Talks

coffee, tea, cookies at 16:15 in the main hall
Monday 16:30-17:30
Seminar room 1+2
- monthly seminars -
Seminar room 1+2
- weekly seminars -
Monday 11:00-12:00
Room 1D1
Wednesday 16:30 - 17:30
Seminar room 1D1
Thursday 14:00-15:00
Seminar room 4

 

 

 

Talks in chronological order

27 Apr 2018
10:00 AM

Economic fitness and complexity

Luciano Pietronero (La Sapienza, Roma, Italy)

Seminarroom 4 iCal Event
07 May 2018
04:30 PM

Colloquium: Stochastic thermodynamics: From principles to the cost of precision

Prof. Dr. Udo Seifert (Universität Stuttgart)

For the macroscopic world, classical thermodynamics formulates the laws governing the transformation of various forms of energy into each other. Stochastic thermodynamics extends these concepts to micro- and nano-systems embedded or coupled to a heat bath where fluctuations play a dominant role. Examples are colloidal particles in time-dependent laser traps, single biomolecules manipulated by optical tweezers or AFM tips, and transport through quantum dots. For these systems, exact non-equilibrium relations like the Jarzynski relation, fluctuation theorems and, most recently, a thermodynamic uncertainty relation have been discovered. First, I will introduce the main principles and show a few representative experimental applications. In the second part, I will discuss the universal trade-off between the thermodynamic cost and the precision of any biomolecular, or, more generally, of any stationary non-equilibrium process. By applying this thermodynamic uncertainty relation to molecular motors, I will introduce the emerging field of "thermodynamic inference" where relations from stochastic thermodynamics are used to infer otherwise yet inaccessible properties of (bio)physical and (bio)chemical systems.

Seminarroom 1+2+3 iCal Event
09 May 2018
02:00 PM

When Turing meets Waddington: a theory of pattern formation in active biphasic multicellular tissues

Adrien Hallou (University of Cambridge, UK)

The formation of self-organized patterns of cell differentiation is key to the morphogenesis of multicellular organisms, and while a general theory of biological pattern formation is still lacking, we introduce a generalisation of Turings work on pattern formation in monophasic systems to biphasic multicellular tissues. Our model incorporate morphogen production and transport, cell differentiation and tissue mechanics in a single framework, where a first tissue phase consists in a three-dimensional viscoelastic network made of cells, and a second phase composed of extracellular fluid, both compartments being separated by cell membranes, actively regulating interfacial fluid and morphogens exchanges. Coupling reaction-diffusion, active membrane transport and tissue poroelasticity, we show that tissue spatial organisation and mechanics can control developmental pattern formation. Overcoming crucial limitations of conventional reaction-diffusion models, we demonstrate the possibility of generating robust spatial patterns of cell differentiation using biochemical signalling pathways and gene regulatory networks involved in cell fate decisions, with either a single morphogen or multiple equally diffusing molecular signals.

Room 1D1 iCal Event
14 May 2018
04:30 PM

Colloquium: tba

Francoise Brochard (Curie Institute)

Seminarroom 1+2+3 iCal Event
06 Jun 2018
02:00 PM

Careers Talk

Christopher Gaul (Cognitec)

Seminarroom 4 iCal Event
11 Jun 2018
04:30 PM

Colloquium:

Michael Ghil (UCLA and ENS Paris)

Seminarroom 1+2+3 iCal Event
18 Jun 2018
04:30 PM

Colloquium: Tensor Network Machine Learning Models

Dr. Edwin Miles Stoudenmire (Flatiron Institute, Center for Computational Quantum Physics, New York, USA)

Tensor networks are an efficient representation of interesting many-body wavefunctions and underpin powerful algorithms for strongly correlated systems. But tensor networks could be applied much more broadly than just for representing wavefunctions. Large tensors similar to wavefunctions appear naturally in certain families of models studied extensively in machine learning. Decomposing the model parameters as a tensor network leads to interesting algorithms for training models on real-world data which scale better than existing approaches. In addition to training models directly for recognizing labeled data, tensor network real-space renormalization approaches can be used to extract statistically significant "features" for subsequent learning tasks. I will also highlight other benefits of the tensor network approach such as the flexibility to blend different approaches and to interpret trained models.

Seminarroom 1+2+3 iCal Event
25 Jun 2018
04:30 PM

Colloquium tba.

Seminarroom 1+2+3 iCal Event
02 Jul 2018
04:30 PM

Colloquium: tba

Paul J. Steinhardt (Princeton University)

Seminarroom 1+2+3 iCal Event
23 Jul 2018
04:30 PM

Colloquium tba.

Seminarroom 1+2+3 iCal Event
30 Jul 2018
04:30 PM

Colloquium: tba

Mike Cates (University of Cambridge, UK)

Seminarroom 1+2+3 iCal Event