While at AIMS, Uriel worked on Dynamic path prediction in IoT systems. The goal of the project was to design a reinforcement learning algorithm to reduce the energy consumption in an IoT sensor network during path planning when data goes from one equipment to another one. After comparison with existing algorithms, he showed that his model increases the network lifetime by a factor of 2.26 compared to 1.58 obtained with these algorithms.

After AIMS, Uriel did a three months Internship at Dev-AI as a Data scientist Intern. He also worked on a project at KES Sarl in collaboration with ENEO Cameroon as a Data Analyst before joining the AMMI program in Senegal.