In this post…
I graduated from the University of La Laguna in Physics and continued to carry out my PhD in the Instituto de Astrofisica de Canarias, both in Tenerife (Spain). During my PhD I focused on the study optical and near-infrared properties of extragalactic objects observed by the Infrared Space Observatory (ISO) making use of the facilities at the Observatory of La Palma. After my PhD I continued my involvement in the infrared space missions Spitzer and Herschel. After a short postdoc in the University of Sussex (Brighton, UK) I joined the Institute of Astronomy (Cambridge, UK) and the Cambridge Astronomy Survey Unit (CASU).
At CASU I became more familiar with data processing, data analysis and data management of surveys carried out with UKIRT/UKIDSS, VISTA and VST. I have been also responsible for developing and operating the VISTA and VST data archives at the CASU data centre. Over the years I have also participated in several other projects, mainly developing software: Virtual Observatory (AstroGrid), Cambridge Planck Analysis Centre and more recently Euclid, WEAVE and 4MOST.
In 2017 I did a career change to join Radar CyberSecurity as a Senior Python Developer.
In 2018 I rejoined the Institute of Astronomy to work in medical imaging analysis as part of the IMAXT project.
Research Associate, 2018 -
Institute of Astronomy, University of Cambridge
Senior Python Developer, 2017 - 2018
Radar CyberSecurity, Vienna
Research Associate, 2004 - 2017
Institute of Astronomy, University of Cambridge
Marie Curie Fellow, 2002 - 2004
Institute of Astronomy, University of Cambridge
PostDoc in Astrophysics, 2001 - 2002
University of Sussex
PhD in Astrophysics, 2001
Instituto de Astrofisica de Canarias
BSc in Physics, 1996
University of La Laguna
Full list of publications in Google Scholar
I describe a few data structures that are useful in distributed systems.
Registration of two images using scikit image.
In this example I am going to explain how to detect a type of anomaly in a time series. The time series is composed by a slow varying background signal with gaussian noise on top of which we simulate a anomaly defined as a set of continuous values above the average.
A custom Jupyter kernel allows for user customization of packages and settings loaded at startup so that we do not have to start all notebooks with the same setup code.