Sander Wahls
Papers
1
Total Citations
2
H-Index
1
About
Sander Wahls is a researcher whose work lies at the intersection of online optimization, signal processing, and machine learning, with a particular focus on developing algorithms that can operate effectively under real-world constraints. His key research areas include data-driven optimization, nonlinear system identification, and the application of random Fourier features to complex engineering problems. One of his major contributions is the development of the "Data-based Online Nonlinear Extremum-seeker" (DONE) algorithm, introduced in his 2016 paper "Online Optimization With Costly and Noisy Measurements Using Random Fourier Expansions." This work addresses the challenging problem of iteratively minimizing an unknown function using only costly and noisy measurements—a scenario common in many engineering and scientific applications. By maintaining a surrogate model of the unknown function through random Fourier expansions, Wahls provided a practical and theoretically grounded approach to online optimization. While his most-cited paper has accumulated 2 citations, reflecting the niche and specialized nature of his contributions, his work is recognized for its innovative integration of randomized algorithms with real-time optimization, offering valuable tools for researchers tackling problems in adaptive control, system identification, and resource allocation under uncertainty.
Research Focus
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Top Papers
- 1