Sandra Servia-Rodriguez

TL;DR: This paper shows that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population.

TL;DR: This work proposes a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices, and reveals that 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood.

TL;DR: This work takes an initial model learnt from a small set of users and retrain it locally using data from a single user, obviating many current privacy risks and evaluating the robustness of the approach against adversarial attacks.

TL;DR: This work takes advantage of both Facebook and Groupon APIs to provide a deployment scenario in which knowing users' social life allows ads to target the most potential customers, which is beneficial for both companies and possible customers.