Portrait of Ronaldas Macas

Ronaldas Macas

Lead Data Scientist @ Nasdaq

Biography

Data scientist developing machine learning and generative AI solutions in financial technology, with expertise in time-series analysis, signal processing, and statistical modeling. Previously searched for exploding stars using the most sensitive instrument in the world.

Interests

  • Generative AI
  • Machine learning
  • Statistical modeling
  • Gravitational waves

Education

  • PhD in Astrophysics — Cardiff University (2016-2020)
  • MSci in Physics and Astronomy — University of Glasgow (2011-2016)

Work Experience

 
 
 
 
 

Lead Data Scientist

Nasdaq

May 2024 – Present Vilnius, Lithuania

Responsibilities include:

  • Developing generative AI and machine learning solutions
  • Leading high-impact projects, communicating and presenting results to the stakeholders
  • Advancing AI knowledge via seminars, town halls, hackathons, mentorships, and personalized 1-on-1 guidance
 
 
 
 
 

Postdoctoral Research Fellow

Institute of Cosmology and Gravitation

Sep 2020 – Dec 2023 Portsmouth, United Kingdom

Responsibilities included:

  • Software 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 Cardiff, United Kingdom

Responsibilities included:

  • Software 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
  • Undergraduate teaching
  • Public outreach
  • Men’s Basketball Club president

Projects

Fast glitch modeling

Modeling gravitational-wave glitches with autoencoders.

Antiglitch

Probabilistic modeling of glitches in gravitational-wave data.

Probabilistic noise estimation

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

Sky localization

The effect of noise in localising gravitational-wave events.

Non-linear noise subtraction

Broadband noise modeling with dense neural networks.

Gamma-ray bursts

Searching for gravitational-waves associated with gamma-ray bursts.

GLADE

Creating a publicly available galaxy catalog.