Riccardo Zecchina

Professor, Theoretical Physics
Department of Applied Science and Technology
Institute of Condensed Matter Physics and Complex Systems
Politecnico di Torino
Corso Duca degli Abruzzi 24, 10129 Torino (Italy)
tel: +39 011 090 7323, fax: +39 011 0907399

Head of Unit at the Human Genetics Foundation
Statistical Inference and Computational Biology.

Current research interests:

Statistical physics, computational neuroscience and computational biology, inverse dynamical problems, distributed algorithms for optimisation, constraint satisfaction problems and statistical inference, information theory, graphical games, …

An almost complete list of preprints from the ArXiv. Links to papers and codes on the sidebar.

2015 papers and some preprints

  • Robust measures, algorithmic accessibility and learning, C. Baldassi, C. Borgs, J. Chayes, A. Ingrosso, C. Lucibello, L. Saglietti, R. Zecchina, in preparation
  • On the role of subdominant domains in multilevel neurons, C. Baldassi, F. Gerace, A. Ingrosso, C. Lucibello, L. Saglietti, R. Zecchina, in preparation
  • Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses, C. Baldassi, A. Ingrosso, C. Lucibello, L. Saglietti, R. Zecchina, Phys. Rev. Lett. 115, 128101 (2015)
  • A three-threshold learning rule approaches the maximal capacity of recurrent neural networks, A. Alemi, C. Baldassi, N. Brunel, R. Zecchina, PLoS Comput Biol 11, e1004439 (2015)
  • Quantitative study of crossregulation, noise and synchronization between microRNA targets in single cells, C. Bosia, F. Sgrò, L. Conti, C. Baldassi, F. Cavallo, F. Di Cunto, E. Turco, A. Pagnani, R. Zecchina, under review
  • Local entropy as a measure for sampling solutions in constrained satisfaction problems, C. Baldassi, A. Ingrosso, C. Lucibello, L. Saglietti, R. Zecchina, in press JSTAT
  • Statistical physics and network optimization problems, book chapter in Mathematical Foundation of Complex Networked Information, Lectures notes in mathematics, Springer, C. Baldassi, A. Braunstein, A. Ramezanpour and R. Zecchina, 27 (2015)

Multidisciplinary projects in our labs

Neural Engineering and Computation (NEC) lab
  • Statistical physics approach to learning and deep learning
  • Complexity of learning in neural circuits with constrained “material” synapses
  • Input supervised learning in neural systems
  • Modelling in Neuromorphic engineering: message-passing in situ hardware learning for large scale data analysis

Statistical Inference and Computational Biology Unit, Human Genetics Foundation (HuGeF)
  • Modelling post-transcriptional regulation: theory and experiments on miRNA/mRNAs interaction
  • Protein contacts inference from evolutionary sequence data
  • Inverse problems arising in Single Cell Genomics