Rodolfo Alves dos Santos Ai

24 Chemin d'Arancette · Bayonne, ZIP 64100 · +33 7 66 12 69 34 · ai.rodolfo@gmail.com

Second year student in Master of Computer Science - Industry 4.0.
Fields of study: Machine Learning, Business Intelligence, Cloud Computing, Micro-Services, Databases, Internet of Things (IoT) and Systems Engineering.

Industrial Engineer graduated from the Universidade Federal do Rio de Janeiro (UFRJ).


Experience

Engineering Internship

Accenture

Development of machine learning and data analytics solutions to help the client to recognize, understand and exploit its customers needs in order to offer the right offer at the right time within a customer-centric marketing strategy.

December 2017 - August 2018

Research Internship

Centro de Tecnologia Mineral (CETEM)

Bibliographic research and market analysis, followed by the development of a simulation model of the world market of rare-earth elements culminating in short, medium and long-term projections of the referred market.

July 2015 - June 2016

Education

Université de Pau et Pays de l'Adour (UPPA)

Master of Computer Science - Industry 4.0

Expected graduation in August 2019

September 2018 - Present

Universidade Federal do Rio de Janeiro (UFRJ)

Industrial Engineer

GPA : 7.9 / 10.0

March 2013 - August 2018

Université de Technologie de Troyes (UTT)

1 year exchange in Industrial Engineering
September 2016 - August 2017

Skills

Programming Languages & Tools
  • Microsoft Office
  • Excel / VBA
  • Python
  • SQL
  • R
  • Java
  • C
  • .NET
  • Java EE
Languages
  • Portuguese
  • English
  • French
  • Spanish

Interests

In my free time, I prefer outdoor activities such as running, soccer and volleyball. Also, in Brazil I used to be part of a group that gave private lessons of math, physics and statistics.

Indoors, I love studying new languages and trying new cooking recipes.


Master Industry 4.0 Project

Autonomic Garden

The practice of cultivating and maintaining home plants can be fulfilling as well as economically rewarding. Nevertheless, the complexity of managing a home garden ranges from irrigation routines to light exposure and crop rotation.

In this matter, the main purpose of this project is to develop a prototype of an autonomic garden that is capable of gathering information from the plant and from its environment through sensors, analyzing the collected data by a cross-validation within a plants database, creating a plan of action outlining the required adjustments of the system to achieve its gardening goals, and executing this plan by managing effectors that actuate on the garden itself.

This autonomic system aims to assist the gardening role through optimization of resource usage and maximization of productivity. As a consequence of learning the ideal amount of water necessary for the development of the plant regarding the environmental circumstances of its cultivation and its biological needs, the system does not only avoid water waste but also enhance plant growth.