Mauro Camara Escudero

Computational Statistics and Data Science Ph.D.

University of Bristol

About Me

Passionate about leveraging data to drive positive change, particularly in tech-for-good sectors such as energy, environmental sciences, and healthcare. Specilized in scalable sampling, variational methods, and generative modelling. Pioneered the first algorithm for efficient Approximate Manifold Sampling and contributed to the development of Markov Snippets, with a focus on Simulator-Based Inference (SBI). In the past, I have organised:


  • Machine Learning
  • Approximate Manifold Sampling
  • Generative Models
  • Distributed Variational Inference
  • MCMC and SMC
  • Genomics & Population Genetics


  • PhD in Computational Statistics and Data Science, 2020-2023

    University of Bristol

  • MRes in Computational Statistics and Data Science, 2020

    University of Bristol

  • BSc in Mathematics with Year in Employment, 2019

    University of Southampton



Research Scientist Intern


Sep 2021 – Dec 2021 London
Designed a high-speed, high-accuracy low-rank treatment effect model in Julia, outperforming the company’s current approach by over an order of magnitude.

Data Scientist and Modeller


Jun 2017 – Aug 2018 Nottingham
  • Led a high-value project with technical and client development components.
  • Developed gas turbine blades damage detection software in OpenCV.
  • Modelled and implemented back-end software in SQLServer and Cassandra.
  • Developed wind power forecasting models with Keras and Sklearn.
  • Designed gradient-free optimization methods to enhance wind farm layout.
  • Deployed bespoke Tableau dashboards to aid gas traders.

Summer Researcher

Dr. Richardson Giles, University of Southampton

May 2016 – Jul 2016 Southampton
Modelled and simulated particle hopping in solar panels with Python.



Exploring News Through Data

LDA & Sentiment Analysis on YouTube comments.

Spotify Wrapped Weekly

Real-time Data Visualization via the Spotify API.


Tutorials, courses and various other rambling.

Approximate Bayesian Computation

Normalizing Flows Course

Recent Posts

Assessing a Variational Autoencoder on MNIST using Pytorch

Learn how to visualize the latent space and generate data using a VAE in Pytorch.

Minimalist Variational Autoencoder in Pytorch with CUDA GPU

Variational Autoencoders in Pytorch with CUDA GPU

Variational Auto-Encoders and the Expectation-Maximization Algorithm

Relationship between Variational Autoencoders (VAE) and the Expectation Maximization Algorithm. Simple Explanation

Towards SMC: Using the Dirac-delta function in Sampling and Sequential Monte Carlo

We derive the Dirac-delta function, explain how to use to approximate an Empirical PDF for a sample.

Towards SMC: Sequential Importance Sampling

Sequential Importance Sampling intuition simply explained for SMC


Selected talks during my PhD.

Approximate Manifold Sampling

Sampling from distributions concentrated around a manifold.

The THUG sampler

The first bespoke sampler for filamentary distributions.


  • Woodland Road, Bristol, BS8 1TH
  • Turn right at the entrance to office GA.14