2019 Intake Profiles

• Matthew Cheng

  Before joining this UCL CDT programme, I had completed an integrated masters in Physics at Imperial College London. I specialised in Space Physics in my final year and my masters' thesis focused on studying magnetic reconnection exhausts in the solar wind and Magnetosheath with Magnetospheric Multiscale spacecraft. The aim was to understand more precisely the structure and energy distribution in reconnection exhausts. The project involved creating a pipeline from data retrieval and data pre-processing to performing statistical data analysis to determine properties of these exhausts in order to predict how the properties would change under different ambient space plasma conditions. I decided to join UCL CDT in Data Intensive Science because of the data-focused courses which are super practical for both academia and industry. There are valuable opportunities throughout the CDT to apply machine learning models to both science and industry projects.

• Graham van Goffrier

  I have always viewed learning as an adventure; this philosophy keeps my interests broad, and allows my academic journey to wander freely. During my undergraduate studies at the University of Maine, I simultaneously earned a B.S. in Physics and an M.S. in Electrical Engineering, graduating as valedictorian. I gained research experience in sensor design, biophysics, and machine learning, and joined the ATLAS Experiment as a summer student with the UM-CERN-REU Program (NSF). I then moved abroad to the University of Cambridge, where I read Part III of the Mathematical Tripos, and studied optimisation on matrix manifolds as a CMP research intern. I have taken active leadership roles in numerous student-professional organisations, such as IEEE and SPS, and have recently been elected postgraduate-research representative to the MAPS Faculty at UCL. I am delighted to join UCL and this CDT programme as a theoretical particle physicist studying neutrino mass models via neutrinoless double-beta decay. The diverse and global scientific environment which UCL fosters is of strong importance to my professional engagement. I aim to combine my passions for mathematics and fundamental physics with tangible applications of modern data science to create impactful research outcomes in both fields.

• Johannes Heyl

  I graduated from Imperial College London with an MSci in Physics with Theoretical Physics. My master's thesis involved modelling double-shell designs for inertial confinement fusion capsules from which we discovered scaling properties. This work was done in Fortran with post-processing in Python. Alongside my degree requirements I have developed considerable interest in machine learning and data science, which led to a number of internships involving these. My research will involve using Bayesian statistics and neural networks to look at the formation routes of amino acids in the interstellar medium. What attracted me to the CDT in Data-Intensive Science was the interdisciplinary nature of this programme as well as the significant training component.

• Nisha Lad

  I graduated with an MSci in Physics from UCL. My Master's project confirmed that the 2D XY-Model of quench dynamics can be realised in a non-equilibrium complex condensed matter system. I investigated this by studying the properties of Vortex Dynamics simulated using a lattice structure in C++, and I am currently working on a paper to publish my results. I also have industry experience working at Oracle as a Software Engineer within their Cloud Platform. Here I implemented features in Oracle's Serverless offering and learnt how software is built to be robust & efficient at scale. I gained experience working with technologies such as Kubernetes, Prometheus QL, Fluentd & Golang, and had exposure to the wider developer community by running hackathons and presenting at meetups. Studying abroad at the University of Washington gave me the opportunity to take further courses in supervised learning and complex systems, developing my interest in neural dynamics. This led me to pursue further research in the field of ML, particularly in Data-Intensive Science. During my PhD, I will be working with Nikos Konstantinidis to investigate pattern recognition and track reconstruction using Graph Neural Nets at ATLAS. I am looking forward to enriching my skillset, by the interdisciplinary nature of the CDT, which will enable me to pursue a future career within data science.

• Petr Manek

  I graduated with two Computer Science master's degrees from Charles University and Czech Technical University. During my studies, I specialized in High Performance Computing and Artificial Intelligence. I later had the opportunity to apply my skills in physics applications at the Institute for Experimental and Applied Physics in Prague and at the ATLAS Experiment at CERN, where I focused on data analysis related to Timepix pixel detectors. Surrounded by physicists, I was inspired to pursue a PhD that would allow me to apply machine learning to computationally challenging problems in High Energy Physics. CDT in Data Intensive Science seemed like the perfect next step in this direction. At UCL, I will be working with Prof. Jennifer Thomas to build megaton neutrino detectors at the CHIPS Experiment.

• Nikolay Walters

  I graduated from UCL with an Msci in Physics where I achieved first class honours. My masters project was on identification of carbon stars using machine learning techniques. I worked with optical spectra acquired by Sloan Digital Sky Survey (SDSS) and used a decoder from a trained variational autoencoder network to aid a selection of machine learning classifiers. By the end of my project I was keen to continue with astrophysics, yet I wanted to center on the more data-driven approach to research questions. CDT in DIS proved to be a good match, allowing me to both focus on astrophysics while continuing to enrich my understanding of data and its applications in the field.

• Sam Wright

  I completed my undergraduate studies at UCL, graduating in 2017 with a first class MSci degree in Physics. My final year master's project looked at the process of classifying W bosons in the LHC's ATLAS detector. To accomplish this I took my first step into data intensive science, employing convolutional neural networks to perform this categorisation task. After graduating, I spent time working in industry for Citigroup's markets division. While there I rotated across a variety of asset classes and product types. The experience allowed me to augment my skills with knowledge of data analysis techniques and libraries commonly used in financial markets, and commercial enterprises more broadly. Now for my PhD I will be investigating Exoplanet atmospheres under the supervision of Sergey Yurchenko and Ingo Waldmann. Analysing the detectability of certain effects at play within such atmospheres and constraining their compositions are naturally very data heavy tasks - as such, UCL's CDT in Data Intensive Science provides the ideal environment and program to pursue this research.