### 10th World Congress in Probability and Statistics

## Invited Session (live Q&A at Track 1, 11:30AM KST)

## Mathematical Population Genetics and Computational Statistics (Organizer: Paul Jenkins)

### Mapping genetic ancestors

Graham Coop (University of California at Davis)

*Arabidopsis thaliana*genomes sampled across a wide geographic extent. We detect a very high dispersal rate in the recent past, especially longitudinally, and use inferred ancestor locations to visualize many examples of recent long-distance dispersal and recent admixture events. We also use inferred ancestor locations to identify the origin and ancestry of the North American expansion, to depict alternative geographic ancestries stemming from multiple glacial refugia. Our method highlights the huge amount of largely untapped information about past dispersal events and population movements contained in genome-wide genealogies.

### Cellular point processes: quantifying cell signaling

Barbara Engelhardt (Princeton University)

### Fitting stochastic epidemic models to gene genealogies using linear noise approximation

Vladimir Minin (University of California, Irvine)

The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We illustrate our new method by applying it to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.

### Q&A for Invited Session 04

###### Session Chair

Paul Jenkins (University of Warwick)

## Deep Learning (Organizer: Johannes Schmidt-Hieber)

### Dynamics and phase transitions in deep neural networks

Yasaman Bahri (Google Research)

### Theoretical understanding of adding noises to deep generative models

Yongdai Kim (Seoul National University)

### Adversarial examples in random deep networks

Peter Bartlett (University of California at Berkeley)

Joint work with Sébastien Bubeck and Yeshwanth Cherapanamjeri

### Q&A for Invited Session 18

###### Session Chair

Johannes Schmidt-Hieber (University of Twente)