Job: PhD Studentships, TU Delft / Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions

Sander just sent me the following:

Dear Igor,

I have two vacancies for PhD students in

Applied Nonlinear Fourier Analysis for Fiber-Optic Communication / Water Wave Analysis

here at TU Delft that I hope might be of interest to some of your readers. More information can be found in the flyer at

http://www.dcsc.tudelft.nl/~swahls/pdf/PhD_Positions_NEUTRINO.pdf

It would be great if you could post them in your (fantastic!) blog.

Best, Sander

--

Dr.-Ing. Sander Wahls
Assistant Professor at TU Delft

http://www.dcsc.tudelft.nl/~swahls

 So you'd think that Sander is just flattering me and the blog into getting a post out to hire PhD students but you'd be wrong. He does very interesting work, check this recent one:

Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions by Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen, Sander Wahls

This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements. The algorithm maintains a surrogate of the unknown function in the form of a random Fourier expansion (RFE). The surrogate is updated whenever a new measurement is available, and then used to determine the next measurement point. The algorithm is comparable to Bayesian optimization algorithms, but its computational complexity per iteration does not depend on the number of measurements. We derive several theoretical results that provide insight on how the hyper-parameters of the algorithm should be chosen. The algorithm is compared to a Bayesian optimization algorithm for a benchmark problem and three applications, namely, optical coherence tomography, optical beam-forming network tuning, and robot arm control. It is found that the DONE algorithm is significantly faster than Bayesian optimization in the discussed problems, while achieving a similar or better performance.

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