The new national PhD program in Artificial Intelligence is on the launchpad!
Our KDD Lab - Knowledge Discovery & Data Mining Laboratory @ University of Pisa and ISTI-CNR of the National Research Council of Italy funds 3 positions at the "AI & Society" PhD.
Interested to join a vibrant research ecosystem on cutting edge topics around on human centered AI, XAI, social AI, AI ethics and Trustworthy AI?
Our cities stand today as a miraculous lab to foster an effective joint effort of different disciplines united by the common goal of setting our cities on a path towards well-being and social and environmental sustainability.
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Yet, they are as unintelligible as they are complex, and relying on them raises significant concerns about their transparency.
In the last decade, the availability of large mobility datasets such as Call Detail Records (CDR) [1, 2, 3], traces from GPS devices embedded in smartphones and cars [3], and geo-tagged posts on Location-Based Social Networks (LBSN) [4], allows characterizing human mobility from a statistical and mathematical point of view, uncovering the invisible rules that govern the individuals' displacements.
In recent years the wide availability of data stored in the form of time series contributed to the diffusion of extremely accurate time series classifiers employed in high-stakes decision making. Unfortunately, the best time series classifiers are usually black-boxes, and therefore quite hard to understand from a human standpoint. This fact slowed down the usage of these models in critical domains, where the explanation aspect is crucial for a transparent interaction between the human expert and the AI system.
As a widely used but generalised term, "migration" can refer to different human movements with residential intentions.
In the last decade, the availability of large mobility datasets such as Call Detail Records (CDR) [1, 2, 3], traces from GPS devices embedded in smartphones and cars [3], and geo-tagged posts on Location-Based Social Networks (LBSN) [4], allows characterizing human mobility from a statistical and mathematical point of view, uncovering the invisible rules that govern the individuals' displacements.
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Apart from a few sporadic attempts, it is only in recent years that soccer statistics have developed, thanks to sensing technologies that provide high-fidelity data streams extracted from every match. These data streams are mainly used by researchers in academia, data scientists in the industry, or sports data journalists to extract meaningful knowledge and tell stories.
The standard deviation of the inter-beats interval between QRS complexes recorded during 24 h (SDNN24) is considered the gold standard of Heart rate variability (HRV) features for cardiac health [1]. SDNN24 is an HRV feature that requires 24 h of continuous recording Inter-Beat Intervals, traditionally achieved using a Holter device, that makes the data collection difficult during people’s everyday life, therefore not performed routinely.