VisioScientiae has been founded to exploit the state-of-the-art of the Artificial Intelligence and Big Data technologies and algorithms to create and deploy innovative services and applications.
Among the application fields where the AI-powered algorithms and systems are experimented and deployed with unrivalled levels of performance, we can list: Robo-Trading, Smart Mobility, Medicine & Healthcare, Social Media.


The roots and Polar Star of the company are summarized by its name (VisioScientiae means “the Vision of Science” in Latin language).
We strongly believe that a scientific approach for the forecasting and risk management in Robo Trading, for the automatic detection of anomalies from traffic road video sequences, for the automatic classification of diagnostic exams, for the prediction of the popularity of an Instagram Post, and so on, fueled by the disruptive growth of Artificial Intelligence and Big Data technologies and cross-pollinated with domain specific competence and experience, is the key to unprecedented performances and opportunities in each of these domains.


VisioScientiae is a spin-off of the University of Cagliari. Its founders are Computer Science Researchers and Trading Experts with strong curriculum in the fields of Artificial Intelligence, Computer Vision, Classification Systems, Sentiment Analysis, Big Data, Semantic Web, Data Mining, Trading Systems. Each founder has also a strong experience in the creation and development of successful international and national companies within the ICT sector.


Machine Learning

A supervised machine-learning model transforms its input data into meaningful outputs, a process that is “learned” from exposure to known examples of inputs and outputs. It is trained rather than explicitly programmed.
It’s presented with many examples relevant to a task, and it finds statistical structures in these examples that eventually allow the system to come up with rules for automating the task.
Classical supervised machine learning algorithms range from Decision Trees to Naive Bayes to Support Vector Machines and so on.

Deep Learning

Deep learning is a specific subfield of machine learning: a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations.
It is based on biologically inspired algorithms called Artificial Neural Networks.
The “deep” in deep learning isn’t a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representations.

Sentiment Analysis

Also known as Opinion Mining, it refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment analysis aims to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event.

Deep Reinforcement Learning

Long overlooked, this branch of machine learning recently started to get a lot of attention after Google DeepMind successfully applied it to learning to play Atari games (and, later, learning to play Go at the highest level).
Currently Deep Reinforcement Learning is the root of many emerging AI-based industrial applications (i.e. self-driving cars), and it is sometimes addressed as the “true Artificial Intelligence”.