Design of digital systems for High-energy physics applications:- AI-embedded digital systems for quantum readout and control.- Automation of synthesis and implementation of machine learning models pipeline, from python to silicon.- Digital systems for control at cryogenic temperatures (4K)- Smart-silicon imaging electronics for particle tracking, detection and classification in high-energy physics experiments. - Radiation-resilient systems for hard-rad environments in particle accelerators.

Design and consulting services on distribution power systems, sensing & control systems and telemetry interfaces.

Research, design and implementation of advanced deep learning techniques for oil & gas reservoir data characterization and petrophysical properties extraction.- Convolutional workflows for seismic volumes processing and characterization (generation, regression and classification). – Convolutional workflows for borehole image data characterization (regression and classification).– Development of deep generational models for reservoir data generation and simulation.– Development of unsupervised and semi‐supervised deep models for automatic reservoir identification.– AI for petrophysical flux parameters estimation.– Uncertainty estimation and characterization with deep bayesian models.– Development of advanced processing tools for artifact removal in borehole images (Patented). – Development of imaging tool for multi‐frequency spectra fluid characterization.

Application of Deep learning and artificial intelligence methodologies for cosmic microwave background (CMB) signal decomposition, reconstruction and generation (PhD. Brian Nord, host) Characterisation of Dark energy survey silicon (DESI) CCDs for dark matter observation in space and discussion on application of artificial intelligence on-chip for smart sparse processing (PhD. Juan Estrada, host).