Amedeo Buonanno, Ph.D.
Research Collaborator
Researcher, ENEA, Portici Research Centre ‑ Italy
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amedeobuonanno
Biography
Amedeo Buonanno received the Laurea degree (MS) in Computer Engineering, cum laude, in 2004 from the University of Naples Federico II with a thesis in Industrial Robotics at Prisma Lab.
Since 2006, for 13 years, he has worked at the R&D Laboratory of Esaote S.p.A as Industrial Researcher and Software Engineer. He has been involved on several research projects that aim to improve the clinical patient information using Image Analysis, Image Processing and Computer Vision techniques on Magnetic Resonance images. In more than 10 years of experience as software engineer he has worked in various companies and on several software projects as analyst, designer, developer and team leader.
In the summer of 2008 he was a visiting researcher at the Massachusetts Institute of Technology (Senseable City Lab), where he used clustering algorithms in order to understand the dynamic of urban areas and behavior of the city using data gathered from cell phone networks.
In November 2012 he became a PhD student at the Dipartimento di Ingegneria Industriale e dell’Informazione at Second University of Naples (SUN) [now University of Campania "L. Vanvitelli"]. On 29 January 2016 he has successfully defended his PhD Thesis entitled "Probabilistic Bi-directional Networks for Inference and Learning".
Since December 2018, he has been working as Researcher at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) in the Energy Technologies Department.
His research interests are in the areas of Machine Learning, Deep Learning and Probabilistic Graphical Models.
He is a member of the IEEE, IEEE Signal Processing Society, IEEE Computational Intelligence Society and Italian Association for Machine Learning.
Publications
Papers:
2021
F.A.N. Palmieri, K.R. Pattipati, G. Fioretti, F. Verolla, G. Di Gennaro and A. Buonanno, "Probability Propagation for Path Planning on Semantic Maps and Partially Unknown Environment," submitted to journal....
G. Di Gennaro, A. Buonanno and F.A.N. Palmieri, "Considerations About Learning Word2Vec," in The Journal of Supercomputing, Springer, pp. 1-16, 6 Apr. 2021.
G. Di Gennaro, A. Buonanno and F.A.N. Palmieri, "Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model," in Soft Computing, Springer, pp. 1-12, 17 Mar. 2021.
A. Buonanno, A. Nogarotto, G. Cacace, G. Di Gennaro, F.A.N. Palmieri, M. Valenti and G. Graditi, "Bayesian Feature Fusion using Factor Graph in Reduced Normal Form," in Applied Sciences, MDPI, vol. 11, 1934, 22 Feb. 2021.
F.A.N. Palmieri, K.R. Pattipati, G. Fioretti, G. Di Gennaro and A. Buonanno, "Path Planning Using Probability Tensor Flows," in Aerospace and Electronic Systems Magazine, IEEE, vol. 36, no. 1, pp. 34-45, 1 Jan. 2021.
2020
G. Di Gennaro, A. Buonanno, A. Di Girolamo, A. Ospedale and F.A.N Palmieri, "An Analysis of Word2Vec for the Italian Language," in Progress in Artificial Intelligence and Neural Systems (Smart Innovation, Systems and Technologies 184), A. Esposito, M. Faundez-Zanuy, F.C. Morabito and E. Pasero, Eds., Springer, 2020, pp. 137–146.
G. Di Gennaro, A. Buonanno, A. Di Girolamo, A. Ospedale and F.A.N Palmieri, "Intent Classification in Question-Answering Using LSTM Architectures," in Progress in Artificial Intelligence and Neural Systems (Smart Innovation, Systems and Technologies 184), A. Esposito, M. Faundez-Zanuy, F.C. Morabito and E. Pasero, Eds., Springer, 2020, pp. 115–124.
F.A.N. Palmieri, K.R. Pattipati, G. Fioretti, F. Verolla, G. Di Gennaro and A. Buonanno, "Exploration/Exploitation in Path Planning Using Probability Propagation," in Proc. IEEE International Conference on Robotics and Automation (ICRA2020), 2nd Workshop on Long-Term Human Motion Prediction (LHMP), June 2, 2020.
2019
F.A.N. Palmieri, M. Baldi, A. Buonanno and G. Di Gennaro, "Information-Preserving Networks and the Mirrored Transform," in Proc. XXIX IEEE International Workshop on Machine Learning for Signal Processing (MLSP2019), Pittsburgh, PA, USA, Oct. 13–16, 2019.
F.A.N. Palmieri, M. Baldi, A. Buonanno, G. Di Gennaro and F. Ospedale, "Probing a Deep Neural Network," in Neural Approaches to Dynamics of Signal Exchanges (Smart Innovation, Systems and Technologies 151), A. Esposito, M.Faundez-Zanuy, F.C. Morabito and E. Pasero, Eds., Springer, 2019, pp. 201–211.
2018
G. Di Gennaro, A. Buonanno and F.A.N. Palmieri, "Computational Optimization for Normal Form Realization of Bayesian Model Graphs," in Proc. XXVIII IEEE International Workshop on Machine Learning for Signal Processing (MLSP2018), Aalborg, DK, Sept. 17–20, 2018. → Finalist for the best student paper award!
A. Buonanno, P. Iadicicco, G. Di Gennaro, F.A.N. Palmieri, "Context Analysis Using a Bayesian Normal Graph," in Neural Advances in Processing Nonlinear Dynamic Signals (Smart Innovation, Systems and Technologies 102), A. Esposito, M. Faundez-Zanuy, F.C. Morabito and E. Pasero, Eds., Springer, 2018, pp. 85–96.2016
A. Buonanno, L. di Grazia , F.A.N. Palmieri (2016) "Bayesian Clustering on Images with Factor Graphs in Reduced Normal Form". In: Bassis S., Esposito A., Morabito F., Pasero E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham
2015
F. A. N. Palmieri, A Buonanno, "Discrete Independent Component Analysis (DICA) with Belief Propagation", Proc. of IEEE Machine Learning for Signal Processing Conference (MLSP2015), Sept. 17-20, 2015, Boston, MA, USA.
A. Buonanno and F. A. N. Palmieri, "Two-Dimensional Multi-layer Factor Graphs in Reduced Normal Form", International Joint Conference on Neural Networks, IJCNN2015, July 12-17, 2015, Killarney, Ireland.
A. Buonanno, F.A.N. Palmieri (2015) "Simulink Implementation of Belief Propagation in Normal Factor Graphs". In: Bassis S., Esposito A., Morabito F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham
F. A. N. Palmieri and Amedeo Buonanno, "Discrete Independent Component Analysis (DICA) with Belief Propagation", 2015, ArXiv.
A. Buonanno and F. A. N. Palmieri, "Towards Building Deep Networks with Bayesian Factor Graphs", 2015, ArXiv.2014
F. A. N. Palmieri and A. Buonanno, "Belief Propagation and Learning in Convolution Multi-layer Factor Graphs", Proceedings of 4th International Workshop on Cognitive Information Processing, CIP2014, May 26-28, 2014, Copenhagen, Denmark.
2013
L. Balbi, A. Buonanno, P. Pellegretti, A. Serra, R. Varriale, M. Vicari, "Biomedical image reconstruction method", 2013.
2009
F. Calabrese, A. Buonanno, "Development of a STEP-NC Network Management Protocol for Decentralized Manufacturing", Advanced Design and Manufacturing Based on STEP, Springer-Verlag London, 2009.