Formazione ed abilitazioni
2007: Ph.D. in Fisica presso il Dipartimento di Fisica, Università di Parma (Italia)
2004: Laurea in Fisica (VO) presso il Dipartimento di Fisica, Università di Parma (Italia)
2022: Abilitazione Scientifica Nazionale in Fisica Matematica (01/A4) come Professore di I fascia
2018: Abilitazione Scientifica Nazionale in Fisica Teorica delle Interazioni Fondamentali (02/A2) come Professore di II fascia
2013: Abilitazione Scientifica Nazionale in Fisica Teorica della Materia (02/B2) come Professore di II fascia
Principali incarichi accademici (dal 2020)
2022-presente: Professore associato in Fisica Matematica presso il Dipartimento di Matematica, Sapienza Università di Roma (Italia)
2019-2022: Ricercatore a tempo determinato (RTD-B) presso il Dipartimento di Matematica, Sapienza Università di Roma (Italia)
2015-2019: Ricercatore a tempo determinato (RTD-A) presso il Dipartimento di Matematica, Sapienza Università di Roma (Italia)
2014: Assegnista di ricerca presso il Dipartimento di Fisica, Sapienza Università di Roma (Italia)
2010-2013: Ricercatore a tempo determinato (RTD) e Coordinatore locale del progetto FIRB “Dynamics and statistical mechanics of lymphocyte networks below the percolation threshold”, presso il Dipartimento di Fisica, Università di Parma (Italia)
2010: Borsista “Fondazione Angelo della Riccia”, presso il Laboratoire de Physique Théorique de la Matière Condensée, Université Pierre et Marie Curie, Parigi (Francia)
2008: Borsista “Fondazione Angelo della Riccia”, presso l'Albert-Ludwigs-Universität, Friburgo (Germania)
Principali incarichi didattici (dal 2020)
2019/20-2023/24: “Mathematical models for Neural Networks” Sapienza Università di Roma - Docente
2019/20-2023/24: “Calcolo e Biostatistica con Metodi matematici e Informatici applicati alla Biologia” Sapienza Università di Roma - (Co-)Docente
Dal 2022: Membro del Collegio di Dottorato in Matematica, Sapienza Università di Roma
Dal 2018: Membro della Commissione Placement per i corsi di laurea in “Matematica” e “Matematica per le applicazioni”, Sapienza Università di Roma
Fondi di ricerca come PI (dal 2020)
2022: PNRR grant PE1 spoke 5 - “Foundations of High-Quality AI” (prot. PE1221852F8F23A5) - linea di ricerca “Theory of information-processing in (deep) Neural Networks”
2022: Premio per ricercatori - Fondo sociale Europe plus (FSE+)
2021: Fondi Ateneo Sapienza per condurre indagini su “Rigorous approaches to the study of collective behaviors” (prot. RM12117A8590B3FA)
2020: Fondi Ateneo Sapienza per condurre indagini su “Mathematical methods and models for complex systems” (prot. RM120172B8066CB0)
Comitati Editoriali (dal 2020)
Since 2022: Main Editor for Physica A: Statistical Mechanics and its Applications
2019-2023: Membro del Divisional Associate Editor for Physical Review Letters
2017-2022: Membro dell'Advisory Panel for the Journal of Physics A: Mathematical and Theoretical
Seminari e colloquia su invito (dal 2020)
28 Maggio: Seminario su invito “Complexity and Machine Learning”, Dipartimento di Matematica, Goethe-Universität Frankfurt (Germania)
16 Maggio 2024: Colloquium “Machine Learning: from mammal's brain to statistical mechanics”, Frascati National Laboratories (”Spring School 'Bruno Touschek' in Nuclear, Subnuclear and Astroparticle Physics”)
4 Dicembre 2023: Colloquium “A statistical-mechanics approach to (shallow) neural networks”, School of Physical and Mathematical Sciences, Nanyang Technology University (Singapore)
3 Maggio 2023: Seminario su invito “Mathematical methods for Boltzmann machines”, Università of Salento (Italy)
12 Gennaio 2023: Seminario su invito “Dense neural networks that learn, store and retrieve”, Istituto Superiore di Sanità, Roma
25 Gennaio 2022: Seminario su invito “The emergence of concepts in shallow neural-networks”, Mathematical Institute, University of Oxford (UK)
19 Novembre 2021: Seminario su invito “Some rigorous results on Boltzmann machines”, Università of South Wales, Pontypridd (UK)
10 Maggio 2021: Seminario su invito “From Memory to Learning and backward”, Università of Salento
25 Novembre 2020: Seminario su invito “Complexity in neural networks: the good with the bad”, Institute for Cross-Disciplinary Physics & Complex Systems, Palma de Mallorca (Spagna)
1 Luglio 2020: Seminario su invito “Machine Learning from a Mathematical perspective”, ISMAR-CNR, Roma
Contributi in conferenze su invito (dal 2020)
22-24 Maggio 2024: “From Machine-Learning Theory to Driven Complex Systems and back” CECAM-HQ-EPFL, Lausanne (Switzerland) contributo su invito “Information processing from classical-conditioning to contrastive-divergence”
15 Febbrario 2024: “FAIR-Meeting Spoke 5” Department of Computer, Control and Management Engineering (DIAG), Sapienza University of Roma (Italy) contributo su invito “Associative neural networks beyond retrieval: learning and generalization on structured data sets”
31 Gennaio 2024: “Dialogs on Neuronal Networks: Theoretical and Biological Perspectives” Istituto Superiore di Sanità, Roma (Italy) contributo su invito “Biological inspired Machine Learning”
15 Settembre 2023: “RoMaDS: A day on Statistical Physics for Machine Learning”, Department fo Mathematics, University of Roma Tor Vergata (Italy) contributo su invito “The early bird regularises better”
11-16 Giugno 2023: “Mathematical Physics of Complex Systems”, Palazzone di Cortona (Italy) invited talk “The early bird regularises better”
30 Maggio 2023: “Hard sciences for machine learning”, Auditorium dell’Area della Ricerca del CNR, Pisa (Italy) contributo su invito “The emergence of concepts in shallow neural-networks”
26-31 Marzo 2023: “DPG-Frühjahrstagung (DPG Spring Meeting) - DPG Focus Session: Physics meets ML”, Technical University of Dresden (Germany) contributo su invito “The emergence of concepts in shallow neural-networks”
2 Dicembre 2022: “Guerra80: Interpolating maxima of rugged landscapes”, Sapienza University of Rome (Italy) contributo su invito “Recurrent neural networks that generalize from examples and improve by dreaming”
24 Ottobre 2022: “From Dispersive Hydrodynamics to Forecasting, Machine Learning and Back”, Isaac Newton Institute, Cambridge (UK) contributo su invito “A Crossroad between Statistical Mechanics, PDE-Theory, Neural networks and Machine learning”
13-14 Giugno 2022: “Random graphs and Networks Workshop”, Mathematics Department of the University of South Wales (UK) contributo su invito “Complex (neural) networks for complex tasks”
5 Agosto 2021: contributo su invito per il Sci-ML webinar series “Learning from Storing”, Carnegie Mellon University, Pittsburgh (US)
22 Luglio 2021: Invited panelist at the workshop “Physical Review Referee Orientation Session”, organizzato by the American Physical Society
15-18 Marzo 2021: “Machine Learning and Statistical Physics”, Lagrange Mathematics and Computation Research Center , Paris (Francia), contributo su invito “Learning from Storing”
24-26 Febbraio 2020: “International Max-Planck Research School - Physics of Biological and Complex Systems”, Göttingen (Germania), contributo su invito “Complexity in neural networks: the good with the bad”
Commissioni scientifiche e attività di revisione (dal 2020)
Revisore per le seguenti istituzioni:
2023: Serrapilheira Institute (Brazil)
2023: External jury member for the “Machine Learning track” at the Radboud science faculty (The Netherlands)
2021: Agencia Nacional de Investigación y Desarrollo (Gobierno de Chile)
2021: University of Insubria
2020: Polish National Science Center
2020: Alexander von Humboldt Foundation
2020: IMéRA: Institut d'études avancées d’Aix-Marseille (Foundation of Université d’Aix-Marseille)
2020: Swiss National Science Foundation
Membro delle seguenti commissioni di valutazione:
2024: Member of the Assessment Committee for the upgrade from Tenure Track position (RTD-B) to Associate Professor in Mathematical Physics at University of L’Aquila (Italy)
2024: Member of the Selection Committee for a tenure-track position on “Statistical Physics and Machine Learning” at Ecole Normale Supérieure, Paris (France)
2024: Member of the Selection Committee for an Assistant/Associate Professor level in Theoretical Biophysics at Radboud University, Nijmegen (The Netherlands)
2024: Member of the Selection Committee for a Tenure Track position (RTT) in Mathematical Physics at University of Modena and Reggio Emilia (Italy)
2024: Member of the Assessment Committee for the PhD programme in Mathematics at University of Bologna (Italy)
2023: Member of the Selection Committee for a Teaching assignment in the Faculty of Science at the University Sapienza of Roma (Italy)
2023: Member of the Assessment Committee for the PhD programme in Physics at University Tor Vergata, Roma (Italy)
2022-2023: Member of the monitoring panel for the AI National Ph.D. program
2022: Member of the Selection Committee for a one-year Fellowship in the field of Mathematical Physics and Probability and Mathematical Statistics at the University Sapienza of Roma (Italy)
2022: Member of the Selection Committee for Tutoring fellowships in the Faculty of Science at the University Sapienza of Roma (Italy) [twice]
2021: Member of the Selection Committee for a one-year Fellowship in the field of Mathematical Physics and Probability and Mathematical Statistics at the University Sapienza of Roma (Italy)
2020: Member of the Selection Committee for a two-year Fellowship in the field of Mathematical and its applications at the University Sapienza of Roma (Italy)
2020: Member of the Assessment Committee for the PhD programme in Physics at the Laboratories de Physique de l’École Normale Supérieure, Paris (France)
Sintesi dei risultati scientifici
Production_ Publications (ISI): 132 Scientific Book Chapters: 3 Referred Proceedings: 8
Impact_ Total Citations 2856 h-index: 30 Source: GoogleScholar
Spreading_ Invited Seminars: 69 Invited lecturer for PhD programs: 3 Organized Intl. Workshops: 7
Pubblicazioni (dal 2020)
E. A., F. Alemanno, Aquaro, A. Fachechi, Regularization, early stopping and dreaming, Neur. Netw. 177, 106389 (2024).
E. A., A. Fachechi, D. Luongo, Algebraic properties for Hebbian-like kernels, Appl. Math. Comp. 474, 128689 (2024).
M. Aquaro, F. Alemanno, I. Kanter, F. Durante, A. Barra, E. Agliari, Hebbian dreaming for small datasets, Neur. Netw. 173, 106174 (2024).
E. A., A. Alessandrelli, A. Barra, F. Ricci-Tersenghi, Parallel learning by multitasking neural networks, J. Stat. 113401 (2023).
E. A., F. Alemanno, Aquaro, A. Barra, Ultrametric identities in glassy models of Natural Evolution, J. Phys. A 56, 385001 (2023).
E. A., L. Albanese, F. Alemanno, A. Alessandrelli, A. Barra, F. Giannotti, D. Lotito, and D. Pedreschi. Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning, Physica A 627, 129143 (2023).
E. A., L. Albanese, F. Alemanno, A. Alessandrelli, A. Barra, F. Giannotti, D. Lotito, and D. Pedreschi, Dense Hebbian neural networks: a replica symmetric picture of supervised learning, Physica A 626, 129076 (2023).
E. A., F. Alemanno, Aquaro, A. Barra, C. Marullo, From Pavlov Condition to Hebb Learning, Neural Comput. 35, 930 (2023)
F. Alemanno, M. Aquaro, I. Kanter, A. Barra, E.A., Supervised Hebbian learning, Europhys. Lett. Perspective 141, 11001 (2023)
100. E.A., A. Fachechi, C. Marullo, Non-linear PDEs approach to statistical mechanics of dense associative memories, J. Math. Phys. (2022)
A. Fachechi, F. Alemanno, A. Barra, E.A., Outperforming RBM Feature-Extraction Capabilities by “Dreaming” Mechanism, IEEE Trans. Neur. Netw. (2022)
E.A., F. Alemanno, A. Barra, G. De Marzo, Unsupervised learning: the emergence of a concepts in associative neural networks, Neur. Netw. 148, 232 (2022)
E.A., F.E. Leonelli, C. Marullo, Storing, learning and retrieving biased patterns, Apps. Math. Comp. 415, 126716 (2021)
E.A., G. Sebastiani, Learning and retrieval operational modes for three-layer restricted Boltzmann Machines, J. Stat. Phys. 185, 10 (2021)
E.A., F. Alemanno, L. Albanese, A. Fachechi, A transport equation approach for deep neural networks with quenched random weights, J. Phys. A 54, 505004 (2021)
F.E. Leonelli, E.A., L. Albanese, A. Barra, On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model, Neur. Net. 143, 314-326 (2021)
C. Marullo, E.A., Boltzmann machines as generalised Hopfield networks: a review on recent results and outlooks, Entropy 23, 34 (2021)
E.A., A. Barra, P. Sollich, L. Zdeborová, Machine learning and statistical physics: preface, J. Phys. A 53, 500401(2020)
E.A., G. De Marzo, Tolerance versus synaptic noise in dense associative memories, Europ. Phys. J. Plus 135, 883 (2020)
E.A., A. Fachechi, C. Marullo, The relativistic Hopfield model with correlated patterns, J. Math. Phys. (2020)
E.A., L. Albanese, A. Barra, G. Ottaviani, Replica symmetry breaking in neural networks: a few steps toward rigorous results, J. Phys. A 53, 415005 (2020)
E.A., F. Alemanno, A. Barra, A.O. Barra, A. Fachechi, L. Moretti, L. Franceschi-Vento, Analysis of temporal correlation in heart rate variability through maximum entropy principle in a minimal pairwise glassy model, Sci. Rep. 10, 15353 (2020)
E.A., A. Barra, A.O. Barra, A. Fachechi, L. Moretti, L. Franceschi-Vento, Detecting heart pathologies via machine learning on clinical markers, Sci. Rep. 10, 8845 (2020)
E.A., F. Alemanno, A. Barra, A. Fachechi, Generalized Guerra’s interpolazione techniques for dense associative networks, Neur. Net. 128, 254 (2020)
E.A., P. Saez, A. Barra, M. Piel, P. Vargas, M. Castellana, A statistical inference approach to reconstruct intercellular interactions in cell migration experiments, Science Adv. 6, eaay2103 (2020)
E.A., F. Alemanno, A. Barra, M. Centonze, A. Fachechi, Neural networks with Redundant Representation: Detecting the Undetectable, Phys. Rev. Lett. 124, 028301 (2020)