Marco LIPPI

Ruolo attuale:: Professore Associato
SSD:: IINF-05/A - Sistemi di elaborazione delle informazioni
Afferenza organizzativa:: Dipartimento di Ingegneria dell'Informazione

Recapiti

	0552758524
	marco.lippi(AT)unifi.it

Marco LIPPI

Orario di ricevimento (aggiornato al 02/04/2024)

Lunedì 10:30-12:30

(Previo appuntamento via email)

Marco LIPPI

Biografia

Marco Lippi è nato a Pistoia il 23/5/1983. Ha conseguito la laurea triennale e la laurea specialistica in ingegneria informatica presso l'Università degli Studi di Firenze, nel 2004 e nel 2006, rispettivamente. Nel 2010 ha conseguito il titolo di Dottore di Ricerca in Ingegneria Informatica e dell'Automazione. Ha avuto posizioni come post-doc presso le Università degli Studi di Firenze (2010-2011), Siena (2011-2014) e Bologna (2014-2016), ed è stato visiting scholar nel 2014 presso l'Université Pierre et Marie Curie di Parigi. Dal 2016 al 2024 è stato ricercatore a tempo determinato di tipo B (2016-2019) e poi professore associato (2019-2024) presso l'Università degli Studi di Modena e Reggio Emilia. Da marzo 2024 è professore associato presso l'Università degli Studi di Firenze.

Marco LIPPI

Curriculum

Formazione:

- Laurea triennale in ingegneria informatica, Università degli Studi di Firenze, 2004 (votazione: 110/110 e lode).

- Laurea specialistica in ingegneria informatica, Università degli Studi di Firenze, 2006 (votazione: 110/110 e lode, con encomio).

- Dottorato di ricerca in ingegneria informatica e dell'automazione, Università degli Studi di Firenze, 2010.

Posizioni accademiche:

- Assegnista di ricerca, Università di Firenze, 2010-2011

- Assegnista di ricerca, Università di Siena, 2011-2014

- Assegnista di ricerca, Università di Bologna, 2014-2016

- Ricercatore a tempo determinato di tipo B, Università di Modena e Reggio Emilia, 2016-2019

- Professore associato, Università di Modena e Reggio Emilia, 2019-2024

- Professore associato, Università di Firenze, 2024-ad oggi

Progetti principali:

- Responsabile (principal investigator) del progetto AMICA (Argument Mining in Covid-19 Articles) finanziato dal Ministero dell'Università e della Ricerca all'interno del programma FISR COVID-19, 2021.

- Participazione come ricercatore al progetto TAILOR (Foundations of Trustworthy AI -- Integrating Reasoning, Learning and Optimization), network of excellence, finanziato dal bando H2020-ICT-48-2020, 2020-2024.

- Partecipazione come ricercatore e responsabile di unità locale come subcontractor (UniMORE) per il progetto POR-FESR 2014--2020 finanziato da Regione Toscana "Semantic Clauses Understanding and Detection" (SCUDO), 2021-2022.

- Partecipazione come ricercatore e responsabile di unità locale (UniMORE) al progetto CLAUDETTE (http://claudette.eui.eu) finanziato dal Research Council dell'European University Institute, per lo sviluppo di un sistema di riconoscimento automatico di clausole potenzialmente fraudolente nei contratti online (2018).

Interessi:

- Modelli neuro-simbolici di intelligenza artificiale

- Modelli interpretabili di intelligenza artificiale

- Informatica giuridica: strumenti di intelligenza artificiale al servizio della legge a tutela dei consumatori.

- Algoritmi di apprendimento automatico per l'analisi di dati acquisiti da missioni spaziali di telerilevamento (Planetary Remote Sensing Missions)

- Natural language processing per l'analisi automatica di documenti testuali in ambito clinico

- Sistemi intelligenti per l'analisi e la modellazione di reti di trasporto

Marco LIPPI

Interessi

- Modelli neuro-simbolici di intelligenza artificiale: combinazione di architetture neurali e metodi simbolici per compiti di apprendimento e ragionamento, in particolare per l'utilizzo di dati strutturati e relazionali e per l'integrazione della conoscenza.

- Analisi automatica di documenti testuali per vari problemi di classificazione e recupero di informazioni, utilizzando tecniche di elaborazione del linguaggio naturale e large language models (LLM): identificazione ed estrazione di argomenti da documenti testuali (argument mining), classificazione di documenti in base al contenuto semantico, identificazione e categorizzazione di clausole rilevanti in varie aree tematiche.

- Algoritmi di apprendimento automatico per l'analisi dei dati acquisiti da missioni di telerilevamento spaziale (Planetary Remote Sensing Missions).

- Informatica giuridica: strumenti di intelligenza artificiale al servizio della legge per la tutela dei consumatori. Analisi dei contratti (Termini di servizio) e delle informative sulla privacy (Privacy Policy) con l'obiettivo di individuare le clausole non congruenti con la normativa vigente.

- Sistemi intelligenti per l'analisi e la modellazione delle reti di trasporto: previsione del flusso di traffico utilizzando algoritmi di apprendimento statistico relazionale e modelli di apprendimento con periodicità stagionale.

- Implementazione di sistemi di apprendimento automatico nel contesto Industria 4.0 e Internet of Things (IoT): strumenti per l'identificazione di anomalie, apprendimento automatico di modelli causali, supporto alle decisioni.

- Bioinformatica: previsione della struttura tridimensionale delle proteine.

Marco LIPPI

Pubblicazioni

Legenda

Contributo su rivista |

Articolo su libro |

Libro |

Contributo in atti di convegno (proceeding) |

Brevetto |

Curatela | Altro

Altro |

Tesi di Dottorato

Baldi A.; Carnevali L.; Collodi G.; Lippi M.; Manes A. (2025). Multi-task neural networks for multi-step soil moisture forecasting in vineyards using Internet-of-Things sensors. SMART AGRICULTURAL TECHNOLOGY, vol. 10, pp. 1-7, ISSN:2772-3755 DOI

Carnevali L.; Lippi M. (2024). Neuro-Symbolic Artificial Intelligence for Safety Engineering. In: 19th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2024, 11th International Workshop on Next Generation of System Assurance Approaches for Critical Systems, SASSUR 2024, Towards A Safer Systems architecture Through Security, TOASTS 2024 and 7th International Workshop on Artificial Intelligence Safety Engineering, WAISE 2024 held in conjunction with the 43rd International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024, ita, 2024, Springer Science and Business Media Deutschland GmbH, vol. 14989, pp. 438-445, ISBN:9783031687372 DOI

Briglia G.; Immovilli F.; Cocconcelli M.; Lippi M. (2024). Bearing Fault Detection and Recognition from Supply Currents with Decision Trees. IEEE ACCESS, vol. 12, pp. 12760-12770, ISSN:2169-3536 DOI

Andrea Galassi; Francesca Lagioia; Agnieszka Jabłonowska; Marco Lippi (2024). Unfair clause detection in terms of service across multiple languages. ARTIFICIAL INTELLIGENCE AND LAW, pp. 0-0, ISSN:0924-8463 DOI

Nardoni V.; Lippi M.; Hyeraci G.; Maccari M.; Tarazjani A.D.; Virgili G.; Gini R.; Marinai S. (2024). Towards Automatically Filling Questionnaires from Clinical Records with Large Language Models. In: 8th Workshop on Natural Language for Artificial Intelligence, NL4AI 2024, ita, 2024, CEUR-WS, vol. 3877, pp. 0-0.

Andrea Galassi; Marco Lippi; Paolo Torroni (2023). Multi-Task Attentive Residual Networks for Argument Mining. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, vol. 31, pp. 1877-1892, ISSN:2329-9290 DOI

Lorello L.S.; Lippi M. (2023). The Challenge of Learning Symbolic Representations. In: 17th International Workshop on Neural-Symbolic Learning and Reasoning, NeSy 2023, La Certosa di Pontignano, ita, 2023, CEUR-WS, vol. 3432, pp. 44-61.

LIPPI, MARCO (2023). Cross-Load Generalization of Bearing Fault Recognition with Decision Trees. In: 7th International Conference on System Reliability and Safety, ICSRS 2023, ita, 2023, Institute of Electrical and Electronics Engineers Inc., pp. 400-406. DOI

Lippi M.; Martinelli M.; Picone M.; Zambonelli F. (2023). Enabling causality learning in smart factories with hierarchical digital twins. COMPUTERS IN INDUSTRY, vol. 148, pp. 0-0, ISSN:0166-3615 DOI

Locatelli A.; Iori M.; Lippi M.; Locatelli M. (2023). Setup Time Prediction Using Machine Learning Algorithms: A Real-World Case Study. In: IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023, nor, 2023, Springer Science and Business Media Deutschland GmbH, vol. 691, pp. 707-721, ISBN:9783031436697 DOI

Hadjidimitriou N.S.; Lippi M.; Mamei M. (2023). Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter. SOCIAL NETWORK ANALYSIS AND MINING, vol. 13, pp. 0-0, ISSN:1869-5450 DOI

LIPPI, MARCO (2023). A General Pipeline for Online Gesture Recognition in Human-Robot Interaction. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, vol. 53, pp. 315-324, ISSN:2168-2291 DOI

Monica R.; Saccuti A.; Aleotti J.; Lippi M. (2022). Detection of Unsorted Metal Components for Robot Bin Picking Using an Inexpensive RGB-D Sensor. In: 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022, deu, 2022, Institute of Electrical and Electronics Engineers Inc., vol. 2022-, pp. 0-0, ISBN:9781665499965 DOI

Lippi M.; Mariani S.; Martinelli M.; Zambonelli F. (2022). Individual and Collective Self-Development: Concepts and Challenges. In: 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, bgr, 2022, Institute of Electrical and Electronics Engineers Inc., pp. 15-21, ISBN:9788396589712 DOI

Hadjidimitriou N.S.; Lippi M.; Mamei M. (2022). Activity Imputation of Shared e-Bikes Travels in Urban Areas. In: 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, 2021, Springer Science and Business Media Deutschland GmbH, vol. 13163, pp. 442-456, ISBN:9783030954666 DOI

Brambilla G.; Rosi A.; Antici F.; Galassi A.; Giansanti D.; Magurano F.; Ruggeri F.; Torroni P.; Cisbani E.; Lippi M. (2022). Argument mining as rapid screening tool of COVID-19 literature quality: Preliminary evidence. FRONTIERS IN PUBLIC HEALTH, vol. 10, pp. 0-0, ISSN:2296-2565 DOI

LIPPI, MARCO (2022). Poka Yoke Meets Deep Learning: A Proof of Concept for an Assembly Line Application. APPLIED SCIENCES, vol. 12, pp. 0-0, ISSN:2076-3417 DOI

Ruggeri F.; Lagioia F.; Lippi M.; Torroni P. (2022). Detecting and explaining unfairness in consumer contracts through memory networks. ARTIFICIAL INTELLIGENCE AND LAW, vol. 30, pp. 59-92, ISSN:0924-8463 DOI

Lippi M.; Antici F.; Brambilla G.; Cisbani E.; Galassi A.; Giansanti D.; Magurano F.; Rosi A.; Ruggeri F.; Torroni P. (2022). AMICA: An Argumentative Search Engine for COVID-19 Literature. In: 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, Messe Wien, aut, 2022, International Joint Conferences on Artificial Intelligence, pp. 5932-5935, ISBN:9781956792003

de Oliveira M.B.; Zucchi G.; Lippi M.; Cordeiro D.F.; da Silva N.R.; Iori M. (2021). Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain. In: 23rd International Conference on Enterprise Information Systems, ICEIS 2021, 2021, Science and Technology Publications, Lda, vol. 1, pp. 634-641, ISBN:9789897585098

Drazewski K.; Galassi A.; Jablonowska A.; Lagioia F.; Lippi M.; Micklitz H.W.; Sartor G.; Tagiuri G.; Torroni P. (2021). A Corpus for Multilingual Analysis of Online Terms of Service. In: 3rd Natural Legal Language Processing, NLLP 2021, dom, 2021, Association for Computational Linguistics (ACL), pp. 1-8, ISBN:9781954085985

Jablonowska A.; Lagioia F.; Lippi M.; Micklitz H.-W.; Sartor G.; Tagiuri G. (2021). Assessing the Cross-Market Generalization Capability of the CLAUDETTE System. In: 34th International Conference on Legal Knowledge and Information Systems, JURIX 2021, ltu, 2021, IOS Press BV, vol. 346, pp. 62-67, ISBN:9781643682525 DOI

LIPPI, MARCO (2021). Attention in Natural Language Processing. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 32, pp. 4291-4308, ISSN:2162-237X DOI

LIPPI, MARCO (2021). Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?. COGNITIVE SCIENCE, vol. 45, pp. 0-0, ISSN:0364-0213 DOI

LIPPI, MARCO (2021). A Data Driven Approach to Match Demand and Supply for Public Transport Planning. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 22, pp. 6384-6394, ISSN:1524-9050 DOI

LIPPI, MARCO (2021). Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches. IOT, vol. 2, pp. 33-49, ISSN:2624-831X DOI

Lippi M.; Mariani S.; Zambonelli F. (2021). Developing a 'Sense of Agency' in IoT Systems: Preliminary Experiments in a Smart Home Scenario. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021, deu, 2021, Institute of Electrical and Electronics Engineers Inc., pp. 44-49, ISBN:9781665404242 DOI

Liepina R.; Ruggeri F.; Lagioia F.; Lippi M.; Drazewski K.; Torroni P. (2020). Explaining potentially unfair clauses to the consumer with the Claudette tool. In: SAE 2020 Automotive Technical Papers, WONLYAUTO 2020, usa, 2020, CEUR-WS, vol. 2645, pp. 61-64.

Lippi M.; Contissa G.; Lonowska A.J.; Lagioia F.; Micklitz H.-W.; Palka P.; Sartor G.; Torroni P. (2020). The Force Awakens: Artificial intelligence for consumer law. THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 67, pp. 169-190, ISSN:1076-9757 DOI

Loreti D.; Lippi M.; Torroni P. (2020). Parallelizing Machine Learning as a service for the end-user. FUTURE GENERATION COMPUTER SYSTEMS, vol. 105, pp. 275-286, ISSN:0167-739X DOI

Galassi A.; Kersting K.; Lippi M.; Shao X.; Torroni P. (2020). Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning. FRONTIERS IN BIG DATA, vol. 2, pp. 0-0, ISSN:2624-909X DOI

Galassi A.; Drazewski K.; Lippi M.; Torroni P. (2020). Cross-lingual Annotation Projection in Legal Texts. In: 28th International Conference on Computational Linguistics, COLING 2020, esp, 2020, Association for Computational Linguistics (ACL), pp. 915-926, ISBN:9781952148279

Lippi M.; Gianotti S.; Fama A.; Casali M.; Barbolini E.; Ferrari A.; Fioroni F.; Iori M.; Luminari S.; Menga M.; Merli F.; Trojani V.; Versari A.; Zanelli M.; Bertolini M. (2020). Texture analysis and multiple-instance learning for the classification of malignant lymphomas. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol. 185, pp. 0-0, ISSN:0169-2607 DOI

LIPPI, MARCO (2020). Machine Learning for Severity Classification of Accidents Involving Powered Two Wheelers. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 21, pp. 4308-4317, ISSN:1524-9050 DOI

LIPPI, MARCO (2020). Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data. APPLIED SCIENCES, vol. 10, pp. 0-0, ISSN:2076-3417 DOI

Cecaj A.; Lippi M.; Mamei M.; Zambonelli F. (2020). Forecasting Crowd Distribution in Smart Cities. In: 2020 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), IEEE, vol. 2020-, pp. 0-0. DOI

Lagioia F.; Ruggeri F.; Drazewski K.; Lippi M.; Micklitz H.-W.; Torroni P.; Sartor G. (2019). Deep learning for detecting and explaining unfairness in consumer contracts. In: 32nd International Conference on Legal Knowledge and Information Systems, JURIX 2019, Artificial Intelligence Department of the Technical University of Madrid, esp, 2019, IOS Press BV, vol. 322, pp. 43-52, ISBN:9781643680484 DOI

Liepin R.; Contissa G.; Drazewski K.; Lagioia F.; Lippi M.; Micklitz H.-W.; Palka P.; Sartor G.; Torroni P. (2019). GDPR privacy policies in CLAUDETTE: Challenges of omission, context and multilingualism. In: 3rd Workshop on Automated Semantic Analysis of Information in Legal Texts, ASAIL 2019, Universite de Montreal, can, 2019, CEUR-WS, vol. 2385, pp. 0-0.

Lippi M.; Contissa G.; Lagioia F.; Micklitz H.-W.; Palka P.; Sartor G.; Torroni P. (2019). Consumer protection requires artificial intelligence. NATURE MACHINE INTELLIGENCE, vol. 1, pp. 168-169, ISSN:2522-5839 DOI

Jaeger M.; Lippi M.; Pellegrini G.; Passerini A. (2019). Counts-of-counts similarity for prediction and search in relational data. DATA MINING AND KNOWLEDGE DISCOVERY, vol. 33, pp. 1254-1297, ISSN:1384-5810 DOI

Riguzzi F.; Kersting K.; Lippi M.; Natarajan S. (2019). Editorial: Statistical relational artificial intelligence. FRONTIERS IN ROBOTICS AND AI, vol. 6, pp. 0-0, ISSN:2296-9144 DOI

LIPPI, MARCO (2019). CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service. ARTIFICIAL INTELLIGENCE AND LAW, vol. 27, pp. 117-139, ISSN:0924-8463 DOI

LIPPI, MARCO (2019). Natural Language Statistical Features of LSTM-Generated Texts. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 30, pp. 3326-3337, ISSN:2162-237X DOI

Immovilli F.; Lippi M.; Cocconcelli M. (2019). Automated Bearing Fault Detection via Long Short-Term Memory Networks. In: 12th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019, fra, 2019, Institute of Electrical and Electronics Engineers Inc., pp. 452-458, ISBN:9781728118321 DOI

LIPPI, MARCO (2019). Distributed Speaking Objects: A Case for Massive Multiagent Systems. In: International Workshop on Massively Multi-agent Systems, MMAS 2018, swe, 2018, Springer Verlag, vol. 11422, pp. 3-20, ISBN:9783030209360 DOI

LIPPI, MARCO (2019). Argumentation-based coordination in IoT: A speaking objects proof-of-concept. In: 12th International Conference on Internet and Distributed Computing Systems, IDCS 2019, ita, 2019, Springer, vol. 11874, pp. 169-180, ISBN:9783030349134 DOI

LIPPI, MARCO (2019). Evaluating origin–destination matrices obtained from CDR data. SENSORS, vol. 19, pp. 0-0, ISSN:1424-8220 DOI

Mayer T.; Cabrio E.; Lippi M.; Torroni P.; Villata S. (2018). Argument mining on clinical trials. In: 7th International Conference on Computational Models of Argument, COMMA 2018, pol, 2018, IOS Press BV, vol. 305, pp. 137-148, ISBN:9781614999058 DOI

Lippi M.; Lagioia F.; Contissa G.; Sartor G.; Torroni P. (2018). Claim Detection in Judgments of the EU Court of Justice. In: VIII Workshop on Artificial Intelligence and the Complexity of Legal Systems (AICOL), Springer Verlag, vol. 10791, pp. 513-527. DOI

Lippi M.; Mamei M.; Zambonellli F. (2018). Predict Cellular network traffic with Markov Logic. In: 10th International Workshop on Agents in Traffic and Transportation, ATT 2018, swe, 2018, CEUR-WS, vol. 2129, pp. 9-14.

Contissa G.; Lagioia F.; Lippi M.; Micklitz H.-W.; Palka P.; Sartor G.; Torroni P. (2018). Towards consumer-empowering artificial intelligence. In: 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, swe, 2018, International Joint Conferences on Artificial Intelligence, vol. 2018-, pp. 5150-5157, ISBN:9780999241127 DOI

LIPPI, MARCO (2018). Can deep networks learn to play by the rules? A case study on nine Men's Morris. IEEE TRANSACTIONS ON GAMES, vol. 10, pp. 344-353, ISSN:2475-1510 DOI

Passon M.; Lippi M.; Serra G.; Tasso C. (2018). Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining. In: 5th Workshop on Argument Mining, co-located with EMNLP 2018, bel, 2018, Association for Computational Linguistics (ACL), pp. 35-39, ISBN:9781948087698

Galassi A.; Lippi M.; Torroni P. (2018). Argumentative Link Prediction using Residual Networks and Multi-Objective Learning. In: 5th Workshop on Argument Mining, ArgMining@EMNLP 2018, Association for Computational Linguistics, pp. 1-10.

Lippi M.; Mamei M.; Mariani S.; Zambonelli F. (2018). An argumentation-based perspective over the social IoT. IEEE INTERNET OF THINGS JOURNAL, vol. 5, pp. 2537-2547, ISSN:2327-4662 DOI

Contissa G.; Docter K.; Lagioia F.; Lippi M.; Micklitz H.-W.; Palka P.; Sartor G.; Torroni P. (2018). Automated processing of privacy policies under the EU general data protection regulation. In: 31st International Conference on Legal Knowledge and Information Systems, JURIX 2018, Het Kasteel, nld, 2018, IOS Press BV, vol. 313, pp. 51-60, ISBN:9781614999348 DOI

Lippi M.; Mamei M.; Mariani S.; Zambonelli F. (2017). Coordinating Distributed Speaking Objects. In: 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, J.W. Marriott Hotel, usa, 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1949-1960, ISBN:9781538617915 DOI

Lippi M.; Palka P.; Contissa G.; Lagioia F.; Micklitz H.-W.; Panagis Y.; Sartor G.; Torroni P. (2017). Automated detection of unfair clauses in online consumer contracts. In: 30th International Conference on Legal Knowledge and Information Systems, JURIX 2017, Kirchberg Campus of the University of Luxembourg, lux, 2017, IOS Press BV, vol. 302, pp. 145-154, ISBN:9781614998372 DOI

Gurevych I.; Lippi M.; Torroni P. (2017). Argumentation in social media. ACM TRANSACTIONS ON INTERNET TECHNOLOGY, vol. 17, pp. 0-0, ISSN:1533-5399 DOI

Petraro A.; Caselli F.; Milano M.; Lippi M. (2017). Driving behaviour clustering for realistic traffic micro-simulators. In: 31st European Conference on Modelling and Simulation, ECMS 2017, hun, 2017, European Council for Modelling and Simulation, pp. 18-24, ISBN:9780993244049 DOI

Lippi M. (2017). Reasoning with deep learning: An open challenge. In: 2016 AI*IA Workshop on Deep Understanding and Reasoning: A Challenge for Next-Generation Intelligent Agents, URANIA 2016, ita, 2016, CEUR-WS, vol. 1802, pp. 38-43.

LIPPI, MARCO (2016). MARGOT: A web server for argumentation mining. EXPERT SYSTEMS WITH APPLICATIONS, vol. 65, pp. 292-303, ISSN:0957-4174 DOI

LIPPI, MARCO (2016). Argumentation mining: State of the art and emerging trends. ACM TRANSACTIONS ON INTERNET TECHNOLOGY, vol. 16, pp. 1-25, ISSN:1533-5399 DOI

LIPPI, MARCO (2016). Optimally solving permutation sorting problems with efficient partial expansion bidirectional heuristic search. AI COMMUNICATIONS, vol. 29, pp. 513-536, ISSN:0921-7126 DOI

LIPPI, MARCO (2016). Semantic video labeling by developmental visual agents. COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 146, pp. 9-26, ISSN:1077-3142 DOI

LIPPI, MARCO (2015). Statistical relational learning for game theory. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, vol. PP, pp. 412-425, ISSN:1943-068X DOI

LIPPI, MARCO (2015). Argument mining: A machine learning perspective. In: 3rd International Workshop on Theory and Applications of Formal Argumentation, TAFA 2015, arg, 2015, Springer Verlag, vol. 9524, pp. 163-176, ISBN:9783319284590 DOI

LIPPI, MARCO (2015). Context-independent claim detection for argument mining. In: 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, arg, 2015, International Joint Conferences on Artificial Intelligence, vol. 2015-, pp. 185-191.

LIPPI, MARCO (2015). En plein air visual agents. In: 18th International Conference on Image Analysis and Processing, ICIAP 2015, ita, 2015, Springer Verlag, vol. 9280, pp. 697-709, ISBN:9783319232331 DOI

LIPPI, MARCO (2014). On-line video motion estimation by invariant receptive inputs. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, usa, 2014, IEEE Computer Society, pp. 726-731. DOI

Paolo Frasconi;Francesco Gabbrielli;Marco Lippi;Simone Marinai (2014). Markov Logic Networks for Optical Chemical Structure Recognition. JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 54, pp. 2380-2390, ISSN:1549-9596 DOI Accesso ONLINE all'editore

Marco Lippi;Matteo Bertini;Paolo Frasconi (2013). Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 14, pp. 871-882, ISSN:1524-9050 DOI Accesso ONLINE all'editore

Manfred Jaeger;Marco Lippi;Andrea Passerini;Paolo Frasconi (2013). Type Extension Trees for feature construction and learning in relational domains. ARTIFICIAL INTELLIGENCE, vol. 204, pp. 30-55, ISSN:0004-3702 DOI Accesso ONLINE all'editore

LIPPI, MARCO (2013). On-line laplacian one-class support vector machines. In: 23rd International Conference on Artificial Neural Networks, ICANN 2013, Sofia, bgr, 2013, Springer, vol. 8131, pp. 186-193, ISBN:9783642407277 DOI

LIPPI, MARCO (2013). Information-based learning of deep architectures for feature extraction. In: 17th International Conference on Image Analysis and Processing, ICIAP 2013, Naples, ita, 2013, Springer, vol. 8157, pp. 101-110, ISBN:9783642411830 DOI

LIPPI, MARCO (2013). Variational foundations of online backpropagation. In: 23rd International Conference on Artificial Neural Networks, ICANN 2013, Sofia, bgr, 2013, Springer, vol. 8131, pp. 82-89, ISBN:9783642407277 DOI

M. Lippi; M. Ernandes; A. Felner (2012). Efficient Single Frontier Bidirectional Search. In: Proceedings of the Fifth Annual Symposium on Combinatorial Search, Niagara Falls, Ontario, Canada, July 2012, AAAI Press, pp. 49-56, ISBN:9781577355847

Passerini, Andrea; Lippi, Marco; Frasconi, Paolo (2012). Predicting metal-binding sites from protein sequence. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, vol. 9, pp. 203-213, ISSN:1545-5963 DOI Accesso ONLINE all'editore

M. Lippi; A. Passerini; M. Punta; P. Frasconi (2012). Metal Binding in Proteins: Machine Learning Complements X-Ray Absorption Spectroscopy. In: European Conference on Machine Learning and Knowledge Discovery in Databases, Bristol, September 2012, Springer, vol. 7524, pp. 854-857, ISBN:9783642334856 DOI

M. Lippi; L. Menconi; M. Gori (2012). Balancing recall and precision in stock market predictors using support vector machines. In: 22nd Italian Workshop on Neural Nets, WIRN 2012, Vietri sul Mare (SA), Springer, pp. 1-8, ISBN:9783642354663 Accesso ONLINE all'editore

LIPPI, MARCO (2012). Information theoretic learning for pixel-based visual agents. In: 12th European Conference on Computer Vision, ECCV 2012, Florence, ita, 2012, Springer, vol. 7577, pp. 864-875, ISBN:9783642337826 DOI

W. Shi; M. Punta; J. Bohon; J.M. Sauder; R D'Mello; M. Sullivan; J. Toomey; D. Abel; M. Lippi; A. Passerini; P. Frasconi; S.K. Burley; B. Rost; M.R. Chance (2011). Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy. GENOME RESEARCH, vol. 21, pp. 898-907, ISSN:1088-9051 DOI Accesso ONLINE all'editore

Marco Lippi; Manfred Jaeger; Paolo Frasconi; Andrea Passerini (2011). Relational information gain. MACHINE LEARNING, vol. 83, pp. 219-239, ISSN:0885-6125 DOI Accesso ONLINE all'editore

A. Passerini; M. Lippi; P. Frasconi (2011). MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence. NUCLEIC ACIDS RESEARCH, vol. Web server issue, pp. 0-0, ISSN:0305-1048 DOI Accesso ONLINE all'editore

L. Menconi; M. Gori; M. Lippi (2011). Computational models for short-term prediction of the stock market. INTELLIGENZA ARTIFICIALE, vol. 5, pp. 217-227, ISSN:1724-8035 DOI Accesso ONLINE all'editore

Marco Lippi;Matteo Bertini;Paolo Frasconi (2010). Collective Traffic Forecasting. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010, Barcelona; Spain, 20-24 settembre 2010, Springer, vol. 6322, pp. 259-273, ISBN:9783642158827 DOI

M. Lippi;P. Frasconi (2009). Prediction of Protein Beta-Residue Contacts by Markov Logic Networks with Grounding Specific Weights. BIOINFORMATICS, vol. 25, pp. 2326-2333, ISSN:1367-4803 DOI Accesso ONLINE all'editore

M. Lippi (2009). Statistical learning for relational and structured data. (Tutor: P. Frasconi)

F. Costa; A. Passerini; M. Lippi; P. Frasconi (2008). A Semiparametric Generative Model for Efficient Structured-Output Supervised Learning. ANNALS OF MATHEMATICS AND OF ARTIFICIAL INTELLIGENCE, vol. 1-3, pp. 207-222, ISSN:1012-2443 DOI

M. Lippi;A. Passerini;M. Punta;B. Rost;P. Frasconi (2008). MetalDetector: a web server for predicting metal binding sites and disulfide bridges in proteins from sequence. BIOINFORMATICS, vol. 24, pp. 2094-2095, ISSN:1367-4803 DOI Accesso ONLINE all'editore

Marco LIPPI

Biography

Marco Lippi was born in Pistoia on May 23rd, 1983. He received his BSc and MSc degrees in computer engineering from the University of Florence in 2004 and 2006, respectively. In 2010, he received the PhD in computer and automation engineering. He held positions as a postdoc at the Universities of Florence (2010-2011), Siena (2011-2014) and Bologna (2014-2016), and was a visiting scholar in 2014 at the Université Pierre et Marie Curie in Paris. From 2016 to 2024 he was a tenure-track fixed-term researcher (RTD-B) (2016-2019) and then associate professor (2019-2024) at the University of Modena and Reggio Emilia. Since March 2024, he is an associate professor at the University of Florence.

Marco LIPPI

Curriculum

Education:

- BSc in computer engineering, University of Florence, 2004 (grade: 110/110 cum laude).

- MSc in computer engineering, University of Florence, 2006 (grade: 110/110 cum laude, with commendation).

- PhD in computer and automation engineering, University of Florence, 2010.

Academic positions:

- Research fellow, University of Florence, 2010-2011

- Research fellow, University of Siena, 2011-2014

- Research fellow, University of Bologna, 2014-2016

- Fixed-term type B researcher, University of Modena and Reggio Emilia, 2016-2019

- Associate professor, University of Modena and Reggio Emilia, 2019-2024

- Associate professor, University of Florence, 2024-today

Main projects:

- Responsible (principal investigator) for the AMICA (Argument Mining in Covid-19 Articles) project funded by the Ministry of University and Research within the FISR COVID-19 program, 2021.

- Participation as a researcher in the TAILOR (Foundations of Trustworthy AI -- Integrating Reasoning, Learning and Optimization) project, network of excellence, funded by the H2020-ICT-48-2020 call, 2020-2024.

- Participation as a researcher and local unit manager as a subcontractor (UniMORE) for the POR-FESR 2014--2020 project funded by Regione Toscana “Semantic Clauses Understanding and Detection” (SCUDO), 2021-2022.

- Participation as a researcher and local unit manager (UniMORE) in the CLAUDETTE project (http://claudette.eui.eu) funded by the Research Council of the European University Institute, for the development of a system for automatic recognition of potentially fraudulent clauses in online contracts (2018).

Interests:

- Neuro-symbolic artificial intelligence

- Interpretable and explainable artificial intelligence

- Legal informatics: artificial intelligence tools at the service of consumer protection law.

- Machine learning algorithms for the analysis of data acquired from Planetary Remote Sensing Missions (SPM) space missions

- Natural language processing for automatic analysis of textual documents in clinical settings

- Intelligent systems for the analysis and modeling of transportation networks

Marco LIPPI

Interest

- Neuro-symbolic models of artificial intelligence: combination of neural architectures and symbolic methods for learning and reasoning tasks, especially for using structured and relational data, and for knowledge integration.

- Automatic analysis of textual documents for various classification and information retrieval problems, using natural language processing techniques and large language models: identification and extraction of arguments from textual documents (argument mining), ranking of documents based on semantic content, identification and categorization of relevant clauses in various subject areas.

- Machine learning algorithms for the analysis of data acquired from space-based remote sensing missions (Planetary Remote Sensing Missions)

- Legal Informatics: artificial intelligence tools serving the law to protect consumers. Analysis of contracts (Terms of Service) and privacy notices (Privacy Policies) with the aim of determining clauses that are not congruent with existing legislation.

- Intelligent systems for the analysis and modeling of transportation networks: prediction of traffic flow using relational statistical learning algorithms and learning models with seasonal periodicity.

- Implementation of machine learning systems in the context of Industry 4.0 and Internet of Things (IoT): tools for anomaly identification, machine learning of causal models, decision support.

- Bioinformatics: prediction of the three-dimensional structure of proteins (metal sites and contact maps) by means of relational models combined with neural networks, prediction of the secondary structure of RNA by means of stochastic grammars and logical-probabilistic models.