Valentin Manes

Violet is a blockchain startup intending to create "Compliance & Identity Infrastructure for Decentralized Finance".

In practice, this translated in creating an KYC-backed OICD backend infrastructure and several front-end website as POCs.

Qonto is a European neobank for professionals. To improve a higher quality of service for its clients, in 2018 it developed its own “Core Banking System”, meaning that it maintains itself all its clients’ accounts and process all their transactions. (Beforehand it was relying on an external partner to do so.) At Qonto, I was part of the Ledger team, which maintains the “source of truth” for all accounts and their transactions. These micro-services were implemented using a Go plus PostgreSQL stack. Moreover, I did multiple interventions to improve and maintain Qonto’s billing system which is implemented using Ruby on Rails.

Qonto uses an advanced micro-service architecture with over 80 services (and counting!) being continuously deployed relying on Gitlab, Kubernetes and Argo CD.

The CSRC is a state-funded research center at KAIST University. I was free to define the problems I worked on, find their potential solutions, then develop and design their implementations, and finally test and evaluate these prototypes. This experience allowed me to demonstrate my ability to abstract.

I also fully utilized my engineering mindset: I carried out three major projects. First, a modification of the Linux kernel memory allocation code for drivers (in C). Second, an improvement to LLVM's dynamic testing tool, a compiler infrastructure project written in C++. This project was merged into the main code by a Google team. And finally, Ankou, my largest project, is a fuzzer that I developed from scratch in Go. Ankou found over a thousand unique bugs in open-source projects.

- At CSRC, collaboration used Slack and Gitlab. More specifically, seven of us participated in writing the survey on fuzzers. All contributions were made via Pull Requests.

- Configuration of experiments in Docker containers to be reproducible and scalable on multiple servers. Command-line tools are invaluable: htop, grep, find, etc…

- Ankou (described below), I started as a study of using Machine Learning techniques to improve the bug-finding capability of fuzzers. For this, standard Python libraries were used: Keras, TensorFlow, Numpy, Pandas. Both parts of the project, Go and Python, communicated via RabbitMQ.