My name is Arthur Carcano, and this website is mostly dedicated to my passion for computer programming. I mostly enjoy programming in Rust, and have authored various libraries and programs in it.
Before being a software engineer, I worked in academic research. I was a PhD student in Mathematics and Computer Science with the InBio team at Institut Pasteur and Inria in Paris.
There, I designed algorithms that use statistical and probabilistic methods to measure how much information biologists can obtain from an experimental protocol, and use this informativeness measure to automatically design the most interesting experiments to perform. I also designed and implemented a software ecosystem around these methods. More information on this topic is available in my PhD. manuscript.
Before starting my PhD, I was a student at the computer science department of École Normale Supérieure (ENS) in Paris, France. While at the ENS, I graduated from the Interdisciplinary approaches to life master program, and from the Parisian master of research in Computer Science.
If you manage to crack my state-of-the-art anti-spam cipher, you can e-mail me at
email@example.com, in French, or English.
I'm the creator of:
- Fwd:AD, a rust library to perform forward auto-differentiation, with a focus on empowering its users to manage memory location and minimize copying.
- Captain a workflow management library.
- Agnos, a single-binary, API-less and provider-agnostic, dns-01 client for Let's Encrypt, allowing you to easily obtain certificates (including wildcards). It answers the DNS verification queries on its own, bypassing the need for API calls to your DNS provider.
- Ratiometric quorum sensing governs the trade-off between bacterial vertical and horizontal antibiotic resistance propagation, A. Banderas, A. Carcano, E. Sia, S. Li, A. B. Lindner, PLOS Biology, 2020 (plos)
- Can optimal experimental design serve as a tool to characterize highly non-linear synthetic circuits?, M. Kryukov, A. Carcano, G. Batt, J. Ruess, European Control Conference, 2019 (hal)
- Probably approximately correct learning of regulatory networks from time-series data, A. Carcano, F. Fages, S. Soliman, Computational Methods in Systems Biology, 2017 (hal)