Ing. G. (Gabriel) Nova MSc

Ing. G. (Gabriel) Nova MSc



I am a Chilean Ph.D. candidate in Transport & Logistics section in the department Engineering Systems and Services of the faculty TPM, in the line "Using AI to automate choice modelling".

My academic background is a Civil Engineer in Transport and a Master in Transport Engineering, both at the University of Chile. My Master's thesis, guided by Professor Angelo Guevara, focused on studying and understanding the dynamics of attribute scrutiny in discrete choice processes. Firstly, the study focused on the collection and analysis of such data using the "click-tracker" and "eye-tracker" methods mounted on a specially designed SP survey. In the second stage, the study considered the formulation and validation of a random utility maximisation model that considers the sequential evaluation of attributes (RUM-DFT). 

Based on this research, so far, it can be argued that there is a predominance of breadth-first information searches in the deliberation process, this behaviour is not total, but becomes more acute as the number of attributes and alternatives shown in SP surveys increases. Thus, the results suggest that the RUM model as such could not adequately describe the choice process. Likewise, the model that considers dynamic utilities and incorporates the assumptions behind the Decision Field Theory model, allows the recovery of both the parameters associated with the attributes and those of the information search process (tolerance, attention weights, etc.).


In parallel and before starting my PhD, I worked as a research collaborator at the Institute of Complex Systems Engineering (University of Chile). The two main lines of research were associated with:

  1. Development of a methodology that with the use of Biosensors (Biomonitor V3.0) and with a contextual application plus a software architecture, allows capturing and storing of information of psychophysiological data of users in different contexts such as the use of the Bicycle, the use of Public Transport, the Driving of Vehicles, the analysis of Telework, the Immersion in Monitors and Virtual Reality, the use of Sports (Golf) and the analysis of validation of IAPS.
  2. Formulation and validation of RUM-DFT that considers the full sequence of intermediate decisions in pre-choice attribute attention given the known attention information matrix. Bayesian estimation is explored to recover the sequence of attended attributes using only the choice in a choice situation.

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