Hello there!

I’m Borja and this is my blog. I develop machine learning and AI algorithms for scientific and engineering applications. I currently work at Axiomatic AI, where I have touched on a wide range of applications and techniques. Perhaps the most prominent (and recent) one is our effort to develop verified scientific reasoning combining AI with formal methods like Lean 4 (see, e.g., this paper). Previously, I did my PhD at ICFO - The Institute of Photonic Sciences, where I developed machine learning algorithms to tackle problems in quantum and biophysics.

In my projects, I’m drawn towards the computational and engineering side. I love coding and I take great pleasure when things go brrr. I constantly strive to learn and improve with every project, and I evangelize good engineering practices in my teams.

I really enjoy teaching and I try to do it as much as I can. I find it a fantastic way to actually understand what I thought I already understood, as well as a great excuse to stay updated and tinker with new concepts. Throughout the years, I’ve taught at various institutions at undergrad and masters levels, and I’ve created entire machine learning courses from scratch for master programs in Barcelona.

I’ve spent more time than I would like to admit gaming and learning how to build my own computers. I also enjoy life outdoors, though! I love climbing and skiing :D

Scientific publications and books

Here’s a list of my main scientific publications and books. You can also check out my google scholar, where there are also datasets and open source libraries.

L. Raluy, et. al., B. Requena, et. al. Intrinsically disordered N-terminal and structured DNA-binding domains jointly regulate progesterone receptor transcriptional condensates. BioRxiv 2026 link
B. Requena, et. al. A minimal agent for automated theorem proving. Accepted in ICML 2026 (arXiv link)
G. Muñoz-Gil, et. al. B. Requena, et. al. Quantitative evaluation of methods to analyze motion changes in single-particle experiments. Nat. Commun. 16, 6749 (2025).
A. Dawid, J. Arnold\(^*\), B. Requena\(^*\), et. al. Machine Learning in Quantum Sciences. Cambridge Univ. Press, ISBN: 978-1-009-50493-5 (2025) (arXiv link). (* equal contribution)
O. Kiss, U. Azad, B. Requena, et. al. Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation. Quantum 9, 1682 (2025).
B. Requena, et. al. Inferring pointwise diffusion properties of single trajectories with deep learning. Biophys. J. 122(22):4360-69 (2023).
B. Requena, et. al. Certificates of quantum many-body properties assisted by machine learning. Phys. Rev. Research 5, 013097 (2023).
G. Muñoz-Gil, et. al. B. Requena, et. al. Objective comparison of methods to decode anomalous diffusion. Nat. Commun. 12, 6253 (2021).
B. Requena, et. al. Shopper intent prediction from clickstream e-commerce data with minimal browsing information. Sci. Rep. 10, 16983 (2020).