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Alireza Mohammadinodooshan is a Postdoctoral Fellow at Linköping University's Department of Computer Science (IDA), Sweden, working within the Database and Information Technology (ADIT) research group. He contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP) – Sweden's largest individual research initiative – focusing on data-driven analysis of social media engagement dynamics across Twitter, Facebook, and Instagram platforms. His research centers on quantifying how political bias, news reliability, and content-agnostic factors shape temporal user engagement patterns. Key interests include social media analysis, user engagement dynamics, data mining, information systems, network science, and artificial intelligence, with emphasis on cross-platform comparative studies and algorithmic amplification effects in news consumption. Analysis of his 15 most recent publications reveals consistent focus on temporal modeling of engagement decay, multi-format content interaction (photos/videos/albums), and the interplay between news source characteristics and user behavior. His methodological approach combines large-scale dataset analysis with network theory to identify virality predictors and platform-specific engagement mechanics. No scientific awards were documented in available sources. No advising roles or research grants were referenced in the provided materials. He operates within the ADIT research group at Linköping University, which specializes in advanced database and information systems for the digital society. This group forms part of IDA's broader WASP-affiliated ecosystem focused on AI-driven autonomous systems, enabling interdisciplinary collaboration on large-scale data challenges in social computing.

• Social Media Analysis
• User Engagement Dynamics
• Data Mining
• +3 more

Nils Fagerberg is a Lecturer at Linnaeus University's Department of Forestry and Wood Technology within the Faculty of Technology. He holds a PhD in Forest Industry Production Systems and focuses on sustainable forestry practices, particularly continuous cover forestry and individual-tree-selection methods. He works 60% at the university, teaching courses like Sustainable Family Forestry III and Hygge-Free Forestry, while dedicating the remaining time to his family farm and consulting in continuity forestry and beef production. His education includes a B.Soc.Sc. in Ecological Economics, M.Sc. in Forestry, and M. in Sustainable Development. His doctoral research investigated optimizing tree selection in uneven-sized forests to balance economic returns with environmental and social benefits. He collaborates with the Forest Management research group, addressing sustainable forest use through interdisciplinary approaches. Fagerberg’s publications emphasize quantitative models for tree growth, knot prediction, and optimization strategies in continuous cover forestry. He has contributed datasets from Linnaeus University’s field trials, enabling further research into spruce growth and canopy dynamics. His work bridges academic research with practical forestry applications, advocating for sustainable management practices.

• Sustainable Forestry Management
• Continuous Cover Forestry
• Individual-Tree-Selection Methods
• +3 more