies.

Erik Schaffernichtمشاهده پروفایل

مدرس ارشد

• Robotics
• Sensor Systems
• Machine Learning
• +۶ مورد دیگر

Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).

Håkan Nilssonمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Decision Making
• Probability Judgment
• +۴ مورد دیگر

Håkan Nilsson is a Senior Lecturer/Associate Professor at the Department of Psychology, Uppsala University, specializing in Perception and Cognition. His research focuses on cognitive psychology, decision making, and probability judgment with significant contributions to understanding human reasoning under uncertainty. His primary research interests include: Cognitive biases in probability judgment, particularly the conjunction fallacy How people interpret odds in sports betting contexts Mathematical modeling of decision processes (configural weighted average model) The relationship between numerical ability and decision quality Development of debiasing techniques for probabilistic reasoning Analysis of his recent publications reveals consistent focus on understanding probability judgment mechanisms. His work demonstrates that even numerically skilled individuals remain susceptible to decision paradoxes, suggesting deep-rooted cognitive processes. Notable research areas include how betting odds are converted to probability estimates and why combo bets appear more attractive despite lower probabilities. His scientific contributions encompass: Development of the configural weighted average model for probability judgment Studies on the relationship between numerical ability and susceptibility to decision paradoxes Research on the cognitive substrate of subjective probability Exploration of how monetary incentives affect probability assessment Nilsson has maintained extensive collaborations with Peter Juslin, Anders Winman, and Patric Andersson. His work appears in leading journals including Psychological Review, Journal of Experimental Psychology, and Cognition, demonstrating significant impact in cognitive and decision sciences.