Seminar at the Department of Mathematics

Sara Wade

Sara Wade
(University of Edinburgh)
Personal website
Bio Sara Wade is a Reader (Associate Prof.) in Statistics and Data Science at the School of Mathematics, University of Edinburgh. Before this, she was a Harrison Early Career Assistant Professor at the Department of Statistics, University of Warwick (2015-2018). She was a post-doc in Machine Learning at the Computational and Biological Learning Laboratory, University of Cambridge working with Prof. Zoubin Ghahramani (2012-2015). In January 2013, she earned her PhD in Statistics from Bocconi University, under the supervision of Prof. Sonia Petrone and Prof. Stephen Walker. Her research interests include statistics, machine learning, and Bayesian analysis, with a focus on flexible methodology and efficient inference for complex data.

Abstract

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. In this talk, I will provide an overview of Bayesian cluster analysis, and demonstrate its advantages in an application to cluster neurons and discover unique neuronal projection patterns, in order to understand the nature of the projections that the entorhinal cortex makes to other neocortex regions. Data is collected through multiplexed analysis of projections by sequencing (MAPseq), which provides high-throughput mapping of projections at the single-neuron resolution. A Bayesian clustering approach is developed that can integrate multiple MAPseq datasets collected across mice and accurately reflect the overdispersed count nature of the data. Lastly, I will describe general tools that we are developing to describe and visualize the posterior over the clustering structure in the Bayesian approach.

Schedule

  • Friday 16 February 2024, 11.00-12.00, room A205 @ Povo 1

Details