## ABOUT ME

I was born in Xalapa, México. I got my BSc in Actuarial Science from UNAM. Later, I obtained a MSc in Statistics from CIMAT/Universidad de Guanajuato, México. I wrote my thesis under the supervision of Dr. Andrés Christen-Gracia. After getting my degree, I worked for two years as a statistical analyst at MD Anderson Cancer Center; that gave me the opportunity to obtain experience in statistical consulting while being involved in research, more specifically, in Bayesian methods for Pharmacokinetics. In 2005, I decided to pursue a doctorate. I was truly fortunate to work on my PhD under the supervision of Sayan Mukherjee and Robert L. Wolpert at Duke University. My thesis project involved applying ideas of Computational Topology and Random Geometric Graphs to graphical modelling. After getting my PhD, I worked as a postdoctoral fellow at Harvard University with Edoardo M. Airoldi, where I started working on social network data. From 2015 to 2017, I held the position of research associate at University College London, where I was mentored by Patrick Wolfe and Sofia Olhede. I held an appointment as a lecturer at Lancaster University from 2017 to 2021. I started at my current post on January 2022.

## RESEARCH INTERESTS

## EDUCATION

2009

Duke University

PhD in Statistics

2002

Universidad de Guanajuato/ CIMAT

MSc in Statistics

Social Networks

I am interested in tackling statistical problems involving social networks from a Bayesian perspective, more specifically: with Edoardo Airoldi, I have worked on the problems of sampling from a social network and finding optimal designs given a specific inference. With Patrick Wolfe and Sofia Olhede, I have been developing multivariate models (static and dynamic) for networks.

2001

Universidad Nacional Autónoma de México (UNAM)

BSc in Actuarial Science

Graphical Models

In one of my previous projects, my co-authors and I proposed new parameterizations and representations of graph space in order to specify interesting priors and Markov Chain Monte Carlo algorithms for graphical models.

Applications of Computational Geometry and Computational Topology to Statistics

I am interested in finding connections between the tools developed in these two fields and Statistics. We have successfully applied tools from Computational Topology to Graphical Models. Constructions proposed in both of these fields are proving useful in my current work on multivariate models for social networks. I am also interested in developing Bayesian models aimed to quantify uncertainty in problems related to topological data analysis.

Shape Theory

It has been quite rewarding to study this topic: at least two of my current projects involve applying ideas that originated in this field to modelling network data.