Adrien Dorise

Adrien Dorise holds a position as Research Engineer at CNES, France. He received the Engineering degree from INSA CVL in 2018, and his PhD in computer science and embedded systems from INSA Toulouse in 2022. His research interests include artificial intelligence, tiny machine learning, computer vision, explainable AI and space radiation.

He is the founder, developer and composer of Law Tech Productions, an indie game studio, and aims to develop this activity into the creation of serious games, combining his interest in interactive games with his research work.

What’s new?

PhD Defense

Finally, it’s done, you can call me Dr.Dorise now!After 3 years of hard work, I…

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Thesis

  1. Adrien Dorise, “Embedded anomaly detection – Machine learning based radiation hardening of space electronics”. Computer Science and Embedded Systems. INSA de Toulouse, 02/12/2022, hal: tel-03997861

Papers

  1. Adrien Dorise, Marjorie Bellizzi, Adrien Girard, Benjamin Francesconi, Stéphane May, “Explaining raw data complexity to improve satellite onboard processing“, European Data Handling and Processing Conference (EDHPC) 2025, Elche, Spain, arXiv: https://arxiv.org/abs/2510.06858
  2. J. Martin-Saquet, A. Dorise, E. Villain, S. Sanchez and D. Panzoli, “Sequential Decision-Making in Atari 2600 Games: Comparing Temporal Features,” 2024 Artificial Intelligence Revolutions (AIR), Roanne, France, 2024, pp. 117-123, doi: 10.1109/AIR63653.2024.00027, hal: https://hal.science/hal-05069376/
  3. Adrien Dorise, Audine Subias, Louise Travé-Massuyès, Corinne Alonso, “Advanced machine learning for the detection of single event effects” Radiation and its Effects on Components and Systems – RADECS 2022, 03/10/2022, hal-03789895
  4. Adrien Dorise, Louise Travé-Massuyès, Audine Subias, Corinne Alonso, “Dyd²: Dynamic Double anomaly Detection: Application to on-board space radiation faults” IFAC Safeprocess 2022 :11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, IFAC, 08/06/2022, Pafos, Cyprus, doi: 10.1016/j.ifacol.20, hal: hal-03609573v2.
  5. Adrien Dorise, Corinne Alonso, Audine Subias, Louise Travé-Massuyès, François Vacher, Leny Baczkowsky, “Machine learning as an alternative to thresholding for space radiation fault detection”, Radiation and its Effects on Components and Systems – RADECS 2021, 13/09/2021, doi: 10.1109/RADECS53308.2021.9954582, hal: hal-03331030v1

Workshops

  1. Serge Dos Santos, Giuseppe Nardoni, Adrien Dorise, Parnian Hemmati, Marco Feroldi, Pietro Nardoni, “Experimental analysis of planar/volumetric defects in ultrasonics NDT: standardization of evaluation metrics using symbiosis of TOFD and TR-NEWS methods“, European Conference of Non-Destructive Testing – ECNDT2023 -Lisbonne – Portugal, 07/2023, doi: 10.13140/RG.2.2.16285.90087
  2. Boris Lenseigne, Julia Cohen, Adrien Dorise, Edouard Villain, “Machine learning for computer vision: a case study in human-machine interaction“, ePicture This Workshop 2023, 06/2023, doi: 10.13140/RG.2.2.36695.44964.
  3. Adrien Dorise, Louise Travé-Massuyès, Corinne Alonso, Audine Subias, François Vacher, Leny Baczkowsky, “Creation of a database for Single events latch-up detection on Atmel SAM3X microcontroller”, European Space Components Conference – ESCCON 2021, 09/03/2021, hal: hal-03330093v1.
  4. Adrien Dorise, Louise Travé-Massuyès, Corinne Alonso, Audine Subias, François Vacher, Leny Baczkowsky, “Anomaly Detection for Radiation Hardening of Space Electronics – Application of Machine Learning Algorithms on an Atmel SAM3X Microcontroller”, 14th ESA Workshop on Avionics, Data, Control and Software Systems – ADCSS2020, 20/10/2020, hal: hal-03330033v1.

Posters

  1. Adrien Dorise, Louise Travé-Massuyès, Audine Subias, Corinne Alonso “Dynamic Double anomaly Detection through evolving clustering: Application to on-board space radiation faults“, ANITI meeting, Toulouse, France, 03/2022, hal: hal-03622285.

Teachings

Gema / Nexa

  • Master level: Machine Learning
    • Designed and delivered a 30-hour course on machine learning fundamentals.
    • Curriculum covers supervised learning (classification, regression & unsupervised learning (clustering and dimension reduction).
  • Master level: Deep Learning
    • Designed and delivered a 30-hour course on deep learning fundamentals.
    • Curriculum covers neural networks, convolutional networks, recurrent networks, and transformers.

Ynov Campus

  • Master level: Artificial Intelligence & Non-Playable Behaviours.
    • Designed and delivered a 35-hour course on reinforcement learning.
    • Led project-based evaluation through the creation of an autonomous car under Unity (project available on GitHub).

INSA Toulouse

  • Master level: Machine Learning.
  • Master level: Deep Learning.
  • Master level: Data Visualization.
  • Bachelor level: Computer Science (C).
  • Bachelor level: Computer Science (ADA).

Université Paul Sabatier

  • Bachelor level: Discrete Control.
  • Bachelor level: Linear Control.