After graduating in Physics in the Complutense University of Madrid with master's courses in Temple University, I turned to technology and now I have over 30 years of experience in software development, systems integration, and project, and product management. I have worked for all kinds of companies, highlighting my time at HP and more recently at Samsung Research where I had the opportunity to apply all my experience in deep learning-related projects. I’m currently Anyverse's product technical expert and point of contact for applications such as sensor simulation or deep learning training using advanced hyperspectral synthetic data.
What I love about my job:
I’m in that rare place where I can create solutions with amazing computer graphics technologies to help AI machine learning models understand reality. How cool is that? I get to work with extremely intelligent people in sectors like automotive that are in the forefront of innovation.
Solution Study
Thursday, June 19
04:00 pm - 04:30 pm
Live in Berlin
Less Details
What is in-cabin monitoring systems’ ultimate goal? That’s right, you guessed it: Safety.
If there is one thing that perception systems need when safety is at stake, that is high levels of accuracy. Many in-cabin monitoring systems are camera-based, deep learning-powered perception systems to detect all kinds of objects and situations that can happen inside a vehicle under different circumstances. Accuracy, in this context, means the confidence the deep learning models give you in detecting what you need in each case. The metrics typically used to measure that confidence are the precision and recall of the model when facing a test dataset.
Highlights from the session: