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  • Machine Learning
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Daniel Robert-Nicoud

Daniel Robert-NicoudDaniel Robert-NicoudDaniel Robert-Nicoud

Research interests

I am currently pursuing a Diploma of Advanced Studies in Data Science with specialization in Machine Learning at ETH Zurich. My topics of interest are Gaussian processes, reinforcement learning, and deep learning. I am also currently collaborating on two research projects in pure machine learning.


  1. Geometric Gaussian processes. Defining valid kernels for Gaussian processes with values in tangent vector fields on manifolds, in particular respecting the geometry of the manifold. As an example, one can think about modelling the wind on the Earth, where the base manifold is the sphere and the wind is tangent to the surface of the Earth.
  2. Active-measurement reinforcement learning. Developing algorithms for solving active-measurement problems, that is to say reinforcement learning problems where the agent does not automatically know its state, but can actively decide to measure it, for a cost. This has many potential applications, for instance in medical RL (where performing analyses on a patient can be expensive or even dangerous) and infrastructure monitoring and maintenance (where analyzing structural soundness of infrastructure can be very expensive).


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