Professore Associato 
Settore scientifico disciplinare di riferimento  (FIS/03)
Ateneo Università degli Studi di ROMA "La Sapienza" 
Struttura di afferenza Dipartimento di FISICA 
Recapiti Elenco recapiti telefonici

Orari di ricevimento

Vedere pagine web dei relativi corsi


Part I – General Information
Full Name Andrea Crisanti
Citizenship Italian
Spoken Languages Italian, English, French
Part II – Education
Type Year Institution Notes (Degree, Experience,…)
University graduation 1985 Università La Sapienza, Roma Laurea in Fisica cum laude
Post-graduate studies 1985/88 Racah Institute of Physics, The Hebrew University, Israel
PhD 1988 The Hebrew University, Israel PhD in Physics
Part III – Appointments
IIIA – Academic Appointments
11/1988 04/1989 Institut de Physique Théorique, EPFL, Lausanne, Scholarship
05/1989 02/1990 Institut de Physique Théorique, EPFL, Lausanne, Research Assistant
03/1990 03/1991 Institut de Physique Théorique, Université de Lausanne, Lausanne, Maitre Assistant
04/1991 09/1991 Dipartimento di Fisica, Università dell'Aquila, Postdoc
10/1991 10/2000 Dipartimento di Fisica, Universita la Sapienza, Ricercatore
11/2000 present Dipartimento di Fisica, Universita la Sapienza, Professore Associato
IIIB – Other Appointments
03/1994 03/1994 EPFL Hôtes Académiques
2003 2011 IPTh, CEA, Saclay Long term periods (one week / three months) as Hôtes Académiques
01/2012 05/2012 CNRS, LPTHE, Universite Pierre et Marie Curie, Paris Hôtes Académiques
2013 2015 CNRS, LPTHE, Universite Pierre et Marie Curie, Paris Long term periods (one week/one month) as Hôtes Académiques
Part IV – Teaching experience
1989/90 EPFL, Lausanne, Switzerland Troisième cycle de la physique en Suisse Romande
1990/91 Université de Lausanne, Switzerland Mécanique
1991/92-1999/00 Università La Sapienza Metodi Matematici della Fisica (esercitazioni)
1993/94 Università La Sapienza Metodi Asintotici e Sviluppi Perturbativi (dottorato)
1994/95-1995/96 Università La Sapienza Fisica Sperimentale I (Laurea in Chimica, esercitazioni)
1995/96 Università de l'Aquila Fisica Teorica
1996/97 Università de l'Aquila Metodi Matematici per la Fisica
1996/97 Università La Sapienza Fisica (Laurea in Scienze Biologiche, esercitazioni)
1995/96 Università La Sapienza Meccanica Statistica (dottorato)
1998/99-2003/04 Università La Sapienza Metodi computazionali ed analitici di base della fisica
2000/01 Università La Sapienza Fisica Generale (laurea in Chimica)
2001/02-2004/05 Università La Sapienza Laboratorio di Calcolo
2002/03-2007/08 Università La Sapienza Laboratorio di Fisica Computazionale I
2008/09-2010/11 Università La Sapienza Laboratorio di Fisica Computazionale II
2003/04 Università La Sapienza Metodi di approssimazione per la Fisica
2004/05-2012/13 Università La Sapienza Metodi Numerici per la Fisica
2013/14-2005/16 Università La Sapienza Metodi Computazionali per la Fisica
2012/13-2014/15 Università La Sapienza Fisica Generale II (laurea in Matematica)
2015/16 Università La Sapienza Meccanica Statistica
Part V - Administrative experience
Dipartimento di Fisica, Università la Sapienza, Department Responsible for: Courses on computational physics (2001/2002)
Dipartimento di Fisica, Università la Sapienza, Member of: Commissione Attività informativa e divulgazione scientica
Dipartimento di Fisica, Università la Sapienza, Member of: Giunta di Dipartimento (2 mandati)
Dipartimento di Fisica, Università la Sapienza Member of: Commissione per lo sviluppo dei servizi informatici della biblioteca ed integrazione
con il polo SBN
Dipartimento di Fisica, Università la Sapienza, Department Responsible for: Segreteria Didattica del Dipartimento di Fisica
Dipartimento di Fisica, Università la Sapienza, Department Responsible for: Laboratorio supporto e sviluppo informatico
Dipartimento di Fisica, Università la Sapienza, Member of: Commissione di manutenzione
Dipartimento di Fisica, Università la Sapienza, Department Responsible for: Servizi informatici e di rete.
Part VI – Research Activities
Topics Brief Description
*) Neural Networks Models
Study of the role of disorder and asymmetry in neural networks models.
The synaptic interactions between pairs of neurons in real brain are not
symmetric. Removing the constraint of symmetric interactions in neural
networks may lead to more efficient and robust systems. For example the
introduction of small asymmetry destroys the spin glass phase, which
correspond in terms of neural networks corresponds to a state of confusion.
*) High Dimensional Disordered Spin Systems
The properties of high dimensional (mean field) disordered spin systems are
investigated using both equilibrium statistical mechanical methods and the
relaxation dynamical approach based on Langevin equations. In the static,
equilibrium statistical mechanics, approach the properties of disordered spin
systems are typically studied using the so-called replica method to perform
the average over disorder. This, however, approach requires the nontrivial
limit of vanishing number of replicas. The dynamical approach makes use
of the functional methods based on the Martin-Siggia-Rose functional and
does not require the introduction of replicas. However, the complex nature
of the phase space of disordered systems leads to a nontrivial interplay
between the long time and the thermodynamic limits. The connection
between the static and dynamic descriptions is not trivial, even in the mean
field limit.
*) Random Matrices, Low Dimensional Disordered Systems, Chaotic Dynamical Systems.
Properties of products of random matrices and their use for the analysis of
chaotic dynamical and low dimensional disordered spin systems. The
evolution of a dynamical system can be described by a map in the phase
space (Poincare' map). The properties of the dynamics can thus be obtained
from that of products of (suitable) matrices. The evaluation of the partition
function of 1D or 2D spin systems can be translated to that of the maximum
Lyapunov exponent of product of (random) transfer matrices. The
connection with products of random matrices allows for the use of efficient
analytical and numerical methods in the study these systems. Chaos in
dynamical systems refers to the sensible dependence on initial condition:
trajectories starting from arbitrarily close initial condition differ of a finite
quantity in a finite time. The presence of chaos may affect the behavior of
systems in sensible way. For example in predicting their future state, or
when the system interacts with some external forcing. Chaos is not limited
to classical systems, however its definition in quantum systems requires
some care.
*) Equilibrium and Nonequilibrium Thermodynamics, Fluctuation relations
Equilibrium and nonequilibrium statistical mechanics description of slow
relaxing glassy systems. Glassy systems are characterized by very long,
well separated, characteristic relaxation time scales, leading to history
dependent behaviors, commonly referred to as aging. The presence of these
time scales reflects the complex structure of the free-energy landscape
where the dynamical evolution takes place. The properties of a glassy
system can then be obtained from the study of the topology of the free
energy landscape. In a dynamical approach the properties of a glassy
behavior can be characterized by the (non equilibrium) fluctuations of
macroscopic quantities, such as work or heat, (fluctuation relations) or by
the response of the system to external perturbations (generalized
fluctuation/dissipation relations).
*) Turbulence and Transport in Fluids
Turbulence and transport in turbulent fluids. Turbulent fluids have chaotic
behaviors whose spatial and temporal properties can be described
statistically, either using models or direct numerical integration of the
partial differential equations describing the evolution of the fluids (Navier-
Stokes equations). The turbulent regime of a fluid has direct consequence
on how other quantities (either passive or active) can be transported by the
fluid. The study of transport properties of fluids can be formulated in terms
of predictability.
*) Spherical p-spin Models for Glassy Behavior
The dynamical behavior of disordered spherical p-spin models is described
in the mean field limit by Mode-Coupling equations of the same form as
those describing glassy systems. Moreover they static and dynamical
properties can be obtained analytically. They thus represent simple
statistical mechanical models for the analysis of glassy behaviors.
*) Statistical Mechanical approach to Random Lasers
The statistical properties of laser light in homogeneous cavities can be
analyzed by mapping the laser dynamics to an ordered Hamiltonian system,
where the nonlinearities describes mode interactions. In Random Laser the
cavity is absent and light confinement is produced multiple scattering due to
the disorder of the medium. In this case the mapping of the random laser
dynamics is with a disordered Hamiltonian system described by coupled
random spherical p-spin models.
*) Monte Carlo, Real Space Renormalization Group, Disordered Systems
The critical behavior of spin systems can be studied using direct numerical
Monte Carlo methods. Renormalization group based on the Migdal–
Kadanoff bond removal approach is often considered a simple and valuable
tool to understand the critical behavior of complicated statistical mechanical
models avoiding direct numerical simulations. In presence of quenched
disorder, however, predictions obtained with the Migdal–Kadanoff bond
removal approach quite often fail to quantitatively and qualitatively
reproduce critical properties obtained in the mean-field approximation or by
numerical simulations in finite dimensions. To overcome these limitations
the renormalization procedure must be generalized to more structured
hierarchical lattice.
*) Gene expression noise
Infectious and mendelian diseases originate from a perturbation, which is
traceable in most cases. For each disease initiating events should be
identifiable. A similar approach, however, fails in multifactorial diseases
such as Multiple Sclerosis (MS). Decades of studies of heritable and
nonheritable causes of MS have unequivocally identified elements
associated with the disease. Nonetheless, these associations are neither
sufficient nor necessary for the development and prediction of MS. The key
heritable and nonheritable factors show up in peculiar way, with subtle
variations. The major nondeterministic component of phenotypic variations
is stochastic gene expression (gene expression noise) which, along with
time, may lead to the development of MS. Characterization and study of the
properties of gene expression noise is central for the prediction of the
occurrence of MS.
*) Stochastic Resonance
Stochastic resonance in biological systems. Noise is intrinsic to biological
systems, which have "learned" how to take extract useful work or, generally
speaking, information from it. Experimental studies have shown that noise
can enhance tactile and proprioceptive stimuli by a stochastic resonance
mechanism. These studies, however, focused on large-scale properties and
the underlining mechanisms are not yet well characterized. The study and
modeling of the effect of noise on sensor neurons in terms of stochastic
resonance may shed new light on the role of noise in biological systems.
*) Growth Kinetic Models
The passage from reversible equations of motion for a many-particle system
to a mesoscopic description, e.g., via a coarse-graining process, leads to
irreversible kinetic equations. These equations describe the (slow)
relaxation of the system towards the equilibrium state. The properties can
be studied either by direct numerical solution of the kinetic equation or
employing functional methods.
Part VII – Publication Summary
Papers [International] 124
Books [scientific] 1 Products of random Matrices in Statistical Physics, Springer Verlag (1993)
Books [teaching] 2 yaC-Primer: Yet Another - C Primer: Linguaggio (Parte I) e Applicazioni (Parte II), Aracne Editrice (1996)