Favaro, Francesca

Francesca Favaro

Assistant Professor

Ph.D. Aerospace Engineering, Georgia Institute of Technology


Preferred: francesca.favaro@sjsu.edu


Preferred: 408-924-3215


Industrial Science, IS–104

One Washington Square, San Jose, CA, 95192

About Me

Prof. Favaro is no longer teaching at SJSU. She was an Associate Professor in the Department of Aviation at Technology at San Jose State University. She also served as the Academic Advisor for the aviation program (see info here, scroll down the page). She joined the department in Fall 2016 as a tenure-track faculty, and, in addition to teaching, began working on the establishment of her research lab: RiSA2S, or Risk and Safety Assessment for Autonomous Systems Lab. In Fall 2017 she became a Research Associate for the Mineta Transporation Institute (MTI), and started her work for MTI as expert in the field of Autonomous Systems. She was promoted to Associate Professor and awarded tenure in 2020, the year when she also took a leave of professional development and joined Waymo (previously Google self-driving project).

Her interests lie in the broad field of System Safety and Risk Analysis with particular emphasis on:

  • Safety of Autonomous Systems: be they self-driving cars or unmanned aerial systems, there are important implications for system safety and risk assessment when you remove the "human" component. Her research in this field is aimed at understanding how to better guide and direct regulations and certification processes for autonomous systems
  • Human Factors in Semi-Autonomous Systems: when you do not plan on completely removing the "human element" from the operations of your system, there are important implications in terms of reaction times and expected operations when the controls and authority are handed from the autonomous brain (e.g., autopilot) to the human operator. Her research in this field aims at answering the important question: "are semi-autonomous system safe for all?"
  • Introducing formal tools to System Safety: bringing together concepts from control and system theory and from formal verification techniques to expand the intellectual toolkit of safety practitioners (e.g., the use of Temporal Logic to bear on risk assessment and for the expression of safety constraints);
  • System Safety Principles: risk analyses and reliability studies are generally domain-dependent; it is thus interesting to explore the idea of abstracting high-level safety principles to provide general guidelines for devising safety features in any engineering domain, which is also useful for engineering education;
  • Accident Causation and the study of the chain of causality: understanding accident precursors and how accidents can be prevented/avoided in the future, in particular in relation to how software contributes to mishaps.

She is an Amelia Earhart – Zonta International Foundation fellow, a Moneti fellow, and an official reviewer for the TRBReliability Engineering and System Safety, Safety Science , and IEEE Transactions on Aerospace and Electronic Systems.

Current Teaching

In the past Prof. Favaro taught AVIA 78, AVIA 02AVIA 150, AVIA 193, AVIA 68. She also served as coordinator for AVIA 03, AVIA 63, AVIA 113 and was a coordinator for many classes taught by SJSU part-time instructors. You can learn more about her teaching here


Prof. Favaro earned a BS and an MS from Politecnico di Milano in 2008 and 2011 respectively (with majors in Aerospace Engineering and Space Engineering) and another MS at Georgia Tech in 2014 (majoring in Aeronautics with a minor in mathematics). In 2010 she spent a year at the University of California – Irvine working on hybrid rocket engines for her Master thesis project. In 2016 she earned her doctorate at Georgia Tech in the School of Aerospace Engineering under the advice of Dr. Joseph H. Saleh, affiliated with the Space System Design Lab.

Licenses and Certificates

  • FAA Certified Advanced Ground Instructor (AGI)
  • FAA Certified Remote Pilot for Commercial Operations (Part 107)
  • Soloed Pilot
  • Coursera Certifications: Machine Learning (100/100 Offered by Stanford University)