معرفی
David Ohlin is a PhD student and part-time lecturer in Automatic Control at Lund University's Faculty of Engineering (LTH), actively contributing to the Wallenberg AI, Autonomous Systems and Software Program (WASP) since 2021. His position bridges advanced doctoral research with instructional responsibilities across multiple control theory courses.
He earned his MSc in Engineering Physics in 2021 with a thesis focused on machine learning applications for EEG signal interpretation, establishing his interdisciplinary approach at the intersection of control systems and biomedical engineering. His educational trajectory demonstrates a clear progression from foundational physics to specialized control theory expertise.
Ohlin's research centers on optimal control methodologies for large-scale networked systems, with innovative applications to social network dynamics and opinion modeling. His work on graph search methods for controlling positive systems and nonlinear synchronization models represents significant contributions to both theoretical control frameworks and their real-world implementations in social dynamics. This dual focus on mathematical rigor and practical application characterizes his scholarly approach.
His publication pattern reveals a growing expertise in network control systems, with recent work examining consensus formation in polarized networks, linear cost optimization, and heuristic search algorithms. These publications collectively demonstrate his ability to translate complex control theory concepts into solutions for networked social systems.
- Teaching since Fall 2021: Non-linear control, Learning-based control, and Network dynamics courses
- Research collaborations with prominent scholars including Anders Rantzer and Emma Tegling
- Active participant in WASP, Sweden's premier AI and autonomous systems initiative
