Research, development and deployment of predictive maintenance models for the automotive industry, more specifically, heavy duty trucks. Driving fleets to zero downtime.

Integrated the E2E Ground Segment Testing team. Driven a pioneer project that aimed at identifying relevant information regarding the health of the mission control systems chain, by interpreting the backlog of system messages. Association learning, word embedding, natural language processing and recurrent neural network techniques were scrutinised for the purpose of classification of types of failures.

Research fellowship proposal and validation of a novel quality prognostics model to be integrated within the existing real time sensing and pre-processing sub-systems at the equipment level of an automated riveting process. (published on IEEE Aerospace Conference 2018)