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Alessio Micheli

Alessio Micheli

D-Index & Metrics

Computer Science

D-Index
36
Citations
5997
World Ranking
11200
National Ranking
360

Overview

Alessio Micheli is a researcher affiliated with the University of Pisa in Italy with significant contributions in the fields of Computer Science and Engineering. Their work primarily spans subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Molecular Biology, Cardiology and Cardiovascular Medicine, and Statistical and Nonlinear Physics.

Their research is closely linked to key topics including Neural Networks and Reservoir Computing, Advanced Memory and Neural Computing, Advanced Graph Neural Networks, Neural Networks and Applications, Computational Drug Discovery Methods, Complex Network Analysis Techniques, and Machine Learning in Materials Science.

Alessio Micheli has coauthored publications with several frequent collaborators, notably Claudio Gallicchio, Domenico Tortorella, Marco Podda, Davide Bacciu, and Federico Errica.

Their research has been disseminated widely in various publication venues. The most frequent include arXiv (Cornell University), Neurocomputing, CINECA IRIS Institutional Research Information System (University of Pisa), IEEE Transactions on Neural Networks and Learning Systems, and EP Europace.

Notable recent papers authored or coauthored by Alessio Micheli include:

  • "Discrete-time dynamic graph echo state networks", 2022, Neurocomputing

Other important papers in related research fields, authored by colleagues with overlapping topics, are as follows:

  • "A gentle introduction to deep learning for graphs", 2020, Neural Networks
  • "Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications", 2024, IEEE Transactions on Neural Networks and Learning Systems
  • "A Deep Generative Model for Fragment-Based Molecule Generation", 2020, arXiv (Cornell University)
  • "Antibody design using deep learning: from sequence and structure design to affinity maturation", 2024, Briefings in Bioinformatics

The integration of methodologies across artificial intelligence, graph neural networks, and computational drug discovery highlights the multidisciplinary nature of the research work associated with Alessio Micheli. Their contributions feature a focus on advanced neural network architectures, memory models, graph computations, and applications relevant to molecular biology and medicine.

Best Publications

  • Neural Network for Graphs: A Contextual Constructive Approach

    A. Micheli

  • Deep reservoir computing: A critical experimental analysis

    Claudio Gallicchio;Alessio Micheli;Luca Pedrelli

  • A Gentle Introduction to Deep Learning for Graphs

    Davide Bacciu;Federico Errica;Alessio Micheli;Marco Podda

  • Design of deep echo state networks

    Claudio Gallicchio;Alessio Micheli;Luca Pedrelli

  • Architectural and Markovian factors of echo state networks

    Claudio Gallicchio;Alessio Micheli

  • Graph Echo State Networks

    Claudio Gallicchio;Alessio Micheli

  • Recursive self-organizing network models

    Barbara Hammer;Alessio Micheli;Alessandro Sperduti;Marc Strickert

  • Echo State Property of Deep Reservoir Computing Networks

    Claudio Gallicchio;Alessio Micheli

  • Human Activity Recognition using Multisensor Data Fusion based on Reservoir Computing

    Filippo Palumbo;Filippo Palumbo;Claudio Gallicchio;Rita Pucci;Alessio Micheli

  • A Fair Comparison of Graph Neural Networks for Graph Classification

    Federico Errica;Marco Podda;Davide Bacciu;Alessio Micheli

  • A general framework for unsupervised processing of structured data

    Barbara Hammer;Alessio Micheli;Alessandro Sperduti;Marc Strickert

  • An experimental characterization of reservoir computing in ambient assisted living applications

    Davide Bacciu;Paolo Barsocchi;Stefano Chessa;Claudio Gallicchio

  • Application of Cascade Correlation Networks for Structures toChemistry

    Anna Maria Bianucci;Alessio Micheli;Alessandro Sperduti;Antonina Starita

  • Analysis of the internal representations developed by neural networks for structures applied to quantitative structure--activity relationship studies of benzodiazepines.

    Alessio Micheli;Alessandro Sperduti;Antonina Starita;Anna Maria Bianucci

  • IoT European Large-Scale Pilots – Integration, Experimentation and Testing

    Ovidiu Vermesan;Arne Bröring;Elias Z. Tragos;Martin Serrano

  • Tree Echo State Networks

    Claudio Gallicchio;Alessio Micheli

  • Fast and Deep Graph Neural Networks

    Claudio Gallicchio;Alessio Micheli

  • Contextual processing of structured data by recursive cascade correlation

    A. Micheli;D. Sona;A. Sperduti

  • A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor

    Marco Podda;Davide Bacciu;Alessio Micheli;Roberto Bellù

  • Ionic liquids: prediction of their melting points by a recursive neural network model

    Riccardo Bini;Cinzia Chiappe;Celia Duce;Alessio Micheli

  • Deep Echo State Network (DeepESN): A Brief Survey

    Claudio Gallicchio;Alessio Micheli

Frequent Co-Authors

Alessandro Sperduti
Alessandro Sperduti University of Padua
Stefano Chessa
Stefano Chessa University of Pisa
Barbara Hammer
Barbara Hammer Bielefeld University
Alessandro Saffiotti
Alessandro Saffiotti Örebro University
Liam Maguire
Liam Maguire University of Ulster
TM McGinnity
TM McGinnity University of Ulster
Gregory M. P. O'Hare
Gregory M. P. O'Hare Trinity College Dublin
Jane Hunter
Jane Hunter University of Technology Sydney
Roberto Barale
Roberto Barale University of Pisa
Haibo He
Haibo He University of Rhode Island

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