Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals are to increase the trust of users in ML tools by extending their evaluation beyond performance.

Research Interest

Arnaud's updated research interest is focused on the application of Machine Learning techniques for improving Distributed Systems. He is interested in exploring how Machine Learning can be used to optimize and automate the management of distributed systems, improve their reliability, and enhance their overall performance. His work involves developing new algorithms and models for addressing the challenges of reliable machine learning deployment. Arnaud aims to contribute to the development of more efficient and intelligent distributed systems through his research.

Education Profile

  • M.Sc., Computer Science Engineering, UCLouvain, Louvain-la-Neuve, Belgium, 2017
  • B.Sc., Engineering, UCLouvain, Louvain-la-Neuve, Belgium, 2015

Selected Publications

Dethise, Canini, M. & Kandula, S. (2019). Cracking Open the Black Box: What Observations Can Tell Us About Reinforcement Learning Agents, NetAI '19