Francesco works at AID in the Machine Learning group as tech lead and engineer, primarily on perception and computer vision problems. He has worked in Machine Learning research and development for startups and large corporations. He has a PhD in statistical physics and a MEng in Engineering
Tech Lead, ML Engineer
Proudly presenting the paper:
Sampling-free Epistemic Uncertainty Estimation
Using Approximated Variance Propagation
Session: Future Representations, Poster 1, Paper ID 1774
Wednesday, October 30 09 – 10:30 am Oral 2.1A (Hall D1)
Our Perception teams develop the algorithms and write the software that senses the world around our self-driving cars and predicts what it will look like in the seconds ahead. The team extensively uses Artificial Intelligence techniques like Deep Learning, as well as our own in-house built software to process camera pixels, Lidar point clouds and radar echoes to model the vehicle’s environment and detect vehicles, pedestrians and other obstacles. The team is responsible managing the training of our Neural Networks and Machine Learning algorithms, including the creation of ground truth datasets out of the vast quantities of data available from our
Featured Perception Job openings
Principal Engineer – Perception
Laying down the technical vision and architecture for a cutting-edge pipeline the Principal Engineer will be driving R&D within perception, resulting in publications in peer-reviewed journals and contributions to the academic community. Through your technical leadership and in conjunction with Line Management, the Principal Engineer will be building an industry-leading team of up to 70 engineers to execute and deliver this vision.
As part of the machine learning team at AID you will work with one or more domain teams to solve specific problems with the help of your unique knowledge and the Data we gather from our development fleet. This will include the creation and evaluation of ground truth datasets, modelling and training of our machine learning components on the compute backend and optimizing the performance on our embedded platforms.