2020 Intake Profiles

• Emily Lewis

  I graduated with a MEng in Nuclear Engineering from the University of Birmingham. For my master’s thesis I investigated the simulation of stacking faults in reactor pressure vessel steels. I then joined the Rutherford Appleton Laboratory (RAL) as a scientific developer where I modelled next generation reactor concepts and developed software for the Diamond synchrotron. After placements in the RAL Scientific Machine Learning Group and at the Culham Centre for Fusion Energy (CCFE), I became passionate about both nuclear fusion as a carbon free energy source and machine learning methods. The CDT in DIS offered a great opportunity to build my data analysis skills while contributing to this research. Under the supervision of Prof. Yiannis Andreopoulos (UCL) and Dr. Stanislas Pamela (CCFE) I am investigating the use of physics informed neural networks for plasma simulation surrogates.

• Matthijs Mars

  Before starting at UCL, I completed a BSc in Physics, a BSc in Astronomy and an MSc in Astronomy and Data Science at Leiden University in the Netherlands. For my masters' thesis, I worked at ESTEC/ESA in Noordwijk, researching the use of machine learning to predict the location of Galactic cirrus in the areas of the sky that will be in the Euclid survey. The CDT gave me the perfect opportunity to learn more about applying machine learning to astronomical problems. Currently, I am researching the implementation of deep learning methods for solving inverse problems in interferometric imaging (e.g. radio interferometry) under the supervision of Jason McEwen and Marta Betcke.

• Alexander Wilkinson

  I graduated with an MSc in Quantum Fields and Fundamental Forces from Imperial College London after completing my undergraduate studies in physics at the University of Birmingham. Whilst at Imperial, I studied topics in theoretical physics and undertook a research project on using machine learning to predict topological invariants of Calabi-Yau manifolds. I was awarded the Abdus Salam postgraduate prize upon completion of my studies. Although I enjoyed my time at Imperial, I was keen to make a more tangible and immediate contribution in the field of high energy physics. The CDT in DIS offers a unique and modern approach to this. During my PhD, I will be working on applying deep learning to detector simulation at the Deep Underground Neutrino Experiment.

• Arianna Saba

  Before starting my PhD at UCL, I obtained an MSci in Astrophysics from Royal Holloway. During my undergraduate studies, I had the possibility to perform observations of the Sun, nebulae and star clusters using the departmental telescope. For my final project, I investigated the age and temperature of the stars in the M36 open cluster through the construction of a Hertzsprung-Russel diagram. Additionally, in the summer of 2018 and 2019 I conducted an internship at INAF in Italy and at ASTRON in the Netherlands, respectively. At INAF, I performed novel research in X-ray binaries while at ASTRON I was investigating the potential for ionosphere monitoring using the LOFAR radio telescope. I extremely enjoyed doing astronomy research and working on big data problems, and the CDT DIS at UCL seemed the right fit to continue doing so while improving my data analysis skills. Currently, under the supervision of Giovanna Tinetti and Ingo Waldmann, I am investigating exoplanet atmospheres both at high and low spectral resolutions by employing data from Hubble, JWST and VLT, among other facilities.

• Patricio Reller

  Coming from a background in Computer Science (graduated with Distinction at the Technische Hochschule Ulm in Germany) and with experience as a software engineer in industry, I decided to join the CDT to deepen my expertise in my main academic focus: data-intensive and high-performance computing applied in a multidisciplinary research stage. Before joining the CDT, I worked both at CERN and at the European Space Agency (ESA), where I have seen the need and potential of my research interest. In the latter, I have written my degree thesis as part of the Astronomy Centre Machine Learning Group, where we explored applications of Deep Learning pipelines in Astronomy. Under the supervision of Prof. Ingo Waldmann (UCL) and Dr. Bruno Merín (ESA), we plan on continuing with this exploration by working on the co-funded project "Enabling data-driven searches in ESA Astronomical images for the first time with deep learning", where we aim at creating open-source tools with special focus on supporting researchers, citizen scientists and users in exploring the contents of the ESA data archives, not based on the metadata, but based on the semantic contents of the data itself. I find much value in interdisciplinary scientific teams, and I feel that the CDT aligns with these values perfectly, making it an ideal place for me to research and contribute.

• Federico Speranza

  I completed my undergraduate at the University of Milan with a first-class MSci degree in Particle and Astroparticle physics. I mostly developed my master thesis between Fermi National Accelerator Laboratory near Chicago and CERN in Geneva within the ICARUS collaboration, which aims to detect neutrino oscillations into a sterile state. After that, I spent some time working in industry for Deloitte as a data analyst. Now, my PhD project is about Space Weather, in particular, modelling electric fields and conductivity in the ionosphere under the supervision of Anasuya Aruliah. The CDT in DIS has been giving me the opportunity to start facing a new field of research, improve my computing skills and gain knowledge of machine learning and AI.