cv
Below you have a summary of my CV, for a version with more details, please download the PDF file.
For a long-detailed version of my CV, please use this [link].
Basics
Name | Fabio Arnez |
Label | Research Engineer in AI | Ph.D. in Computer Science |
name.lastname@cea.fr | |
Url | https://fabioarnez.github.io/ |
Work
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2019.10 - Present Palaiseau, France
Research Engineer in AI
CEA-LIST, DILS, LSEA
Research in Deep Learning Uncertainty, Out-of-Distribution Detection, and Trustworthy AI/AI Safety.
- Trustwrothy Deep Learning Team Leader
- Participation in the EU&FR funded projects: Comp4Drones (WP4-T4.4 leader and contributor), Confiance.ai program (contributor), DeepGreen (contributor)
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2019.04 - 2019.10 Cochabamba, Bolivia
Data Scientist
Bamboo Tec
Enterprise data analysis and visualization (dashbords)
- Data Science & Engineering Team Leader
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2016.09 - 2018.08 Lugano, Switzerland
Research Assistant
University of Applied Arts and Science of Souther Switzerland - SUPSI
Embedded Systems and IoT R&D
- Embedded Machine Learning for signal classification
- Embedded systems prototyping for wireless IoT applications (802.15.4, Bluetooth)
- RF PCB Design
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2016.05 - 2014.05 Cochabamba, Bolivia
UAV Research Engineer
Jalasoft
UAV Computer Vision, Navigation, and Embedded Systems development
- UAV localization & navigation
- ROS integration
- Embedded systems and PX4/Pixhawk autopilot development
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2014.09 - 2019.03 Cochabamba, Bolivia
Adjunt Lecturer and Researcher
Universidad Privada Boliviana
Research on Smart Embedded Systems for IoT, and Lecturer from the Electronics and Computer Science majors
- Smart Street Lightning Project
- Courses Taught: Embedded Electronic Systems (2016), Microprocessor Architecture and Technology (2015, 2016, 2019), Electronics and Telecom. Project (2018), Telecom. Electronics (2018), Electronic Instrumentation (2014, 2015)
Education
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2019.12 - 2023.12 Palaiseau, France
Ph.D. in Computer Science - Artificial Intelligence
Université Paris-Saclay
Deep Learning Uncertainty Quantification, Trustworthy AI, AI-Safety
- Ph.D. Thesis Title: Deep Neural Network Uncertainty Runtime Monitoring for Robust and Safe AI-based Automated Navigation
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2016.09 - 2018.07 Lugano, Switzerland
MSc. in Engineering - Embedded Sytems and Microelectronics
University of Applied Arts and Science of Southern Switzerland (SUPSI)
Precision Systems and Telecom
- MSc. Thesis Title: Real-Time Human Footstep Recognition on Smart Anti-Static Floor
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2008.02 - 2014.05 Cochabamba, Bolivia
BSc. in Electronics and Telecommunications Engineering
Universidad Privada Boliviana (UPB)
Embedded Systems and IoT
- Grade Project Title: VIRMS – A Vehicle Information and Road Monitoring System
Awards
- 2016.09
RETECA Fondation Scholarship
RETECA Foundation, Switzerland
The aim of the RETECA Foundation is to help and support young researchers from developing countries engaged in post-education or research activities in the field of electrical engineering or applied electrical engineering.
Publications
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2025.06 FindMeIfYouCan: Bringing Open Set metrics to Near, Far and Farther Out-of-Distribution Object Detection
ArXiV
This paper shows that the current evaluation protocol for OOD-OD violates the assumption of non-overlapping objects with respect to the In-Distribution (ID) datasets, and obscures crucial situations such as ignoring unknown objects, potentially leading to overconfidence in deployment scenarios where truly novel objects might be encountered. To address these limitations, we manually curate, and enrich the existing benchmark by exploiting semantic similarity to create new evaluation splits categorized as near, far, and farther from ID distributions. Additionally, we incorporate established metrics from the Open Set community, providing deeper insights into how effectively methods detect unknowns, when they ignore them, and when they mistakenly classify OOD objects as ID. Our comprehensive evaluation demonstrates that semantically and visually close OOD objects are easier to localize than far ones, but are also more easily confounded with ID objects. Far and farther objects are harder to localize but less prone to be taken for an ID object.
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2024.07 Latent Representation Entropy Density for Distribution Shift Detection
PMLR
This paper proposes using simple sample-based techniques for estimating uncertainty and employing the entropy density from intermediate representations to detect distribution shifts. We demonstrate the effectiveness of our method using standard benchmark datasets for out-of-distribution detection and across different common perception tasks with convolutional neural network architectures. Our scope extends beyond classification, encompassing image-level distribution shift detection for object detection and semantic segmentation tasks.
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2022.09 Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components
IEEE
This paper provides a method that considers the uncertainty and the interaction between BDL components to capture the overall system uncertainty. We study the effect of uncertainty propagation in a BDL-based system for autonomous aerial navigation. Experiments show that our approach allows us to capture useful uncertainty estimates while slightly improving the system’s performance in its final task. In addition, we discuss the benefits, challenges, and implications of adopting BDL to build dependable autonomous systems
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2022.05 Quantifying and Using System Uncertainty in UAV navigation
ArXiV
We study the effect of the uncertainty from perception representations in downstream control predictions. Moreover, we leverage the uncertainty in the system's output to improve control decisions that positively impact the UAV's performance on its task.
Languages
Spanish | |
Native speaker |
English | |
Fluent C1/C2 |
French | |
Basic-Intermediate B1 |
Italian | |
Basic A2 |