Working with Schlumberger on improving uncertainties estimates in a Bayesian framework, on the measures obtained with the Nuclear Magnetic Resonance on porous media. Michele obtained his MSc degree in Mathematics at the Catholic University of Brescia in 2017, with a final project related to the description of the time evolution of the traffic flow at a highway entrance ramp, based on the non-local conservation laws theory. He worked for a year in a global consulting firm in the financial area before joining SAMBa, following his passion for mathematics applied to real world problems. Apart from mathematics, Michele is interested in eating (and sharing my home country food), running (with some breaks to eat berries), learning languages and travelling.

Research project title:
Bayesian inference for low-resolution Nuclear Magnetic Resonance in porous media

Supervisor(s):
Silvia Gazzola, Tony Shardlow and Edmund Fordham

Project description:
Nuclear Magnetic Resonance is used to infer properties of porous media, such as rocks, through which oil can be extracted. Michele’s research project aims to surpass the current standard inference methodology by providing uncertainty estimates alongside state estimates in an efficient manner and to develop the technique for shales. Working with Schlumberger, this will be achieved through reformulating the problem in a Bayesian framework and applying tools from numerical linear algebra.