Itai Benjamini

Krzysztof
Burdzy

Tom Liggett

I categorize problems on a conceptual-technical spectrum. That is

**Type 1**We have a question in words, but we don't know how to state a precise mathematical problem which both seems capable of solution and whose solution would be interesting.**Type 2**We have a precise mathematical problem, but we do not see any plausible outline for a potential proof.**Type 3**We have a precise mathematical problem, and a plausible outline for a potential proof, but we cannot carry through the technical details.

- (0.5) Martingale, for practical purposes.
- (0.7) Congestion in networks: SOC, HOT and the percolation-fragmentation phase transition.
- (1.6) Mixing times for coagulation-fragmentation processes.
- (1.7) The constrained Ising model as an algorithm for storage in dynamic graphs.
- (1.8) Bacon and eggs: task scheduling into batches.
- (1.9) Metropolis on Cayley graphs.
- (2.1) Mixing times for the branch-rotation chain on cladograms.
- (2.2) Random Eulerian circuits.
- (2.6) Percolation and empires.
- (2.7) Greedy tour length and the greedy spatial service algorithm.
- (2.8) A Spatial Model of City Growth and Formation.
- (3.0) Wright-Fisher diffusions with negative mutation rate!
- (3.2) A conjectured compactification of some finite reversible Markov chains.
- (3.3) The low-density limit of coalescing branching random walk.

In a different realm, I really really really want someone to do this simulation. Of course, it's not easy!

Also, pages 9-12 of Lecture 3 in these Local weak convergence of random graphs and networks lectures give a "recursive distributional equation" I would like someone to solve numerically.

- How many Brownian particles escape when you control with total drift = 1?
- WLLN for first-passage percolation on finite graphs..
- Spectral gap for the interchange (exclusion) process on a finite graph.
- Independent sets in sparse random graphs
- Power laws and killed branching random walk.
- Mixing time for a Gibbs sampler on the simplex.
- Scaling window for percolation of averages in the mean-field setting.
- Performance of the Metropolis algorithm on a disordered tree.
- Coding invariant processes on infinite trees.