We offer a wide range of graduate level general probability and stochastic processes courses which together form a comprehensive basis for advanced study in probability. These are augmented, yearly, by advanced special topic courses, as listed below.

Spring 2020

• Interacting particle systems - Procaccia.
• Brownian motion and stochastic calculus - Crawford.
• The Ising Model - Rosenthal.
• random graphs and complexes - Bobrowski.

Selection of courses from previous semesters

• Probability on Graphs - Rosenthal.
• Random Graphs - Yeo.
• Random Matrices - Rosenthal.
• Random interface models - Louidor.
• Hydrodynamic limits  - Crawford.
• Universality of exit times for random walks - Wacthel. 
• Gaussian Processes and Geometry - Adler
• Stochastic partial differential equations and measure-valued branching processes - Mytnik
• The discrete Gaussian free field and related topics - Louidor
• Gaussian Processes (and some geometry) - Adler.
• Long range dependence and heavy tails - Owada.
• The theory of random permutations  and some connections to probabilistic number theory - Pinsky.
• Levy processes and local times of Markov processes - Kaspi.
• Markov Processes - Kaspi
• Spatial Stochastic Processes - Adler
• Large Scale Dynamics of Interacting Particles - Ioffe
• Directed Polymers and Random Geodesics - Ioffe
• Levy Processes - Mytnik
• Topological Methods in Electrical and Systems Engineering - Adler