Since September 2023, I am a post-doc at the Grenoble Institute of Neuroscience.

I am working on a new MRI acquisition technique called MR Fingerprinting. This technique makes it possible to estimate several physiological parameters from a single MRI acquisition, whereas conventional examinations require one acquisition for each parameter.

The MRF method could thus make it possible to considerably reduce the time required for MRI examinations, which in turn would increase the rate of examinations and make MRI more useful in emergency situations such as stroke.

My thesis was carried out from September 2020 to August 2023. I defended it on Thursday, July 20, 2023 at ÉNS Lyon. The manuscript is available here and the slides here.

The context of bandit problems is the following: consider $K$ distincts probability distributions ${\nu }_1,\dots ,{\nu }_K$. Those distributions are unknown but at each step you are able to select an arm $1\le k\le K$ and obtain the value of an independent realization of ${\nu }_k$. You can define the strategy you want (that is to say choose the next arm to observe by using all the previous observations).

There are several mathematical objectives. For instance, in Best Arm Identification, the goal is to identify the best arm, which is the arm with highest associated expectation. There are two settings:

• in the Fixed Confidence setting, you have a confidence level $\delta \in ]0,1[$ and you need to find a strategy that identify the best arm with probability at least $1-\delta$. The objective is then to minimize the expectation of the number of observations required by the strategy.
• in the Fixed Budget setting, you are given a fixed number of observations $n\in {\mathbb{N}}^{\ast }$ and you have to find a strategy that maximizes the probability of returning the best arm after those observations.

For more information about bandit problems the book of Tor Lattimore and Csaba Szepesvári is a good introduction.