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:

Interests

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

Education

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

Experience

Research Scientist Intern

Afiniti

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

Uniper

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.

Projects

Exploring News Through Data

LDA & Sentiment Analysis on YouTube comments.

Spotify Wrapped Weekly

Real-time Data Visualization via the Spotify API.

Ramblings

Tutorials, courses and various other rambling.

Mathematical Machine Learning

Graduate-Level Mathematics for Machine Learning

Approximate Bayesian Computation

Basics and Recent Literature

Normalizing Flows Course

Learn about Normalizing Flows in a hands-on course using Python and Pytorch.

Recent Posts

Sampling from a specific level set of a Gaussian

How to sample uniformly from a level set of a Gaussian

Last updated on Apr 18, 2024 2 min read

Measure Theory for ML, AI and Diffusion Models

Measure Theory for Machine Learning from Scratch

Last updated on Feb 21, 2024 46 min read

Assessing a Variational Autoencoder on MNIST using Pytorch

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

Last updated on Oct 5, 2020 7 min read vae, pytorch, mnist

Minimalist Variational Autoencoder in Pytorch with CUDA GPU

Variational Autoencoders in Pytorch with CUDA GPU

Last updated on Oct 5, 2020 11 min read vae, pytorch, gpu

Variational Auto-Encoders and the Expectation-Maximization Algorithm

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

Last updated on Jul 18, 2020 10 min read vae, variational-auto-encoders, variational-inference

Talks

Selected talks during my PhD.

Approximate Manifold Sampling

Sampling from distributions concentrated around a manifold.

Mar 15, 2023 9:00 AM — Mar 17, 2023 5:00 PM Levi, Finland

The THUG sampler

The first bespoke sampler for filamentary distributions.

Jul 17, 2022 9:00 AM — Jul 22, 2022 5:00 PM Linz, Austria