جست‌وجوی استاد و پژوهشگر

• Predictive Modelling
• Intensive Care Medicine
• Anesthesiology
• +۳ مورد دیگر

Peder Andersson is an affiliated researcher at Lund University's Division of Clinical Chemistry and Pharmacology, part of the Faculty of Medicine. He also serves as a teaching staff member in the Medical Programme's Mathematical Statistics section. His research focuses on predictive modeling in intensive care medicine, particularly around ICU prognostication, cardiac arrest outcomes, and biomarker-driven mortality prediction. He holds an ORCID identifier (0000-0001-8261-9613). His work emphasizes artificial neural networks for clinical prediction systems and improving risk stratification in critical care settings. Notable projects include register-based studies on ICU patient outcomes and validation of machine learning algorithms for neurological prognosis after cardiac arrest. Collaborations span multiple institutions, with active research networks involving co-authors from Sweden and Denmark. His research has garnered significant readership on platforms like Mendeley, with over 150 readers for his 2019 cohort study.

• 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.

• Physical Activity Assessment
• Public Health
• Exercise Physiology
• +۳ مورد دیگر

Patrick Bergman is a Professor at Linnaeus University, affiliated with the Faculty of Health and Life Sciences and the Department of Medicine and Optometry. He leads the master's program in eHealth and specializes in teaching scientific methods, particularly quantitative approaches. His research focuses on physical activity assessment and its relationship with health, leveraging digital technologies for population-level monitoring and personalized health interventions. Key projects include Iron(wo)man, HERO, and Longlife Active, addressing topics like biomarker analysis in endurance athletes, sedentary behavior in the elderly, and AI-driven healthcare solutions. His research interests span physical activity assessment methodologies, public health, and digital health innovations. Notable contributions include studies on accelerometer-based data protocols, environmental impacts on activity patterns, and AI applications in diagnostics. Collaborations with healthcare sectors and industry emphasize practical translation of research. He holds a PhD from Karolinska Institutet (2009) and has contributed to over 100 peer-reviewed publications, emphasizing interdisciplinary approaches to health science. Current projects explore medication risk prediction, digital health testbeds, and outdoor exercise benefits. His work bridges clinical research, technology, and policy, aiming to improve health outcomes through evidence-based strategies.

Volker Kruegerمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Volker Krueger is Professor at Lund University's Department of Computer Science, with joint appointments in Robotics and Semantic Systems. He leads research at the intersection of artificial intelligence, computer vision, and autonomous systems through the ELLIIT initiative and NEXTG2COM Competence Centre. His research group focuses on developing robust perception and decision-making systems for industrial and service robotics applications. Research interests span robot perception under challenging conditions, uncertainty quantification in machine learning models, and human-robot interaction frameworks. Recent projects investigate vision-language models for robotic failure handling, domain-specific languages for robot programming, and optimization methods for logistics systems. His laboratory coordinates multiple European projects including RIACT (Radical Innovation of Adaptive Cobot Technology) and NextG2Com (Next Generation Communication Infrastructures). He actively promotes robotics education through initiatives like the Robotics Week for Schools and maintains extensive industry collaborations in manufacturing and automation sectors.

• Supply Chain Management
• Operational Development
• Data Analysis
• +۵ مورد دیگر

Ola Johansson is a Senior Lecturer at the Faculty of Business, Kristianstad University. With over 10 years of academic experience, he bridges industry and academia, having previously worked at ABB and Ericsson in operational development roles focusing on Lean and Six Sigma methodologies. His research integrates data analysis, logistics systems, and sustainability, with a focus on supply chain management, recycling behavior, and IoT applications. He contributed to the SmartVA project (2021–2023), exploring real-time sewage water monitoring via IoT. Key research areas include: Supply Chain Optimization and Auto-ID Technologies Consumer Recycling Behavior Analysis Transportation Emissions Reduction Strategies IoT-Driven Environmental Monitoring Logistics Performance Benchmarking He has organized multiple conferences and workshops, including the RECS Workshop on Artificial Intelligence and IoT (2022) , fostering interdisciplinary collaboration. His work aligns with UN Sustainable Development Goals related to Industry, Innovation, and Climate Action. Notable projects include: SmartVA: Flood Risk Mitigation via Sewage Data Analysis Benchmarks for Auto-ID in Swedish Manufacturing (2009) IoT Applications in Waste Management (2006–2008) Publications span peer-reviewed journals like Benchmarking and Journal of Solid Waste Technology and Management , emphasizing empirical and simulation-based research. His teaching integrates interactive technologies, such as student response systems, to enhance learning outcomes.

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.

Mohammad Ghasempourمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Causal Inference
• Health Inequalities
• +۳ مورد دیگر

Mohammad Ghasempour is a Research Fellow at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, specializing in the Department of Statistics. His work focuses on advancing machine learning techniques for causal inference and addressing health inequalities through statistical methodologies. Education & Affiliations: Postdoctoral Fellow at USBE, Umeå University Research Interests: His research emphasizes integrating machine learning with causal inference to tackle complex problems in health disparities and statistical efficiency. He explores applications such as convolutional neural networks for causal analysis and developing R packages for causal dimension reduction. His work bridges computational methods and real-world challenges in public health and labor market studies. Research Projects: Machine learning to study causality with big datasets (2022–2026): Focuses on valid statistical conclusions via scalable methods. Statistical models for life trajectories (2017–2021): Analyzes labor market and health dynamics using advanced statistical frameworks. Grants & Collaborations: Member of the Stat4Reg research group, contributing to interdisciplinary projects at the intersection of statistics and societal challenges. Labs/Teams: Active in the Stat4Reg group, advancing statistical methodologies for regulatory and applied research.

Xavier de Lunaمشاهده پروفایل

استاد

• Causal Inference
• Machine Learning
• Statistics
• +۵ مورد دیگر

Xavier de Luna is a Professor of Statistics at Umeå School of Business, Economics and Statistics (USBE) at Umeå University in Sweden. He leads the Stat4Reg Lab and is part of the Umeå SIMSAM Lab and the SINGS graduate school. His research focuses on causal inference, machine learning, and statistical methodology applied to social and health sciences. He serves as Editor of the Scandinavian Journal of Statistics (2025-2027) and has held roles as Associate Editor for Biometrics, Computational Statistics and Data Analysis, and Observational Studies. Education: PhD in Statistics from Swiss Federal Institute of Technology (1996). Prior roles include Head of the Department of Statistics at Umeå University (2009-2011) and affiliation with institutions like IFAU and the Institute of Labour Market Policy Evaluation. Research interests span causal machine learning, covariate selection, and register-based studies in health and social sciences. Key projects include using generative AI for open science and machine learning for causal discovery. His work addresses challenges like unobserved confounding and high-dimensional data. Notable contributions include methodological advances in causal inference (e.g., covariate balancing, neural networks for causal effects) and applications in health economics, retirement effects, and brain imaging studies. He has advised over 15 PhD students and secured grants from the Swedish Research Council and Wallenberg Foundation. Labs/Teams: Stat4Reg Lab (causal inference), Umeå SIMSAM Lab (register-based research), SINGS (interdisciplinary graduate school).

Alexander Friedrichمشاهده پروفایل

پژوهشگر ارشد

• Geometric Deep Learning
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Alexander Friedrich serves as a Research Fellow in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, with his office located at MIT-huset, plan 3, room MIT.D.317 in Umeå (901 87). He is actively affiliated with the Geometric Deep Learning research group, focusing on advancing methodologies for non-Euclidean data structures. His research centers on Geometric Deep Learning , which extends neural network architectures to irregular domains like graphs and manifolds. This work bridges theoretical mathematics with practical applications in artificial intelligence, particularly in computer vision and network analysis, emphasizing geometric representations for structured data. The field integrates differential geometry, graph theory, and machine learning to develop novel algorithms for complex data types. As a core member of Umeå University's Geometric Deep Learning research group, Dr. Friedrich contributes to interdisciplinary collaborations exploring real-world implementations of geometric learning techniques. The group investigates foundational theories and applications across scientific domains, including molecular chemistry and social network modeling, leveraging mathematical statistics to enhance deep learning frameworks for non-Euclidean data.

• Robotics
• Computational Geometry
• Topology in Robotics
• +۳ مورد دیگر

Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.

Fredrik Strandمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Medical Imaging
• Breast Cancer Research
• +۳ مورد دیگر

Fredrik Strand is a researcher at the Karolinska Institute , specifically within the Department of Oncology-Pathology under the Faculty of Medicine . He leads the Computational Breast Imaging Group , focusing on leveraging Artificial Intelligence (AI) for early breast cancer detection , precision diagnostics , and image-guided minimally invasive procedures . His work spans cross-disciplinary collaboration with institutions like the Royal Institute of Technology (KTH) and involves major clinical trials such as ScreenTrustCAD and ScreenTrustMRI . His research emphasizes AI in precision screening to address interval cancers and optimize radiologist workflows. Projects like VAI-B and RadioVal aim to validate AI algorithms and predict therapy response for neoadjuvant treatment . Recent publications highlight advancements in multi-modality imaging (mammography, MRI), GAN-based synthetic data generation , and trustworthy AI frameworks for clinical deployment. A consistent focus is on cost-effectiveness , equity in AI diagnostics , and human-AI interaction dynamics . Key trends in his work include deep learning for tumor detection , reinforcement learning for screening policies , and multi-institutional validation of AI tools. His research is funded by organizations such as Medtechlabs , Vinnova , EU Horizon Europe , and the Swedish Breast Cancer Association .

Mehran Ebrahimianمشاهده پروفایل

استادیار

• Household Finance
• Macro-Finance
• Human Capital
• +۳ مورد دیگر

Mehran Ebrahimian is an Assistant Professor in the Department of Finance at Stockholm School of Economics (SSE) and holds the Eva and Mats Qviberg Research Fellowship at the Swedish House of Finance. His academic journey includes a PhD in Finance from the Wharton School of the University of Pennsylvania, a M.Sc. in Economics, and a B.Sc. in Physics from Sharif University of Technology. His research focuses on Household Finance, Macro-Finance, and Human Capital, examining how financing frictions affect economic inequality and real-world outcomes. Key themes include student loan policies, credit access in low-income regions, and the interplay between financial development and international trade. Notable contributions include analyzing tuition-free college policies' unintended wealth transfers and demonstrating how credit supply shocks disproportionately impact entrepreneurship in economically disadvantaged areas. His methodology innovations, such as robustness checks in structural models, have advanced computational economics. Current work explores preferences' role in wealth mobility and the redistributive effects of free higher education. His interdisciplinary background in physics and economics informs quantitative approaches to complex financial systems and policy design.

Riccardo Sabbatucciمشاهده پروفایل

دانشیار

• Empirical Asset Pricing
• Forecasting
• Dividends and Payout Policy
• +۵ مورد دیگر

Riccardo Sabbatucci is an Associate Professor at the Department of Finance, Stockholm School of Economics (SSE) and a Visiting Associate Professor at the Wharton School, University of Pennsylvania. He is also a Research Fellow at the Swedish House of Finance (SHoF). His research focuses on empirical asset pricing, using statistical tools to analyze stock prices, fund dynamics, and macroeconomic impacts. Recent work addresses pandemic effects on financial markets, high-frequency data forecasting, and retirement accounts’ role in wealth inequality. Education: PhD in Finance from the University of California, San Diego (2016); MSc and BA in Economics from Bocconi University. He teaches Quantitative Modeling of Asset Prices in SSE’s MSc program and supervises theses. Notably, he received the 2021 Geneva Institute Award for his paper on synthetic risk factors. His research has been published in top journals like the Journal of Finance and Review of Financial Studies . Key research themes include return predictability, dividends, retirement plan dynamics, and machine learning applications. He is currently on sabbatical and maintains the Corporate Activity Tracker ( https://corporateactivitytracker.com ). Contact: Riccardo.Sabbatucci@hhs.se or rsab@wharton.upenn.edu .

Erik Elmrothمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Erik Elmroth is a Professor at the Department of Computing Science, Umeå University. He leads research in distributed systems, cloud/edge computing, and autonomous resource management, directing a 30+ member research group. His leadership includes transformative roles as department head (2009-2021) and Deputy Director at High Performance Computing Center North (HPC2N). Elmroth serves on executive committees for the SEK 6.2B Wallenberg AI program (WASP) and SEK 390M eSSENCE initiative, and leads multiple Kempe Foundation projects. Research interests center on: Autonomous control of cloud/edge infrastructures Software-defined systems and federated clouds AI-driven resource optimization High-performance computing architectures Robust machine learning for distributed environments His publications emphasize adaptive cloud systems, anomaly detection, and federated learning, with consistent focus on scalability and resilience in edge/cloud deployments. Awards and honors: Member of Royal Swedish Academy of Engineering Sciences (IVA) Nordea Scientific Prize (2011) SIAM Linear Algebra Prize (2000) National HPC Lecturer appointment He has supervised 40+ PhD students and secured major grants including the Swedish Research Council's second-largest award for the Cloud Control project. Elmroth founded the Control Workshops series and co-founded Elastisys AB, a cloud security firm with 50+ employees recognized as Umeå's Spin-off Company of the Year.

Polina Kurtserمشاهده پروفایل

دانشیار

• Machine Learning
• Robotics
• Agricultural Robotics
• +۴ مورد دیگر

Polina Kurtser is an Associate Professor at the Department of Computing Science, Umeå University. Her research focuses on machine learning, robotics, and sensor systems, particularly in agricultural and autonomous applications. She has held postdoctoral positions at AASS (Örebro University) and the Department of Radiation Sciences at Umeå University. PhD in Computer Vision and Agri-Robotics (Ben-Gurion University, 2019) Postdoctoral positions at AASS and Radiation Sciences, Umeå University Her work spans agricultural robotics, including sweet pepper harvesting, grape vine detection, and mechanical stress induction in plants. She also explores illumination-independent target detection, dynamic sensing strategies, and statistical models for fruit detectability. Recent publications highlight her contributions to color-to-thermal AI for building inspection, multi-camera vision for gesture recognition, and RGB-D datasets for agricultural perception. Her research integrates machine learning, sensor fusion, and autonomous navigation in challenging environments. She teaches deep learning, machine learning, robotics, and optimization. Her academic affiliations include collaborations in gastronomy (sensory evaluation of robot-cultivated basil) and medical robotics (e.g., ECG chaotic component detection via neural networks).