About me

I am an astronomer working in the Renoir team at the Centre de Physique des Particules de Marseille, a CNRS laboratory, trying to understand our universe and its evolution through the discovery and characterization of specific astrophysical transient events called Type Ia Supernovae.

My scientific contributions are mainly in the time domain astronomy field, and include difference image analysis and Machine Learning, as well as statistical studies with observational data from telescopes around the world, including the final data release of the Dark Energy Survey Supernova Program DES-SN5YR.

Current Position

Since 2023 I am a member of the Vera C. Rubin Observatory Alert Production team, specifically working on difference image analysis algorithms, performance characterization, and scientific validation. I work everyday developing software, using the LSST Software Stack, writing tests, squashing bugs and improving the system performance.

Research topics

My main interests include Cosmology, time-domain astronomy with transient events and periodical variability, image analysis as well as applications of Machine Learning in these research areas.

Additionally I have worked with Gravitational Wave Astronomy, mostly related with Electromagnetic/Optical Counterparts to Compact Binary Merger GW events.

I enjoy creating different data visualizations, and I have experience in Feature Engineering for optimizing Machinge Learning models performance.

Skills

I have a PhD. in Astronomy, and I am able to create and use mathematical and physical models, to describe and explain data from diverse experiments.
Additionally, I have skills in software developement using Python, R as well as SQL and Fortran.
I work everyday with software from the LSST Stack, and I am an expert in analyzing data from the Vera C. Rubin telescope, that uses the largest camera for astronomy in the world.
I posses the know-how on scientific data-analysis pipeline design using Object Oriented Programming, software design patterns, as well as diverse workflows for Extract-Transform-Load processes.
I have experience applying Machine Learning models to astronomical data, using both supervised and non-supervised learning. I have know-how to implement both feature based ML as well as Neural Networks that don’t depend on features (such Deep Convolutional Networks, Auto-Encoders, LSTM networks and diverse types of embeddings).
I am also able to optimize numeric calculus for a wide range of problems using a tool sets involving many technologies, such as Spark, Dask, Tensorflow, Keras, etc.
As an advocate of good practices of software developing I use Git version control techniques for collaborating, and I am proficient in software testing as well, using techniques like unit-testing, continuous integration, ticket-based work tracking, code reviews in pull requests, and property based testing.

Main Publications

A full list of publications is available in ADS. You can also find me in Google Scholar, ORCID and ResearchGate.

Links

Find my personal contact information at CPPM-BOS.