Massive stars, binary evolution, and AI for Astro

I am a PhD student at the Max Planck Institute for Astrophysics working on advancing stellar and binary evolution with Prof. Selma de Mink. I was previously a Research Assistant in the Gravitational Astrophysics Lab at NASA Goddard Space Flight Center. I have an MSc. in Computer Science from KU Leuven and research experience in applying AI to astrophysics in numerous projects at NASA and ESA.

Research interests

My current research interests involve solving interesting inverse problems in astrophysics. Coming from a computational science background, and seeing the vast amounts of astronomical data available, there's a need for new and efficient methodologies for inferring information from this data. Traditional methods are often too inefficient, cannot handle sparse, irregularly sampled, or heterogeneous datasets, or suffer from analytic degeneracies. Finding better ways to infer information from data is key for fully exploiting the current data, and necessary for attempting to use the next decade of data (The Vera C. Rubin Observatory, by itself, will produce data the size of the Internet Archive!).

Past projects

• You're Gonna Need a Bigger Core
  Massive stars mix material just above their convective cores. We used hundreds of observed stars to figure out how much of that mixing really happens, and found that the helium cores end up much larger than most models assume. That challenges the usual extrapolations from lower-mass stars, and it matters a lot for how massive stars live and die! (PS please excuse the Jaws reference) [arxiv.org/abs/2608.02540].
• Binary stars take what they get!
  During the first year of my PhD, we showed that when stars throw mass onto a companion star that is orbiting closely, this companion accretes a lot of the transferred mass. This is in direct conflict with commonly used theoretical models, which is why it was surprising! [doi.org/10.48550/arXiv.2505.14780].
• Neutron stars in multiwavelength
  At NASA Goddard Space Flight Center, I did research on neutron star X-ray and Gamma-ray emission. In particular, we inferred the mass, radius and magnetic field geometry of neutron stars from NICER and Fermi data using neural networks emulators, speeding up simulations by 7 orders of magnitude at a minimal loss in accuracy.
• Neural networks for confined particles
  As part of an Honour's project at KU Leuven, I constructed a neural network to reconstruct spectral functions (and complex poles) of confined particles in high energy physics. This generalized upon previous approaches and achieved state of the art performance on genuine lattice data [SciPostPhys.13.4.097].
• Dark matter in the Galactic Center
  At ESA's Advanced Concepts Team, I implemented a novel approach to reconstruct Dark Matter distributions from stellar orbits in the Galactic Centre, eliminating the need to make assumptions on the physical composition of the Dark Matter distribution [doi.org/10.1051/0004-6361/202347738].
• Instance space of the Hamiltonian completion problem
  Benchmarking algorithm performance on graphs is challenging: standard instance sets are lacking, and existing generators often yield a narrow, insufficiently diverse spectrum of difficulty. We used evolutionary algorithms to generate a diverse set of instances, then analyzed which graph attributes best predict algorithm performance and filled gaps in the instance space to create graphs with previously unseen feature combinations. We applied this methodology to the Hamiltonian completion problem using the Concorde TSP Solver and a multi-start local search algorithm [doi.org/10.1016/j.cor.2022.106019].

Some past talks

Seminar talk in the IAU Working group on B stars:

Talk at the Center for Astrophysics | Harvard & Smithsonian as part of the AstroAI workshop:

PhD progress bar

Free time