Archives: Team

I hold a Bachelor’s degree in Environmental Engineering and a Master’s degree in Data Science from the University of Naples Federico II. I am pursuing a Ph.D. in Artificial Intelligence for Environmental Applications under a national program at the same university. My research interests concern the application of AI in earth observation, especially above-ground biomass and carbon stock estimation, and leveraging Artificial Intelligence to model the process of biofuel production. Additionally, I am keenly interested in physics-informed machine-learning techniques. I have been selected as a finalist in the OrbitalAI IMAGIN-e Challenge, which has allowed me to come as a visiting researcher at the European Space Agency (ESA).

I am doing a PhD in computational engineering with the Jülich Supercomputing Centre and the University of Iceland.

The objective of my work is designing algorithms based on high-performance computing for data-intensive Earth observation applications. My current research focus is quantum machine learning.

As a current PhD student at Imperial College London, Abi Riley will completing a 6-month internship in the φ-lab, in a joint project with the UNICEF Giga Data Science Team. TheirPhD works on the development of Bayesian spatiotemporal models for applications to air pollution modelling and non-communicable disease epidemiology, including the use of earth observation products as proxy data sources, predictive variables, and model covariates. Before her PhD, shecompleted her undergraduate and master’s education in mathematics, specialising in Bayesian modelling and differential equations. 

The joint UNICEF-ESA project aims to quantify the connectivity of children and schools to vital electricity and internet services, with the hope to identify priority regions and schools. Abi’s branch of the project will be on mapping and predicting electricity connectivity in Brazil and Sub-Saharan Africa, through the use of large geospatial datasets, including a number of earth observation products, socio-economic variables, population estimates, and existing energy infrastructure. Her aim is to link her PhD work in Bayesian modelling to cutting-edge models in Bayesian machine learning and AI algorithms.

I am an ambitious and enthusiastic PhD candidate with a profound passion in environmental health epidemiology. I am currently investigating the effects of temperature on people’s health in São Paulo, Brazil, focusing on identifying vulnerable groups to inform targeted policies. This is part of my PhD in Public Health at Imperial College of London, UK, in collaboration with the University of São Paulo, Brazil. I am particularly interested in the application of novel methodologies and data sources to climate change research. Prior to that, I trained in Biomedical Sciences at the Autonomous University of Barcelona and hold a first-class MSc degree in Health Promotion by the University of Girona. As a visiting researcher at the Phi-Lab, I will be exploring different machine learning approaches and EO data to produce accurate predictions of daily temperature at a high spatial resolution for the municipality of São Paulo, Brazil.

Artur Miroszewski is a postdoctoral researcher at Jagiellonian University. He obtained his Ph.D. in 2021 from the National Centre for Nuclear Research, Warsaw, Poland, in the field of theoretical physics. His doctoral thesis investigated the potential existence of quantum gravitational effects in the early universe, proposing a primordial singularity avoidance scenario known as the Big Bounce. His research also explored possible observational signatures of this scenario within the gravitational waves spectrum.

Currently, Artur is actively involved in a European Space Agency project focusing on the exploration of quantum machine learning applications for satellite data analysis. His primary focus revolves around the utilization of quantum kernel methods for classification tasks.