Course 1: Dibyendu Das - Stochastic processes related to gene expression and cell division

Lecture-1: Number fluctuations & first passage in cell populations. 
Cell age-distribution in a growing population. 
Lecture-2: Stochastic gene expression: Fluctuations and threshold crossing (first passage) of gene products 

Lecture-3: Distribution of mRNA threshold crossing times in Laplace space.  
Lecture-4: Distribution of protein threshold crossing times in Laplace space;
application to experiments on cell lysis & pore formation in endosomes.
Lecture-5: Heterogeneity in cell division times (first passage times) —>impact on distribution of protein count  in cellular ensembles.

Relevant reading material: https://drive.google.com/drive/folders/1p2v_l-Fb1NrMFhDLyQseQo-IVSPWEv7c?usp=drive_link

 

Course 2: Christian Maes  - Linear response theory

Introductory:   What is nonequilibrium? 

Relevant papers

1) Aaron Beyen, Faezeh Khodabandehlou and Christian Maes, Quasistatic response for nonequilibrium processes: evaluating the Berry potential and curvature. Journal of Physics A: Mathematical and Theoretical 59, 205001 (2026).

2) Lander Bogers, Faezeh Khodabandehlou and Christian Maes, Negative specific heats: where Clausius and Boltzmann entropies separate. Physical Chemistry Chemical Physics 27, 15009-15023 (2025).

3) Christian Maes, Response theory: a trajectory-based approach. Frontiers in Physics, section Interdisciplinary Physics (2020).

4) Christian Maes and Maarten H. van Wieren: Time-symmetric fluctuations in nonequilibrium systems , Physical Review Letters 96, 240601 (2006).

 

Course 3: Kabir Ramola - Statistical physics of athermal systems

TBA

 

Course 4: Valentina Ros  -  Dynamics of disordered systems

Out-of-equilibrium in high-dimensional systems with disorder

Course outline: In these lectures, I will discuss the dynamics of high-dimensional complex systems, namely systems composed of many interacting degrees of freedom coupled through random all-to-all interactions that may be non-reciprocal. I will focus on models whose dynamics is analytically tractable, at least to some extent, and I will discuss the types of out-of-equilibrium behavior they generate (such as aging and chaos) and how these phenomena are affected by high dimensionality and non-reciprocity.

Lecture 1: Introduction: setting, questions, and roadmap. Simple models with non-reciprocal interactions: linear random forces and non-linear random forces.
Lecture 2: Warm-up: finite-N systems with linear random forces.  Concepts: relaxation dynamics, landscapes, equilibrium and non-equilibrium behavior.  Tools: mode decomposition and ordinary differential equations.
Lecture 3: Large-N systems with linear forces: averaging over initial conditions; spectral properties of large asymmetric random matrices and their impact on the dynamics.  
Concepts: concentration, self-averaging, and universality. Tools: random matrix theory.
Lecture 4: Large-N systems with linear forces: asymptotically long-time dynamics; slow relaxation; effect of non-reciprocity. Concepts: separation of time scales, aging, slow (algebraic) decay, and acceleration induced by non-reciprocity.  Tools:large-N asymptotic analysis.
Lecture 5: Large-N systems with non-linear forces: dynamical mean-field theory (DMFT) and chaos in high dimensions.  Concepts: mean-field dynamics, self-consistent stochastic processes, fixed points versus chaotic dynamics. Tools: DMFT equations and their asymptotic analysis, Hamiltonian formulation, Kac–Rice formalism (just a few words if time permits).

Lecture notes: https://sdrive.cnrs.fr/s/kpCtkQBrwtzdJ6K

 

Course 5: Sudeshna Sinha - An Introduction to Chaos

TBA

 

Course 6:Hugo Touchette -  Introduction to Reinforcement Learning

Preparation lecture 0:  Markov processes (On YouTube)

Lecture 1: Markov reward processes
Lecture 2: Markov decision processes
Lecture 3: Optimal equations and Bellman equations
Lecture 4: Dynamic programming
Lecture 5: Temporal difference methods

Relevant Videos and Reading material: https://www.youtube.com/watch?v=I3V-NULa1no

For material to read: R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 2nd ed. edition, 2018.
Available for free at http://incompleteideas.net/book/the-book-2nd.html

Material for Tutorials: pdf1, pdf2, pdf3, pdf4