Stochastic dynamics on large networks: Prediction and inference

International Seminar & Workshop
8 - 26 October 2018
(workshop week: 15 - 19 October 2018)

This international seminar & workshop will bring together researchers from statistical physics, statistics, numerical mathematics, machine learning and their applications in order to discuss challenges arising from dynamical data. The event will focus on how recent techniques can be used to model and learn from dynamic data, providing a forum for exploring synergies between solutions to inference tasks in different communities.

Topics include

  • state and parameter estimation in stochastic differential equations
  • inference in spatio–temporal models (e.g. reaction diffusion)
  • inferring networks from
  • dynamical datapath integral approaches
  • Monte Carlo methods, particle filter based inference
  • inference and stochastic control, rare event simulations
  • approximate inference
  • agent systems and traffic models
  • stochastic reaction networks
  • population and network models
  • neural dynamics and learning


Invited speakers

Jose Bento (US)
Luca Bortolussi (IT)
Alfredo Braunstein (IT)
Luca Dall‘Asta (IT)
Tobias Galla (UK)
Ramon Grima (UK)
Carsten Hartmann (DE)
Heinz Köppl (DE)
Reimer Kühn (UK)
Ben Leimkuhler (UK)
Ron Meir (IL)
Sebastian Reich (DE)
David Saad (UK)
Reinhold Schneider (DE)
Chris Sherlock (UK)
Vladimir Spokoiny (DE)
Gasper Tkacik (AT)
Eric Vanden-Eijnden (US)

Scientific Coordinators

Manfred Opper
(Technical University Berlin, DE)

Guido Sanguinetti
(University of Edinburgh, UK)

Peter Sollich
(Georg-August-University Göttingen, DE & King's College London, UK)


Maria Voigt
(Max Planck Institute for the Physics of Complex Systems Dresden, Germany)


The call for applications is closed.


Scientific Program

The scientific program of the workshop and seminar can be found here.


How to reach us


Useful information for your way to the venue.

Contact Institute