Katti Faceli

About

Katti Faceli is a Brazilian computer scientist whose research bridges machine learning, intelligent systems, and robotics, with particular emphasis on how computational techniques can be harnessed to solve complex real-world problems. Her early and most influential work centers on **sensor fusion** for mobile robotics — a technically demanding challenge in which data from multiple sensors must be intelligently combined to overcome the inherent limitations of individual sensors, which often produce incomplete, inconsistent, or inaccurate readings. Faceli's most cited contribution, "Combining Intelligent Techniques for Sensor Fusion" (2004, 30 citations), demonstrated that integrating machine learning methods could substantially enhance the reliability and accuracy of sensor data interpretation, a finding that has shaped subsequent research in autonomous systems. Her complementary experimental studies from 2002 and 2003 laid important groundwork by systematically evaluating different machine learning approaches within sensor fusion frameworks. Beyond robotics, Faceli has become widely recognized in the Brazilian academic community for her contributions to machine learning education, co-authoring influential instructional resources that have helped train a new generation of researchers. Her work reflects a consistent commitment to making intelligent systems more robust, practical, and accessible.

Research Focus

Key Achievements

Top Papers

1. 1
   Combining Intelligent Techniques for Sensor Fusion

   30 citations · 2004
2. 2
   Experiments on machine learning techniques for sensor fusion

   5 citations · 2002
3. 3
   Combining intelligent techniques for sensor fusion

   4 citations · 2003

Key Collaborators

Contact & Links