The AstroML Research Group

Junsong Cang is a postdoctoral researcher specializing in 21cm cosmology. His research focuses on the theoretical modeling and numerical simulation of the cosmic 21cm signal during the epoch of reionization, as well as inference techniques for analyzing 21cm data. He is currently working on the data analysis of EDGES and SKA experiments.

Junsong got his PhD in 2023 from the Institute of High Energy Physics, Chinese Academy of Sciences, working on the searches for dark matter and primordial black holes using CMB, 21cm and gravitational wave. In 2021-2022 he was a visiting student at Scuola Normale Superiore di Pisa, and he returned there as a postdoctoral researcher in 2024, collaborating with Prof. Andrei Mesinger on 21cm numerical simulation and Bayesian inference analysis of EDGES experimental data.

Andre is a postdoctoral researcher primarily focused on machine learning applications to problems in high energy/astroparticle physics with a specific focus on dark matter and beyond the standard model physics.

He completed his Ph.D. at the University of Adelaide in Australia where a majority of his work focused on dark matter phenomenology, before moving to INFN Torino for 2 years where he acquired a more generalized interest in unsupervised learning and generative models before ultimately ending up with the data science group at SISSA in November 2022.  

Chiara is a postdoctoral researcher mainly focused on cosmology, specifically on the Large Scale Structure of the Universe and nonlinear galaxy clustering. She works on theoretical modelling of cosmological observables and Bayesian inference of cosmological parameters from upcoming galaxy survey data. She’s involved in the science preparation of the Euclid mission, in modelling and testing the nonlinear galaxy power spectrum and bispectrum both in LCDM and extended cosmological models, and in the optimisation of the likelihood pipeline. She’s also a developer of the official Euclid likelihood package.

She got her PhD in 2020 from the University of Trieste, working on approximate cosmological simulations in the context of modified gravity theories. She then moved first to Queen Mary University in London and then to the University of Edinburgh, where she focused mainly on theoretical modelling of the nonlinear galaxy distribution, forecasts for Stage-IV spectroscopic surveys and data analysis of available galaxy clustering data.

Rahul analyzes challenges in astrophysics using computer simulations and machine learning. Particularly, he is interested in detecting black holes and understanding their progenitor galaxy and stellar environments. To this end, he works on developing rapid gravitational wave detection pipelines and improving simulations of binary black hole formation. 

Since December 2023, he has been a postdoctoral researcher in the SISSA Data Science group. He received his Ph.D. in 2023 from the Observatoire de la Côte d’Azur in Nice, France on the topic of understanding the galactic environment of stellar mass binary black holes. In his Master’s degree in Physics (2018), he specialized in exploring geodesics around charged, spinning black holes spacetimes.

Mauro is a second-year Ph.D. student. He got his master’s degree in Computational Physics from the University of Trento, where he graduated with a thesis on Variational Monte Carlo methods using Neural Network Quantum States.

He’s interested in Physics, Data Science, and Machine Learning, and in how each field can benefit from sharing the tools and frameworks developed within the others. He likes programming, cooking, drawing, and hiking and he plays the guitar.”

Kosio is a doctoral student on the intersection(s) of cosmology and data science. Until now he has focused mainly on inference of dark matter on galactic and sub-galactic scales and is now starting with supernova cosmology.

In his spare time, he loves taking (and editing) photographs, programming data visualisations, if the inspiration strikes, and he occasionally plays the guitar.

Mingmeng graduated from the Ingénieur Polytechnicien Program at Ecole Polytechnique in France, and he also holds a Master’s degree (M2) in Mathematical Modelling.  

He has always been attracted to interdisciplinary research involving social sciences or biology and has done several internships on machine learning, biophysics, and biomathematics. He is currently a PhD student at SISSA and his research topic is big data analytics to innovate demoscopic polling and improve understanding of public perception. 

He enjoys traveling, watching movies, and is a fan of many sports. 

Satvik is a new Ph.D. student in the Theoretical and Scientific Data Science group at SISSA. He obtained his M.Sc degree in Physics from RWTH Aachen University, focusing on Astroparticle Physics and Cosmology, and carried out his thesis on Modeling the Epoch of Reionization.

His other interests include photography, cooking, and traveling.

Uros is an intern at SISSA and a master’s student enrolled in the Scientific and Data-Intensive Computing program at the University of Trieste. He obtained his bachelor’s degree in artificial intelligence at the Johannes Kepler University Linz.

His current work under the supervision of Roberto Trotta focuses on improving data processing methods for dark matter direct detection experiments using machine learning.

On the side, Uros also likes to investigate automatic note transcription for the piano utilizing computer vision.