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)

Elsewhere

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.