Team

Josep Escrig

Trustworthy AI Line Manager - Distributed Artificial Intelligence Research Group

Contact josep.escrig@i2cat.net
Dr. Josep Escrig is the *Research Line Manager for "Trustworthy AI"* within the Distributed Artificial Intelligence ({*}DAI{*}) research group at the I2CAT Foundation.

He holds a *PhD in Fluid Mechanics* (University of Nottingham) and degrees/master’s in *Mechanical Engineering* (INSA of Toulouse), {*}Industrial Engineering{*}, and *Sustainability and Energy Efficiency* (University of Jaume I).

Josep is the founder of the successful {*}DAI group{*}, which he led for six years. Recognized and funded by the Generalitat de Catalunya, the group now comprises {*}24 researchers{*}. Under his direction, the DAI group has participated in *over 20 European projects* and {*}more than 30 projects with public and private companies{*}.

His professional experience spans advanced engineering and data science. He held a postdoctoral position focusing on *AI for smart sensors* (University of Nottingham). His academic output includes an *h-index of 10* with {*}over 20 publications on AI{*}.

Publications

Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems

Oliver J Fisher, Nicholas J Watson, Josep E Escrig, Rob Witt, Laura Porcu, Darren Bacon, Martin Rigley, Rachel L Gomes, "Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems", Computers & Chemical Engineering, Volume 140, 2020. https://www.sciencedirect.com/science/article/abs/pii/S0098135419308373

Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes

J. Escrig, E. Woolley, A. Simeone, N.J. Watson, "Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning", Food Control, Volume 116, 2020,

Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning

Escrig, J., Woolley, E., Simeone, A., & Watson, N. J. (2020). Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning. Food Control, 107309. https://doi.org/10.1016/j.foodcont.2020.107309