Starting in October 2020, I am building a new cross-disciplinary research group at the International School for Advanced Studies (SISSA) in Trieste, with the aim of bringing together expertise in statistics, astrophysics, cosmology and data science. Our research focuses on machine learning, data science, cosmology and Bayesian methods. We are also active in science communication and consulting, and have an interest in ethical aspects of machine learning.
The Trotta Lab is part of the new SISSA Data Science Excellence Department and of the Research Group in Theoretical and Scientific Data Science at SISSA.
No restrictions on nationality. Deadline: Feb 19th 2021
Victor works on precision cosmology from supernovae type Ia and machine learning. He’s an expert on the large scale structures of the Universe, more specifically on galaxy evolution, and on Sunyaev-Zel’Dovich effect allowing the characterisation of the hot gas in galaxy clusters.
He enjoys applying different techniques of Machine Learning and Deep Learning algorithms to public data of the sky in optical, infrared, and microwave frequencies.
Ira is a postdoctoral researcher working on data science and cosmology. His current research focuses on Artificial Neural Networks (ANN) and their applications in likelihood component separation.
In his spare time, he enjoys studying everyday physics, teaching physics to the general public, hanging out with his kids and cooking.
Max is a PhD student in the Statistics Section at Imperial College London under supervision of Professor David van Dyk and Professor Roberto Trotta. He is interested in a broad range of Data Science problems and he is currently working on Statistical Machine Learning, Bayesian Statistics and Causal Inference methods with applications to Observational Cosmology.
In his spare time, he enjoys going sailing, skiing, playing handball and experiencing different cultures while traveling.
Roberto is a student of the Master in Data Science and Scientific Computing at the University of Trieste. Currently he is working on his thesis project with Prof. Roberto Trotta on machine learning methods for supernova Type Ia selection effects modelling.
In his free time, he enjoys programming, reading sci-fi novels and jogging.
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.
Currently a PhD student in the Astrophysics department at Imperial College London, Wahid is working with Bayesian Statistics, Machine Learning and Type Ia Supernovae to unravel the mysteries of the cosmos. Wahid’s primary research interests are in using SNIa for inference of Anisotropies and selection effects.
When away from his desk, he can often be found in a dark corner of a snooker hall, playing the piano or working up a sweat in the gym.