Mauro Camara Escudero

Statistical Machine Learning Ph.D.

University of Bristol

About Me

Passionate about leveraging data to drive positive change, particularly in tech-for-good sectors such as democracy, AI safety, energy, and environmental sciences. Specialized in scalable sampling, variational methods, and generative modelling. Pioneered the first algorithm for efficient Approximate Manifold Sampling and contributed to the development of Integrator Snippets, with a focus on Simulator-Based Inference (SBI)

In the past, I have organised:


  • Score-based sampling (HMC, MALA)
  • Generative Modelling (VAEs, NFs, DDGM)
  • Denoising Diffusion Models
  • Approximate Manifold Sampling
  • Scalable and Robust inference
  • Particle methods (SMC)
  • Simulator-Based inference
  • Variational Inference
  • General Machine Learning


  • 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.

Maternal Mortality

A study on the lifetime risk of maternal mortality.

Spotify Wrapped Weekly

Real-time Data Visualization via the Spotify API.


Tutorials, courses and various other rambling.

Mathematical Machine Learning

Approximate Bayesian Computation

Normalizing Flows Course

Recent Posts

Sampling from a specific level set of a Gaussian

How to sample uniformly from a level set of a Gaussian

Measure Theory for ML, AI and Diffusion Models

Measure Theory for Machine Learning from Scratch

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


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