Looking back, looking forward

Yesterday’s group Xmas party (a bit early in the season, granted, as some of the team are off today to visit CCA at the Flatiron Institute) was the opportunity to thank everyone in the group for a great year together.
I am grateful not only for the science we did together with these young and talented researchers, but also for how we’ve come together as a team, and created what feels like a positive, supportive environment in which to work.

Let me introduce them one by one alongside with some examples of their recent work (from left to right):

– Rahul Srinivasan:  floZ: Evidence estimation from posterior samples with normalizing flows, arxiv: 2404.12294
– Mauro Rigo
– Mingmeng Geng: Is ChatGPT Transforming Academics’ Writing Style? arxiv: 2404.08627
– David Prelogovic: Percent-level timing of reionization: self-consistent, implicit-likelihood inference from XQR-30+ Lyα forest data arXiv:2412.00799
– Martin de los Rios: Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC, arXiv:2410.13799
– Kosio Karchev: STAR NRE: Solving supernova selection effects with set-based truncated auto-regressive neural ratio estimation,  arXiv: 2409.03837
– Yours Truly
– Serafina di Gioia
– Chiara Moretti: StratLearn-z: Improved photo-z estimation from spectroscopic data subject to selection effects, arXiv: 2409.20379
– Satvik Mishra
– Andre Scaffidi: Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline, arxiv: 2410.00755
– Junsong Cang, The EDGES measurement disfavors an excess radio background during the cosmic dawn, arXiv:2411.08134
– Uros Zivanovic
(sadly not there: Gaby Contardo: A Data-Driven Search For Mid-Infrared Excesses Among Five Million Main-Sequence FGK Stars, arXiv:2403.18941)

One of the great rewards of being an academic is to have the opportunity to work with such brilliant, kind and passionate people.

Merry Xmas, AstroML group, and here’s to all successes for 2025! 🥂 🎄