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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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