Education
Oct 2017 - Jun 2022
PhD in Computing: On the Structure of Learning and Transfer in Machines
Imperial College London; Supervised by Prof. Marc Deisenroth
- Thesis introduced a differential-geometric first-principles theory of learning and transfer in machines.
Reinforcement Learning
Gaussian Processes
Differential Geometry
Linear Algebra
Measure Theory
Transfer Learning
Multi-task Learning
Meta Learning
Deep Learning
PyTorch
Tensorflow
JAX
Sep 2013 - Jun 2017
MEng in Aerospace Engineering (First Class Honors)
University of Sheffield
- Thesis (supervised by Prof. Neil Lawrence) explored the use of Model-based Reinforcement Learning for control of a simulated robotic system.
Control Theory
Thermodynamics
Fluid Dynamics
Aerodynamics
Machine Learning
Materials Science
Electrical and Electronic Engineering
Differential Equations
Linear Algebra
Finite Element Methods
Computational Fluid Dynamics
Professional Experience
Apr 2024 - present
Software Engineer; Uncertainty Quantification in Financial Modelling
Signaloid Limited
C
Go
Rust
Python
Quantitative Finance
Uncertainty Quantification
Monte Carlo methods
Apr 2022 - Apr 2024
Research Associate in Probabilistic Computing
Physical Computation Laboratory, University of Cambridge
- Wrote research papers (see Publications section).
- Designed and implemented an extensible tensor operation and automatic differentiation framework in C for use in embedded systems.
- Developed an SDK for running uncertainty tracking experiments on the Signaloid Cloud Compute Engine.
- Wrote firmware for flash storage on embedded systems.
- Collaborated on topics including Control Theory (handling uncertainty in closed-loop control) and Materials Science (Lagrangian modelling of dislocation dynamics).
C
Go
Python
OCaml
Verilog
Automation scripting (Make)
Processor design
Uncertainty Quantification
Gaussian Processes
Bayesian Neural Networks
Monte Carlo methods
Embedded Systems
Aug 2018 - May 2019
Research Student Placement
PROWLER.io (now SecondMind Labs)
- Devised and implemented multi-task RL models using PyTorch.
- Wrote a paper: Disentangled Skill Embeddings for Reinforcement Learning
- Gave Gave an oral presentation at the Learning Transferrable Skills workshop at NeurIPS 2019.
PyTorch
Python
Multi-task Reinforcement Learning
Deep Learning
Variational Inference
Projects
Nov 2023 - present
Gaussian Process Predictions with Uncertain Inputs Enabled by Uncertainty-Tracking Microprocessors
Submitted to MLCNP Workshop at NeurIPS 2024
- Formulated a method using an uncertainty-tracking microprocessor to efficiently compute the Gaussian Process predictive posterior distribution on uncertain inputs.
Gaussian Processes
C
Python
Uncertainty Quantification
Monte Carlo methods
Microprocessor design
Jan 2023 - Dec 2023
The Monte Carlo Method and New Device and Architectural Techniques for Accelerating It
Submitted to MLCNP Workshop at NeurIPS 2024
- Conceived a unified view of the Monte Carlo method.
- Designed, implemented, and analysed experiments that compared Monte Carlo methods to automatic uncertainty quantification using an uncertainty-tracking microprocessor.
C
Go
Python
Uncertainty Quantification
Monte Carlo methods
Microprocessor design
May 2021 - Jun 2022
On the structure of learning and transfer in machines
PhD Thesis
Reinforcement Learning
Differential Geometry
Measure Theory
Transfer Learning
Multi-task Learning
Meta Learning
Deep Learning
Aug 2018 - May 2019
Disentangled Skill Embeddings for Reinforcement Learning
Research Student project at PROWLER.io
Refer to the Professional Experience section for details.
- Gave oral presentation at the Learning Transferrable Skills workshop at NeurIPS 2019.
Python
PyTorch
Reinforcement Learning
Transfer Learning
Variational Inference
Machine Learning
Sep 2016 - Apr 2017
Development of Machine Learning based controller for problems of control
MEng dissertation (Supervisor: Prof Neil D. Lawrence)
Refer to the Education section (MEng) for details.
- Documented experience and progress of the project on blog posts.
Python
Reinforcement Learning
Gaussian Processes
Robotics
Sep 2015 - Apr 2017
Sheffield Eco Motorsports
Co-founder and Technical Director of University engineering team
Founded (obtained funding, recruited team, etc) and led a student engineering team to compete at the Shell Eco Marathon.
- Documented 2 years of experience founding and leading the team on a blog post.
- Gave a public presentation on the car at the Sheffield Festival of Science and Engineering 2017.
Technical Director
Leadership and Team Management
Fund raising
Gaussian Processes
DC Motor design
Materials Science and Manufacturing
Jun 2015 - Jul 2015
Electronic Analogues of Regulatory Networks in Biological Systems
Sheffield Undergraduate Research Experience project
Engineered analog circuits that simulate genetic regulatory systems (supervised by Prof Nick Monk)
- Presented a poster at the Physics of Living Matter 2016 symposium.
Dynamical Systems
Genetic Regulatory Systems
Analog Computing
Analog Electronics design
Teaching, Marking and Supervision
2023
RISC-V Processor Design: Senior Demonstrator
University of Cambridge
3rd Year group project on CPU (RISC-V) design and programming FPGAs with Verilog and other tools.
- Revamped the course by streamlining the course deliverables and updating course notes.
- Streamlined student onboarding by creating a GitHub repository with clearer installation processes.
- Led lab sessions and discussions.
- Marked and graded student reports.
2022
RISC-V Processor Design: Demonstrator
University of Cambridge
3rd Year group project on CPU (RISC-V) design and programming FPGAs with Verilog and other tools.
- Led lab sessions.
- Marked and graded reports.
2021
MSc projects: Supervisor
University College London
- Supervised 2 MSc students on Machine Learning projects for Formula One (Mercedes).
2021
Machine Learning Seminars: Teaching Assistant
University College London
- Marked quizzes and held office hours.
2020
Health Data Science MSc, Machine Learning: Teaching Assistant
Imperial College London
- Marked quizzes and led tutorials.
2018
Algorithms 2: Marking Assistant
Imperial College London
- Marked assignments.
2018
Data Analysis and Probabilistic Inference: Teaching Assistant
Imperial College London
- Tutored students.
2017
Mathematical Methods: Teaching Assistant
Imperial College London
- Marked assignments and tutored students.