Fairness in data management: the students will investigate how algorithms can unintentionally encode existing human biases and add new ones; then they will study how to enforce contextual fairness in recommender systems/optimization problems.
The role of context in fair group recommendations: the student will investigate the role played by the context, i.e. the situation a group of people is experiencing, in the design of a system that recommends sequences of activities as a multi-objective optimization problem, where the satisfaction of the group and the available time interval are two of the functions to be optimized. The dynamic evolution of the group can be the key contextual feature that has to be considered to produce fair suggestions.
Inferring a change of context from temporal series: the student will investigate how to infer a change of the current context (the situation a user or group of people is experiencing) from temporal data and by using ML algorithms.
Analysis of data produced by smart devices: the student will study data-mining algorithms to infer new knowledge from big datasets produced by sensors (e.g. Fitbit) in order to discover temporal patterns, correlations and suggest more advanced functionalities in Apps.
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