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Modelling and inference for dynamics in complex and disordered systems
11-13 June 2014 Albanova University Centre
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A NETADIS-NORDITA meeting in honor of John Hertz' 70th birthday

Organizers: Yasser Roudi, Manfred Opper and Peter Sollich

Studying the dynamics of large systems with complex dynamics, e.g. arising
from disorder in the form of competing interactions, is important for a wide
range of problems from spin glasses to machine learning, neuroscience, protein
interaction networks, metabolic networks and systemic risk in finance.

The dynamics in these systems are typically stochastic and time irreversible,
raising many technical and conceptual challenges: How do we describe the
dynamics accurately without full stochastic simulation? How can we solve
"dynamical inverse problems", i.e. infer properties of the underlying network
from observations of the dynamics? How do we extend inference approaches
for complete observations of trajectories to partial (and noisy) observations?

In this workshop we bring together experts in statistical physics approaches
suited to large networks together with experts in interdisciplinary application
areas to address these questions.

Invited Speakers
  • Sara A. Solla (Northwestern Univ)
  • David Sherrington (Oxford Univ)
  • Matteo Marsili (ICTP)
  • Giorgio Parisi (La Sapienza)
  • Bert Kappen (Radboud Univ Nijmegen)
  • Frederico Ricci-Tersenghi (La Sapienza)
  • Peter Latham (Univ College London)
  • Ramon Grima (Univ of Edinburg)
  • Guido Sanguinetti (Univ of Edinburg)
  • Ole Winther (Technical Univ of Denmark)
  • David Saad (Aston Univ)
  • Enzo Marinari (La Sapienza)
  • Nicolas Sourlas (École normale supérieure)
  • Heinz Horner (Univ of Heidelberg)
  • Reimer Kuhn (Kings College London)
  • Theo Nieuwenhuizen (Univ of Amsterdam)

Sponsored by:

NETADIS Nordita


Dates: from 11 June 2014 09:00 to 13 June 2014 18:00
Location: Albanova University Centre
Room: FA32
Chairs: ROUDI, Yasser
OPPER, Manfred
SOLLICH, Peter

AlbaNova  | Last modified 08 August 2014 18:31  |  HELP