Fabio Arnez

I’m a research engineer at the CEA-List Institute, Laboratory of Embedded and Autonomous Systems (LSEA), in France, where I delve into the fascinating world of trustworthy deep learning for automated systems. I actively contribute to cutting-edge, industry-driven projects on Trustworthy AI, funded by the European Union and the French government. My work is not just a job; it’s a mission to ensure that the AI technologies shaping our future are both innovative and reliable.

I hold a Ph.D. in Computer Science from Université Paris-Saclay, France, which has provided me with a solid academic foundation that fuels my professional endeavors. My main research interests lie in uncertainty estimation in DNNs, out-of-distribution detection in DNN-based computer vision components, LLMs & VLMs hallucination detection, and world models. My work is particularly focused on embodied AI for automated robots and autonomous vehicles applications, where precision and reliability are paramount.

My journey in the tech world began with a BSc. in electronis and telecommunications from Universidad Privada Boliviana (UPB), in my home country, Bolivia. Then, I was fortunate enough to win the RETECA foundation scholarship to pursue my MSc. studies in Switzerland. By following an MSc. in embedded systems and microelectronics from the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), I first honed my skills and developed a keen interest in the intricacies of intelligent systems.

news

Jul 01, 2024	Our work was featured in the CEA List Research Report! Check out the full report here. A Safety supervision environment for autonomous systems CEA-List has developed a runtime safety supervision environment for autonomous systems built using AI. It factors in the uncertainties related to the system and to its environment to effectively determine the level of safety and potential risks. The system has been evaluated on an autonomous drone (UAV) use case.

Jul 01, 2024

Our work was featured in the CEA List Research Report!

Check out the full report here.

A Safety supervision environment for autonomous systems

CEA-List has developed a runtime safety supervision environment for autonomous systems built using AI. It factors in the uncertainties related to the system and to its environment to effectively determine the level of safety and potential risks. The system has been evaluated on an autonomous drone (UAV) use case.

latest posts

Mar 26, 2025	a post with plotly.js
Dec 04, 2024	a post with image galleries
May 14, 2024	Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra

selected publications

FindMeIfYouCan: Bringing Open Set metrics to near, far and farther Out-of-Distribution Object Detection

Daniel Montoya, Aymen Bouguerra, Alexandra Gomez-Villa, and 1 more author

2025

Bib PDF

@misc{montoya2025findmeifyoucanbringingopenset,
  title = {FindMeIfYouCan: Bringing Open Set metrics to near, far and farther Out-of-Distribution Object Detection},
  author = {Montoya, Daniel and Bouguerra, Aymen and Gomez-Villa, Alexandra and Arnez, Fabio},
  year = {2025},
  eprint = {2506.14008},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2506.14008},
}

Latent representation entropy density for distribution shift detection

Fabio Arnez, Daniel Alfonso Montoya Vasquez, Ansgar Radermacher, and 1 more author

In Conference on Uncertainty in Artificial Intelligence (UAI), 2024

Bib PDF

@inproceedings{arnez2024latent,
  title = {Latent representation entropy density for distribution shift detection},
  author = {Arnez, Fabio and Vasquez, Daniel Alfonso Montoya and Radermacher, Ansgar and Terrier, Fran{\c{c}}ois},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2024},
  url = {https://proceedings.mlr.press/v244/arnez24a.html},
}

Deep neural network uncertainty runtime monitoring for robust and safe AI-based automated navigation

Fabio Alejandro Arnez Yagualca

Dec 2023

Bib PDF

@phdthesis{arnezyagualca:tel-04672736,
  title = {{Deep neural network uncertainty runtime monitoring for robust and safe AI-based automated navigation}},
  author = {Arnez Yagualca, Fabio Alejandro},
  url = {https://theses.hal.science/tel-04672736},
  number = {2023UPASG100},
  school = {{Universit{\'e} Paris-Saclay}},
  year = {2023},
  month = dec,
  keywords = {Deep neural network ; Uncertainty ; Monitoring ; Propagation ; Automated system ; R{\'e}seaux neuronaux profonds ; Incertitude ; Surveillance du temps d'ex{\'e}cution ; Propagation ; Syst{\`e}me automatis{\'e}},
  type = {Theses},
  hal_id = {tel-04672736},
  hal_version = {v1}
}

Quantifying and using system uncertainty in uav navigation

Fabio Arnez, Ansgar Radermacher, and Huascar Espinoza

arXiv preprint arXiv:2206.01953, Dec 2022

Bib

@article{arnez2022quantifying,
  title = {Quantifying and using system uncertainty in uav navigation},
  author = {Arnez, Fabio and Radermacher, Ansgar and Espinoza, Huascar},
  journal = {arXiv preprint arXiv:2206.01953},
  year = {2022}
}

Towards dependable autonomous systems based on bayesian deep learning components

Fabio Arnez, Huascar Espinoza, Ansgar Radermacher, and 1 more author

In 2022 18th European Dependable Computing Conference (EDCC), Dec 2022

Bib

@inproceedings{arnez2022towards,
  title = {Towards dependable autonomous systems based on bayesian deep learning components},
  author = {Arnez, Fabio and Espinoza, Huascar and Radermacher, Ansgar and Terrier, Fran{\c{c}}ois},
  booktitle = {2022 18th European Dependable Computing Conference (EDCC)},
  pages = {65--72},
  year = {2022},
  organization = {IEEE}
}