Hey I'm Xav.
You can get my CV here
Here are my projects listed on my CV:

Improving MonteCarlo Tree Search (MCTS)
Used MCTS to determine which card should be played in the Swiss game of Jass.
I improved upon the default heuristics of MCTS and made a report in the process.
I wrote a Latex report about it here.
The code is available here.

An iOS app to help vegans, with recipes, restaurants around you, advice on nutrition and more.
Worked on a backend infrastructure to enable the restaurants around you feature. I used GCP, Elasticsearch and previously GeoFire.
The rest of the content that requires connectivity is tipically hosted on Firebase Storage and Firebase Realtime Database.
Worked with influencers to integrate their content in the app.
It is built using swift, and "restaurants around me" is built using Apache Solr Search Engine on a GCP Compute Engine instance.
The feature was originally built with Geofire and GCP Firestore.
The rest of the content that requires connectivity is tipically hosted on Firebase Storage and Firebase Realtime Database.
Here is a link.

With a friend, we built tools for analysing market data of cryptocurrency exchange websites Binance and Coinbase Pro.
More specifically, we searched for arbitrage oportunities. We built it using GCP. We used MySQL for storing market data,
that we fetched from the public apis of these two exchanges using a cron job and a Compute Engine instance. (fetching scripts written in javascript)
We analysed the data using python.
Learning is easier when your goal is to be the teacher. Of course, being the teacher is not easy and most of the time not attainable, but
setting this goal improves learning performances by a lot. When I try to learn an interesting subjects, I often write a Latex document explaining
what I understand from this subject. Keep in mind that these documents were elaborated by a unskilled student, and that there may be
invalid proofs/arguments made. They simply stand here as a testimony of my dedication, and it's a huge bonus if someone is helped in any way.

Deep Learning, kNN and kmeans clustering implementation, written in English,
it is a simple implementation of
three popular machine learning algorithms.
I implemented kNN and kmeans from scratch in java and used TensorFlow for the deep learning approach.

Analysis in R^{n}, written in French.

Cryptography, written in English,

Finite Fields and Error Correcting Codes, written in English,

Information Theory, written in English,
is an introduction to Information Theory,
where we are only interested in coding for noisless channels.
The formalisms in this document are rigorous.

A proof of the Spectral Theorem, written in French,
that does not rely on
the availability of a scalar product in the field, but a less demanding symetric bilinear form.
It goes on to give the proof and
numerical strategies for obtaining the associated matrix in the field of real numbers.

BolzanoWeierstrass in R^{n} , written in French,
gives a very complete and
elementary proof of this theorem for vectors of real numbers.

R^{n} space , written in French,
gives the properties of the field R^{n}. More
specifically, we are interested in the topology of the space.

Differential Equations, written in French,
again a very short introduction to
Ordinary Differential Equations,
where we give strategies for finding solutions by hand to simple cases like separated
linear differential equations or of first order.

Riemann Integrals in R, written in French,
a very short introduction to the
formalism that leads to the definition of integrals.