Marco Lippi

Associate Professor

University of Modena and Reggio Emilia

Italy

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Marco Lippi is an Associate Professor at the Università degli Studi di Modena e Reggio Emilia in Italy. His research areas encompass machine learning applications, including unfair clause detection in terms of service, neuro-symbolic reasoning, and soil moisture forecasting using IoT sensors. Additionally, he explores concepts related to autonomous mental development, smart grid adaptation, and human-robot interaction, as well as the impacts of social media data on population analysis following natural disasters.

Marco Lippi

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Articles (19)

Autonomous Mental Development at the Individual and Collective Levels: Concept and Challenges

Data-Driven Adaptation of Smart Grids With Hierarchical Digital Twins

The KANDY benchmark: Incremental neuro-symbolic learning and reasoning with Kandinsky patterns

Multi-task neural networks for multi-step soil moisture forecasting in vineyards using Internet-of-Things sensors

Unfair clause detection in terms of service across multiple languages

Bearing Fault Detection and Recognition From Supply Currents With Decision Trees

A General Pipeline for Online Gesture Recognition in Human–Robot Interaction

Multi-Task Attentive Residual Networks for Argument Mining

Enabling causality learning in smart factories with hierarchical digital twins

Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter

Poka Yoke Meets Deep Learning: A Proof of Concept for an Assembly Line Application

Argument Mining as Rapid Screening Tool of COVID-19 Literature Quality: Preliminary Evidence

Detecting and explaining unfairness in consumer contracts through memory networks

A Data Driven Approach to Match Demand and Supply for Public Transport Planning

Attention in Natural Language Processing

Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers

Comparing Deep Learning and Statistical Methods in Forecasting Crowd Distribution from Aggregated Mobile Phone Data

Collaborators (10)

Andrea De Cesarei

Lorenzo Sabattini

Rita Gamberini

Cristian Secchi

Andrea Galassi

Marco Picone

Mauro Dell'Amico

Franco Zambonelli

Paolo Torroni

Stefano Mariani

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