Hi there! 👋

My name is Flavio. I currently work as a Machine Learning Scientist at Bayer Pharmaceuticals in Berlin where I am developing new methods to analyze microscopy images generated by high-content screening. Previously, I worked as a statisitical consultant at the Free University of Berlin were I worked on over 30 scientific projects and also as a business consultant at BCPro and PwC.

I am a statistician by training: I got my M.Sc. in Statistics at the Humboldt University of Berlin. I have one B.Sc. in Economics and one B.A. in Comparative Literature (major) and Computer Science (minor), both from the Free University of Berlin. Moreover, I am an organizer of the Berlin Bayesians meetup.

Reseach interests

Machine learning for science
Bayesian methods / Bayesian workflow
Causality

Resources and links

Contact

If you would like to collaborate with me, feel free to reach out in any of the social networks I have listed above.

If you are interested in my consulting services, please check this link.

Tech stack

In addition, I am familiar with Bayesian frameworks such as Stan and PyMC. I also have some experience with Julia, Rust, Go, C, and Java.

Flavio Morelli's Projects

brms

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

contact

A CLI-tool that makes it possible to add an manage contacts in your contact book.

emdi

emdi: estimating and mapping regionally disaggregated indicators

flaviomorelli

Config files for my GitHub profile.

ml-project-template

cookiecutter template to create new machine learning based projects

poverty_estimation_bayes

Code and Latex files from my master's thesis

talks

This repository contains the slides from talks I have given.

vi_gentle_introduction

Variational inference is a technique for estimating Bayesian models that provides similar precision to MCMC at a greater speed, and is one of the main areas of current research in Bayesian computation. In this introductory talk, we take a look at the theory behind the variational approach and some of the most common methods (e.g. mean field, stochastic, black box). The focus of this talk is the intuition behind variational inference, rather than the mathematical details of the methods. At the end of this talk, you will have a basic grasp of variational Bayes and its limitations.

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