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:

Interests

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

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.

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

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

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.

Last updated on May 19, 2020 3 min read sampling, dirac-delta-funtion, dirac-delta-mass, importance-sampling, sequential-monte-carlo

Towards SMC: Sequential Importance Sampling

Sequential Importance Sampling intuition simply explained for SMC

Last updated on May 19, 2020 2 min read sequential-importance-sampling, sequential-monte-carlo, sequential-data

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