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Ronaldas Macas

Data Scientist

University of Portsmouth

Biography

Analysing data from the most sensitive instrument in the world to search for exploding stars. Interested in astrophysics and multi-messenger astronomy enabled by gravitational waves. Data Scientist at the University of Portsmouth. Member of LIGO.

Interests

  • Machine learning
  • Statistical modelling
  • Code development
  • Gravitational waves

Education

  • PhD in Gravitational Physics, 2016-2020

    Cardiff University

  • MSci in Physics and Astronomy, 2011-2016

    University of Glasgow

Work Experience

 
 
 
 
 

Data Scientist

University of Porstmouth

Sep 2023 – Present Portsmouth, United Kingdom
Analysing audio and GPS data from GB Row Challenge 2023.
 
 
 
 
 

Postdoctoral Research Fellow

Institute of Cosmology and Gravitation

Sep 2020 – Aug 2023 Portsmouth, United Kingdom
Responsibilities include:

  • Code development in Python, JAX, PyMC, TensorFlow and PyTorch
  • Leading and training a group of ~30 people to investigate gravitational-wave data quality around astrophysical events
  • Closely interacting and working with various research groups within the LIGO-Virgo-KAGRA scientific collaboration to build automated gravitational-wave data quality tools
  • Student supervision
  • Public outreach
 
 
 
 
 

PhD student in Gravitational Physics

Cardiff University

Oct 2016 – Aug 2020 Portsmouth, United Kingdom
Responsibilities include:

  • Code development in Python and Matlab
  • Leading and training a group of ~10 people to search for gravitational waves associated with Gamma-ray bursts
  • Presenting the group’s work within the LIGO-Virgo-KAGRA scientific collaboration, at various seminars and international conferences
  • Student teaching
  • Public outreach
  • Men’s Basketball Club president

Projects

Fast glitch modelling

Modelling gravitational-wave glitches with autoencoders.

Antiglitch

Probabilistic modelling of glitches in gravitational-wave data.

Probabilistic noise estimation

Measuring the amount of non-Gaussian noise in the data.

Sky localisation

The effect of noise in localising gravitational-wave events.

Non-linear noise subtraction

Broadband noise modelling with dense neural networks.

Gamma-ray bursts

Searching for Gamma-ray bursts associated with gravitational waves.

GLADE

Producing publicly available galaxy catalogue.

BayesWave

Evaluating the BayesWave’s performance.

Selected Talks

Revisiting GW200129 with machine learning noise mitigation: is it (still) precessing?

Binary black hole GW200129 is claimed to be the highest precessing binary ever observed. In fact, the measured orbital precession is …

Gravitational-wave detector data quality

Gravitational-wave transient noise modelling and mitigation

Gravitational-wave transient noise poses a challenge in the upcoming LIGO-Virgo-KAGRA fourth observing (O4) run. In this talk, I will …

Do glitches in gravitational-wave data affect our ability to estimate the correct sky localization?

Gravitational-wave (GW) data contains non-Gaussian noise artifacts called ‘glitches’. These glitches can sometimes overlap …