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Three VRSS students have successfully passed major milestones.

Three VRSS students have successfully passed major milestones.

Author: Karen Marais
Event Date: April 26, 2019
Nicoletta Fala, Kim Rink, and Georgios Georgalis have all passed major degree milestones.

Nicoletta successfully defended her doctoral thesis. She developed a survey to evaluate how potential cognitive biases impact pilot response in data-driven risk communication. She used data from flight data recorders to create debrief messages in several risk communication methods for different flight scenarios. Pilots taking the survey responded to each flight by evaluating and trying to mitigate their risk in future flights. Nicoletta found that the different methods affect how pilots respond and future research needs to address whether the type of flight needs to be taken into account before choosing how to present risk to pilots. Nicoletta will be an Assistant Professor at Purdue's School of Aviation and Transportation Technology starting this Fall.

Kim successfully defended her Master's thesis. She designed a knowledge management tool that uses a nodal network to disseminate lessons learned from the NASA Jet Propulsion Laboratory. She deployed an experiment to see how aerospace engineering students explore different knowledge management tools and whether they rely on these tools to help answer systems engineering questions. She found that tools with more functionality enable participants to provide better engineering solutions, and that a nodal network tool is a promising method of lessons learned dissemination. Kim will join NASA's Jet Propulsion Lab this Summer.

Georgios passed his PhD Preliminary Examination and is now officially a PhD candidate. He is developing a prototype to predict future occurrences of project failures and failure causes. The prototype uses activities, behaviors, and opinions of the project team members during a given week as inputs to machine learning algorithms for the predictions. In the future, Georgios will investigate whether project performance improves by providing targeted feedback based on such predictions.