Pedro Canotilho Ribeiro

Pedro Canotilho Ribeiro has a diverse background in computer vision, machine learning, and research. Pedro Canotilho started their career as an intern at EDP in 2002. Following that, they worked as a research assistant at the Institute for Systems and Robotics (ISR-Lisboa) from 2003 to 2010, where they focused on developing and implementing vision and machine learning systems for human-robot interaction.

In 2012, Pedro joined CoVii, where they worked as a computer vision and machine learning scientist. Their responsibilities included project development and library creation for vision systems, specifically focusing on human-machine interaction. Pedro Canotilho also served as an R&D assistant during their time at CoVii.

In 2013, Pedro joined CEA - VisionLab with Thales Group as a computer vision and machine learning researcher. Here, they collaborated with the integration team from Thales Group to develop a prototype of an abnormal event detection algorithm for future commercial products. Pedro Canotilho also worked on improving pedestrian and human activity detection and recognition systems.

Most recently, Pedro joined ARCURE in 2015, where they started as an R&D engineer specializing in computer vision and project management. Pedro Canotilho has played a crucial role in several projects, particularly as a machine learning expert. Additionally, they have worked as a project manager and has been responsible for coordinating team efforts.

Overall, Pedro Canotilho Ribeiro has gained valuable experience in computer vision, machine learning, and project management.

Pedro Canotilho Ribeiro's education history begins with a technical-professional course in Electronics at Colégio de Gaia, which they completed in 1994. Following this, they pursued a degree in Electrical Engineering and Computers at Instituto Superior Técnico from 1995 to 2003. Later, from 2004 to 2010, Pedro pursued a postgraduate PhD curricular plan at the same institution, specializing in Control and Robotics, Computer Vision, and Machine Learning.