I have a wide range of interests that spans Astrophysics, Scientific Computing, Applied Mathematics and Machine Learning. I enjoy understanding the engineering behind putting large scale Machine Learning pipelines into production, and learning of the nuances and challenges one faces when certain algorithms are implemented in the wild. Related to this, I have a strong interest into Software Engineering best practices and how this can be extended or modified to suit the world of Research Software Engineering and Data Science pipelines.
I am a 3rd year PhD student in the Centre for Data Intensive Science at UCL. My research focussing on development of online algorithms for classification of Astrophysical transients. Much of this work is done collaboratively within the Dark Energy Science Collaboration (DESC) which is a subset of a very large community working on research for the next generation telescope, the Large Synoptic Survey Telescope (LSST); currently under construction in Chile.
When the LSST comes online, it will be produce a deluge of data and collect a stream of millions of alerts per night that will need to be processed and classified in near real time. This presents many Big Data challenges that are also being faced by many other disciplines as well as in industry. As such, inspiration for solutions come form a variety of areas and domains.
Since this is a field that is inherently interdisciplinary, effective collaboration is essential for allowing the development, as well as the deployment into production, to be successful. As such, the teams of people involved, need to ensure best practises are adhered to in order for an already large shared codebase to scale with the growing number of researcher and packages that are used when developing an end-to-end analysis pipeline.
To facilitate this process, strong foundations and skills in version control, test-driven development and workflows are extremely important and it is my aim for the fellowship to help strengthen the skills for researchers.
I would also like to use this Fellowship as an opportunity and platform to advocate for Open Source, Open Science and Reproducibility in Research.
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