ACLAI – Lab

ACLAI Lab, part of the Mathematics and Computer Science Department at the University of Ferrara, focuses on studying, designing, and delivering artificial intelligence applications. Our research is focused on the edge between logic and AI and our primary objective is to design mathematically sound and formally certified tools. We are also members of the OVERLAY research group.

Team

Prof. Guido Sciavicco

ASSOCIATE PROFESSOR

Dr. Giacomo Piva

ADJUNCT PROFESSOR

Dr. Ionel Eduard Stan

RESEARCHER – Milano-Bicocca

Dr. Giovanni Pagliarini

POST-DOCTORAL RESEARCHER

Dr. Alberto Paparella

PHD STUDENT

Dr. Federico Manzella

MASTER'S STUDENT

Dr. Mauro Milella

MASTER'S STUDENT

Dr. Michele Ghiotti

MASTER'S STUDENT

Dr. Andrea Paradiso

MASTER'S STUDENT – Udine

Dr. Patrik Cavina

MASTER'S STUDENT – Udine

Dr. Edoardo Ponsanesi

MASTER'S STUDENT

Dr. Pietro Casavecchia

MASTER'S STUDENT

Dr. Pietro Bellodi

MASTER'S STUDENT

Dr. Leonardo Serrentino

MASTER'S STUDENT

Mr. Riccardo Pasini

BACHELOR'S STUDENT

Mr. Marco Perrotta

BACHELOR'S STUDENT

Mr. Lorenzo Negrini

BACHELOR'S STUDENT

Mr. Giacomo Pampalone

BACHELOR'S STUDENT

Projects

Foundations of modal symbolic learning

We study the foundations of symbolic learning methods, with special focus on the potential of modal logic. Our purpose it to define a complete symbolic framework that includes data analysis, model learning and evaluation, post-hoc model analysis, and model visualization. Our framework is the basis onto which we develop our applied projects; its implementation, Sole.jl, is completely open-source, and written in the Julia programming language.

EEG reading and interpreting

Within our symbolic learning framework, we extract, interpret, and test symbolic models to extract information from EEG signals. Examples include neurophysiology trials in which subjects undergo several tasks, including artwork observation and/or simple movements, while their brain activity is being recorded. Our goal is to devise symbolic models that explain and predict the outcomes.

Predictive maintenance

We study predictive maintenance tasks of several kinds of machines, using sensors' data and their behavior in time. For example, we considered a predictive task designed to warn against possible 'trip' event in gas turbines, and designed both general and machine-specific symbolic temporal model, which allow the experts not only to perform preemptive shutdowns but also to study what seems to influence the events insurgence.

Information extraction from audio

In collaboration with the leading experts in conversation automation, we study symbolic temporal models of audio for different purposes. Our models aim to extract information about the speaker(s), their characteristics, sentiment, and other relevant characteristics from human and semi-automatic conversations, using innovative approaches based on the audio rather than on the text.

Health monitoring

We design, test, and apply symbolic model for online, real-time data from health monitors and accelerometers/gyroscopes. Such models live on health monitor software/hardware systems, specifically built for emergency situations, and are tested in real cases. Real situations that can be monitored include the movement status of the subjects, possible falls and/or injuries, but also cardiac/respiratory events and/or distress. Models are completely symbolic, and produce verifiable rules that can be analyzed, discussed, and possibly modified by the expert.

Geochemical fingerprinting of food products and water resources

We study, design, and implement protocols for geochemical fingerprinting based on physical, chemical, and isotopic data. We work with the Department of Physics and Earth Sciences of the University of Ferrara (Italy), the University of Còrdoba (Spain), and the Ayesa Foundation (Sevilla, Spain).

Medical data management and decision support system

In collaboration with the Research Center for the Study of Menopause and Osteoporosis of the University of Ferrara, we are designing and maintaining a data processing center equipped with a innovative decision support system.

Time series classification

We design and study new time series classification methods using symbolic, temporal-logic based knowledge extraction methods. At the moment, we are applying our techniques to many tasks; among them, we work with Siemens Europa, and we are trying to build systems that allow one to predict the need of maintenance of gas turbines.

Image classification

We are studying novel techniques for image classification and segmentation, based on symbolic, spatial-logic based knowledge extraction techniques. These techniques have shown promising results with satellite/areal data, specifically on the task of land cover classification.

Partners

Research

ACLAI promotes an undergraduate research initiative for mathematics and computer science students. The purpose of this initiative is to stimulate undergraduate students to start their research career and enrich their curricula with conference and journal publications. The Bachelor Degree in Computer Science within the University of Ferrara, in particular, includes 21 ECTS credits (12+9) for internship and final project; students joining the undergraduate research initiative spend this amount of credits at the ACLAI laboratory, enjoy the possibility of studying extra-curricular topics, and, in some cases, publish early-stage theoretical and implementation results. Current and past students that have joined the program are: