**When: Wednesday, October 2, 2019, 2pm
**

**Where: Informatics East, room 322
**

**Speaker: Jean-Gabriel Young
**

**Efficient and fully Bayesian inference of complex networks from noisy data**

**Abstract: **Rarely do we have access to error-free measurements of networks. Instead, we typically get to observe sequences of states that are, at best, indirect observations of a system's structure. Recent research has led to a formalization of just how much, and in which ways, these measurements can inform us on the structure of real complex networks. It is now understood that domain-agnostic models are not silver bullets---one can come up with many different models of how a data set maps to a network, all leading to different inferences. The motivation for the work presented here is the realization that this leads to a tension preventing a broad adoption of these network reconstruction methods by practitioners. On the one hand, domain expertise must *necessarily *go into devising good models, as to avoid erroneous conclusions. But at the same time, designing models can be challenging because one has to: derive a complete inference procedure from scratch; implement this procedure; verify the inference; and start anew if the results are not correct---for every model.

In this presentation, I will introduce a Bayesian framework that abstracts away most of the computational work, putting flexible model design center stage. The crucial modeling task that our framework leaves to the practitioner is that of determining how individual pair-wise measurements of interaction are explained by the presence or absence of an edge between two nodes. The method is broadly applicable in that these measurements can be anything, from a straightforward number of observed interactions, to time-series, or pairs comprising of a number of attempted and successful observations.

**Biography:**

Jean-Gabriel Young is broadly interested in problems at the intersection of statistics and complex systems. His recent work focuses on new exciting inference problems in network science, including the inference of the past of dynamical networks, network reconstruction from noisy data; and the inference of high-order interactions from pairwise data. He received his Ph.D. in Physics from Université Laval (2018), and is now a James S. McDonnell postdoctoral Fellow in complexity at the University of Michigan.

** **