2021 Intake Profiles

• Jackson Barr

  I graduated with a BSc in Physics at UCL, after which I obtained an MSc in Scientific and Data Intensive Computing. During my bachelor's project, I applied machine learning to the classification of Higgs boson decays. In my master's project, I worked with the cybersecurity company NCC on the classification of malicious Windows binaries. As part of the CDT, I will be working with Tim Scanlon on the improvement of b-tagging in the ATLAS experiment. I am looking forward to further developing my computational skills and the opportunity to apply these skills to a wide range of problems.

• Thandikire Madula

  I graduated with an MSci in Physics at UCL. The bulk of my master's thesis was centred on using deep learning techniques, namely generative adversarial neural networks, to model QCD backgrounds for di-Higgs decays. My PhD, with Nikos Konstantinidis, will also be on using machine learning for Higgs to 4b analyses.

• Matthew M. Docherty

  Prior to joining UCL, I obtained a first class MSci Physics with Astrophysics degree from the University of Glasgow. In my final year, I worked within the Institute for Gravitational Research looking at Bayesian likelihood-free inference techniques applied to Gravitational Waves. This project demonstrated the importance of being able to handle large amounts of data efficiently and implement bespoke machine learning pipelines, guided by intuition and experience. I chose to continue my studies at UCL's CDT for Data Intensive Science to develop my data science abilities from many different avenues in parallel. The CDT training and industry opportunities allow me to develop the practical fundamentals of data science whilst my project with Prof. Jason McEwen on simulation-based inference for Cosmology gives me the rare opportunity to carry out blue-skies research on petabyte-scale data from real dark universe surveys. The CDT path at UCL provides me all the benefits of a traditional PhD track, with outreach and teaching opportunities to prepare me for further roles in academia, whilst also offering first-hand experience in industry to train me for the data science and AI revolution that is happening in the private sector.

• Ross Dobson

  I completed my undergraduate studies at UCL, receiving an MSci in Astrophysics in 2021. My 3rd year group project focused on exoplanets, specifically transit timing variations in the orbits of "Hot Jupiters", while my MSci dissertation was on instrumentation and data analysis. I also completed an RAS Summer Internship at UCL's Mullard Space Science Laboratory, using recurrent neural networks to predict risks to infrastructure from space weather. In my project, I am combining these interests of exoplanets and state-of-the-art data science techniques. This is because while the wealth of new data from recent space missions such as Gaia, Kepler and the Transiting Exoplanet Survey Satellite (TESS) presents an opportunity to uncover new insights about the exoplanet population, the volume and complexity of the data can make it challenging to fully analyse and understand. As well as developing Machine Learning, Bayesian and Monte Carlo techniques to discover and classify exoplanets and their host stars, I look forward to employing my skills in non-academic contexts through the CDT industrial placements. Outside of my research, I am a strong advocate of science outreach: I run public tours at UCL Observatory, I teach coding to school students from disadvantaged and under-represented backgrounds, and I have worked as a teaching assistant in undergraduate observatory practical courses. The working title of my project with Vincent van Eylen is: Unveiling the exoplanet population with novel data science techniques.

• Philippa Duckett

  Before joining the UCL CDT in DIS, I completed an integrated masters in Physics at Oxford University specialising in Particle and Quantum Physics. For my masters' project, I focussed on developing machine learning approaches for event selection applied to a specific decay mode of charmed B mesons using data from the LHCb experiment. The opportunity to study at the CDT in DIS greatly appealed to me as it provides the opportunity to deepen my research interests in HEP whilst acquiring widely applicable skills in data analysis and machine learning techniques and gaining invaluable commercial experience. My research under the supervision of Tim Scanlon will involve applying machine learning techniques to improve the tracking and identification of b-jets in the ATLAS detector at the LHC.

• Elizabeth Guest

  Before coming to UCL, I graduated from Cambridge University with a BA and MSc in Natural Sciences. I studied chemistry along with mathematics and other physical sciences, specialising in theoretical chemistry for my masters. My masters' project involved trying to use machine learning to improve quantum chemistry calculations, using neural networks to predict the convergence of quantum Monte Carlo methods. Being excited by astronomy led me to this CDT. I will be applying machine learning techniques to molecular line profiles under the supervision of Jonathan Tennyson. I am excited to use data science to explore the atmospheres of exoplanets. My research title is "Machine learning of pressure dependence of molecular line profiles for Exoplanets".