Andrei Albert MESINGER
Andrei Mesinger is a Full Professor of Astrophysics and Cosmology at the Dipartment of Physics and Astronomy of the University of Catania (UniCT), Italy. He received his PhD at Columbia University (USA) in 2006, and subsequently held postdoctoral fellowships at Yale University and Princeton University. In 2011 he moved to Scuola Normale Superiore in Pisa (Italy) as junior faculty, becoming an associate professor in 2020, and in 2025 he was awarded a full professorship at UniCT. His research interests include cosmic first light, reionization, the cosmic 21-cm signal, high-redshift galaxies, physical cosmology including dark matter, modeling techniques, machine learning and Bayesian inference. His research was awarded the NASA Hubble prize fellowship in 2008, the Starting Grant Award from the European Research Council (ERC) in 2015, a PRIN award (national PI) from the Italian Ministry of Universities and Research in 2023, among other recognitions. He has written over 200 peer-reviewed publications (h-index 69), and edited two books. He is deeply involved in current efforts to detect the cosmic 21-cm signal, and is an executive board member of the Hydrogen Epoch of Reionization Array collaboration as well as being on the board of the Epoch of Reionization Science Team for the Square Kilometer Array telescope.
(last updated Oct. 2025)
• First Light and Reionization: The dawn of the first astrophysical structures: What were they? In what numbers? When and how did they reionize and heat the early Universe? How did they interact with their surroundings through feedback processes (radiative, chemical, mechanical)? How can we observe this epoch? How can we learn about cosmology and astrophysics from the 21cm line?
• High-Redshift Sources: What were the properties and abundances of quasars and galaxies? Can we infer these from observations?
• The Nature of Dark Matter: What is it? How can we constrain the properties of Dark Matter using high-redshift observations?
• Data Science and Machine Learning: How do we best exploit upcoming large data sets? How do we develop efficient simulators and forward models? How can we obtain unbiased, optimal posteriors using simulation-based interference?
PEER-REVIEWED PUBLICATION SUMMARY (last updated Oct 2025)
>200 peer-reviewed publications since 2004
>13k citations; >3.4k citations normalized by author number; (source: NASA ADS Database)
h-index of 69; i10 index of 160; i100 index (>100 citations) of 43 (source: NASA ADS Database)