Fabio Arnez - Website

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 electronics 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

May 13, 2026	Honored to be recognized as a Gold Reviewer at ICML 2026, placing among the top reviewers for this year’s conference!
Apr 30, 2026	Our paper “Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization’s Impact on VLMs Beyond Accuracy” was accepted at ICML 2026 (July 6-11, 2026, Seoul, South Korea)!!! Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization’s Impact on VLMs Beyond Accuracy Authors: Aymen Bouguerra, Daniel Montoya, Alexandra Gomez-Villa, Chokri Mraidha, and Fabio Arnez ICML 2026, Seoul, South Korea.
Apr 13, 2026	Our paper “FedSALAT: Adaptive Buffer-Based Active Learning for Federated Data Streams” was accepted at FLICS 2026 (June 9-12, 2026, Valencia, Spain)!!! FedSALAT: Adaptive Buffer-Based Active Learning for Federated Data Streams Authors: Prajit T Rajendran, Fabio Arnez, Huascar Espinoza, Agnes Delaborde, Chokri Mraidha FLICS 2026, Valencia, Spain.
Mar 31, 2026	Our paper “Digital Twins and World Models: A Systematic Taxonomic Disambiguation” was accepted at the MIDas4CS 2026 workshop, part of CAiSE 2026 (June 9, 2026, Verona, Italy)!!! Digital Twins and World Models: A Systematic Taxonomic Disambiguation Authors: Fabio Arnez MIDas4CS 2026 Workshop at CAiSE 2026, Verona, Italy.
Nov 03, 2025	New Blog Post: Uncetainty Quantification & Propagation in a DNN-based Navigation System After a long long pause in writing blog content, I wrote a new post about “Uncertainty Quantification & Propagation in a DNN-based Navigation System”. The post describes how to quantify and propagate uncertainty in a minimalistic UAV/drone DNN-based navigation system (2 neural networks) and shows how to use uncertainty to improve the navigation system’s performance inside the AirSim simulation environment. For more details, check the post here.

latest posts

Nov 03, 2025	Quantifying and Using Uncertainty in Deep Learning-based UAV Navigation
Apr 13, 2020	Probability & Statistics Fundamentals: Part I
Apr 07, 2020	Notes on Simple Approximate Inference in Bayesian Neural Networks

selected publications

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization’s Impact on VLMs Beyond Accuracy

Aymen Bouguerra, Daniel Montoya, Alexandra Gomez-Villa, Chokri Mraidha, and Fabio Arnez

2025

Bib PDF

@misc{bouguerra2025precisereliablesystematicevaluation,
  title = {Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy},
  author = {Bouguerra, Aymen and Montoya, Daniel and Gomez-Villa, Alexandra and Mraidha, Chokri and Arnez, Fabio},
  year = {2025},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2509.21173},
  pdf = {https://arxiv.org/pdf/2509.21173}
}

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

Daniel Montoya, Aymen Bouguerra, Alexandra Gomez-Villa, and Fabio Arnez

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},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2506.14008},
  pdf = {https://arxiv.org/pdf/2506.14008}
}

Latent representation entropy density for distribution shift detection

Fabio Arnez, Daniel Alfonso Montoya Vasquez, Ansgar Radermacher, and François Terrier

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},
  pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/arnez24a/arnez24a.pdf}
}

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},
  pdf = {https://theses.hal.science/tel-04672736v1/file/122361_ARNEZ_YAGUALCA_2023_archivage.pdf},
  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, 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 François Terrier

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

news

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization’s Impact on VLMs Beyond Accuracy

FedSALAT: Adaptive Buffer-Based Active Learning for Federated Data Streams

Digital Twins and World Models: A Systematic Taxonomic Disambiguation

New Blog Post: Uncetainty Quantification & Propagation in a DNN-based Navigation System

latest posts

selected publications