# Hello

I am a math PhD student at the University of Geneva, doing research in tensor networks, numerical linear algebra and machine learning. I like to do data science as a hobby, and I will use this website as a blog and post some of my projects.

Feel free to contact me if you’re interested in any of the things I do!

# My blog

## GMRES: or how to do fast linear algebra

Linear least-squares system pop up everywhere, and there are many fast way to solve them. We’ll be looking at one such way: GMRES.

## Machine learning with discretized functions and tensors

We recently made a paper about supervised machine learning using tensors, here’s the gist of how this works.

## Low-rank matrices: using structure to recover missing data

A lot of data is naturally of ‘low rank’. I will explain what this means, and how to exploit this fact.

## How to edit Microsoft Word documents in Python

Parsing and editing Word documents automatically can be extremely useful, but doing it in Python is not that straightforward.

## Blind deconvolution #4: Blind deconvolution

Finally, let’s look at how we can automatically sharpen images, without knowing how they were blurred in the first place.

## Blind Deconvolution #3: More about non-blind deconvolution

Deconvolving and sharpening images is actually pretty tricky. Let’s have a look at some more advanced methods for deconvolution.

## Blind Deconvolution #2: Image Priors

In order to automatically sharpen images, we need to first understand how a computer can judge how ‘natural’ an image looks.

## Blind Deconvolution #1: Non-blind Deconvolution

Deconvolution is one of the cornerstones of image processing. Let’s take a look at how it works.

## Time series analysis of my email traffic

I have 15 years worth of email traffic data, let’s take a closer look and discover some fascinating patterns.

## 2020 in music

2020 was a great year for music, I will look back and give some thoughts on the best albums that came out in 20202.

## Modeling uncertainty in exam scores

We use exams to determine how much a student knows, but exams aren’t perfect. How can we estimate the uncertainty in students’ exams scores?

## How big should my validation set be?

Cross validation is extremely important, but how should we choose the size of our validation and test sets?

## How do my music preferences evolve?

I use last.fm to track my music listening. Let’s look at my data to discover how my musical preferences evolve over time.

## Is my data normal?

Normally distributed data is great, but how do you know whether your data is normally distributed?