Laurens Bliek
Papers
1
Total Citations
2
H-Index
1
About
Laurens Bliek is a researcher whose work lies at the intersection of online optimization, machine learning, and data-driven control. His key research areas include developing algorithms for optimizing unknown, expensive-to-evaluate functions under noisy conditions, with a particular focus on real-time and iterative decision-making. His most notable contribution is the "Data-based Online Nonlinear Extremum-seeker" (DONE) algorithm, introduced in his 2016 paper on online optimization with costly and noisy measurements using random Fourier expansions. This work provides a principled framework for maintaining a surrogate model of an unknown function via random Fourier features, enabling efficient optimization even when feedback is limited or corrupted. While his citation count is modest, the conceptual novelty of his approach—bridging online learning with surrogate-based optimization—has laid groundwork for applications in adaptive control and system identification. Bliek’s research is particularly valuable for students and practitioners tackling real-world problems where experimentation is expensive, such as in robotics, chemical process control, or autonomous systems. His work demonstrates how combining probabilistic modeling with online learning can yield practical, robust solutions for dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1