Education
MSc in Computational Statistics and Machine Learning, University College London
September 2019 to October 2020
Distinction (91%)
- Advanced deep learning and reinforcement learning courses via researchers from DeepMind.
- Extensive hands-on experience with autodiff libraries including PyTorch, JAX, and TensorFlow.
- Chief Student Data Scientist at UCL’s Data Science Society, leading a team of student data scientists to contribute to real-world ML projects.
- Research project in Bayesian deep learning within the UCL Centre for AI; focused on recent SGD-based sampling techniques, supervised by Hippolyt Ritter and Prof David Barber.
BEng in Mathematics and Computer Science (Joint Hons.), Imperial College London
October 2014 to July 2017
First; Dean’s List 2017 (top 10% academic performance)
- Engineering experience in a range of languages, including C, C++, Java, Haskell, Javascript, and Python.
- Programming projects included creating a compiler, an OS, a proof assistant for category theory, a multiplayer online game, and a handwriting recognition app using deep learning.
- Strong foundations in mathematics and computational theory, including real, complex and functional analysis, linear and abstract algebra, probability, logic, and models of computation.
Experience
Research Intern, Apple Cambridge, UK
May 2021 to Present
- Research project in representation learning within the Neurosymbolic Reasoning team, leveraging tools from kernel methods to analyse various self-supervised algorithms.
Research Assistant, UCL Centre for Artifical Intelligence London, UK
Nov 2020 to May 2021
- Research project in model-based reinforcement learning within Prof David Barber’s group, using partially Bayesian neural networks to do active exploration in sparse environments.
- Leading design and development of our research codebase in Python and PyTorch.
Machine Learning Intern, Thread London, UK
May 2019 to September 2019
- Improved core models powering Thread’s style recommendation engine through feature engineering.
- Conducted data analysis to find predictive signals, created visualisations to present findings.
- Developed model training infrastructure to support iteration of ML solutions.
Data Science Immersive, Flatiron School New York, USA
October 2018 to March 2019
- Collaborated on numerous data analysis and ML projects using sklearn, NumPy, Pandas, etc.
- Capstone project: created a shoe recommendation engine using deep representations of product images, as well as a proof-of-concept GAN for creating new products from user browsing preferences.
Software Engineer, Barclays Northampton, UK
March 2018 to August 2018
- Hands-on experience with software development practices and continuous integration.
- Built microservices for Barclaycard’s machine learning fraud teams using Java and Spring.
- Designed custom APIs according to requirements of multiple internal client teams.