About me

Research interests

My research interests cover a wide range, from the calibration of the tracker to jet measurements. I have expertise with global fits including a large number of parameters. More recently, I have also developed interest for the development for future detectors and for data science.

Quantum Chromodynamics

I have been involved in several jet measurements at CMS with Run-2 pp collision data at 13 TeV for several years. In particular, I wrote my thesis on the inclusive b~jet production. I have developed experience in multi-differential unfolding with different approaches (D'Agostini, Tikhonov, ...). Currently, I am involved in several measurements of the strong coupling and extraction of PDFs. In particular, I played a leading role in the recent publication on inclusive jet production at 13 TeV with CMS data; further analyses will appear soon. Together with Radek Zlebcík, I am also the author of several generic analysis tools and techniques, such as the tests of smoothness.

Tracker alignment

The CMS detector features in its innermost layers the largest silicon tracker in the world, both in size and granularity. Its alignment is a challenging problem, with several hundreds of thousands of parameters to fit. I have been heavily involved in and coordinating the alignment of the tracker over the whole Run-2, and coordinated the recent publication over alignment performance and strategies of the CMS silicon tracker during LHC Run-2. I am in regular contact with other collaborations to discuss generic alignment issues (Belle-2, MUonE, CBM, ...). Currently, I am Tracker DPG convener in the CMS Collaboration.

R&D for CMS Tracker upgrade

At DESY, I was contributing to the development of a non-invasive characterisation of the support structures for the PS and 2S modules in the end-caps of the Phase-2 tracker, in particular to investigate the cooling of the structure. Understanding the cooling process and the design of the tracker is essential to prepare optimally the alignment of the future tracker in operations for Run-4.

Data science

With extremely large statistical samples, High Energy Physics is an ideal environment to develop and practice new approaches in supervised and unsupervised Machine Learning. In particular, I am interested in the refinement techniques to let fast simulations reach the quality of full simulations relying on Geant4. One bachelor thesis and one master thesis were achieved under my supervisions: both obtained the highest possible score, and the first one was even awarded.

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