I am a PhD student at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge as part of the UKRI Centre for Doctoral Training in the Application of Artificial Intelligence for Environmental Risk and a resident member of Pembroke College.
I am supervised by Professors Peter Haynes FRS and Rich Kerswell FRS as part of the Atmosphere-Ocean Dynamics research group.
My research interests lie in:
- Deep Learning
- Atmospheric and Oceanic Dynamics - primarily Zonal Jets
- Nonlinear and Chaotic Systems
- Stochastic Dynamical Systems
My research is currently focused on using Machine Learning to determine the predictive nature of turbulent fluid flows, specifically investigating the Beta-Plane System - an idealised model analogous to the tropospheric mid-latitudinal regions of our atmosphere, that govern jet streams.
We are using Machine Learning to learn the dynamics of the system in order to produce a forecast or long-term emulation that can dramatically reduce the associated computational cost compared to numerical integration. In learning the underlying dynamics we use Machine Learning to find a Reduced Order Model to find the intertial manifold that the system lies on, however, the Beta-Plane System is a stochastically forced system with a non-stationary manifold, requiring a probabilistic map - more information can be found here.
My faculty homepage can be found here.
A copy of my CV can be found here.
Data-Driven Exploration of Mid-Latitude Weather - Report
- Used a Autoencoder to explore whether a beta-plane turbulence model of tropospheric mid-latitude circulation lay on an internal manifold.
- Looked to evolve the dynamics of the model in the reduced latent space, before exploring the variability of the system due to its stochastic parameterisation scheme.
- Supervisors- Professors Peter Haynes and Rich Kerswell.
Python: Keras, Tensorflow; MATLAB ; LaTeX- Repository
Assessing Temporal Change In The Exposure Of Informal Settlements Through Repeat Satellite Observation - Group Project - Report
- This project focused on assessing change in the exposure of Caribbean informal settlements over time to natural hazards.
- Informal settlements were located through segmentation of satellite images using a Random Forest model as well as Deep Learning models.
- We then used Deep Learning to identify changes to settlement sizes and to quantify vulnerability. For example, following a disaster, change detection algorithms aim to determine the extent of damage suffered (e.g. destroyed, majorly damaged, undamaged).
Quantifying the effecitiveness of natural hazard preventions by using an LSTM to predict rainfall runoff in flood risk mitigation - Group Project
- Project to investigate the effectiveness of natural flood management interventions undertaken in the town of Shipston-on-Stour during 2017 to 2020 using an LSTM model.
Deep Learning Robustness for Neutrino Event Detection using Adversarial Neural Networks - Bachelor's Thesis - Report
- Used a Domain-Adversarial Neural Network (DANN) to improve the performance of a Convolutional Neural Network (CNN) to classify neutrino interactions, for the analysis of neutrino oscillations.
- This method looked to produce a model that is invariant to the differences in statistics between the input data (the labeled Monte Carlo simulations used to train the classifier) and the detector data.
- Supervisor- Dr Chris Backhouse.
Python: Keras, Tensorflow; C++: Root, NOvAsoft; Scientific Linux; LaTeX- Repository
HPGe Detector Gamma Ray Spectroscopy simulation of nuclear emission and subsequent detector interactions - Group Project Report
Cellular Automata Model to Simulate Traffic Flow's Similarities to Granular Flow - Report
- Used a Cellular Automata model to simulate motorway traffic flows, in order to compare the similarities to the granular flow, turbulence and choked flow when traffic shockwaves arise.
- The model consisted of a few rules with the system was able to evolve over time with a stochastic element put in place to represent human decision making and irrationality, and was extended to contain different vehicles with different maximal speeds, blockages such as accidents or road closures to try and model a driverless car system.
- Supervisor- Professor David Bowler.
Chatbot to translate text in Facebook Messanger Hackathon - Developer Circles from Facebook AI Messanger Hack
- I was selected to represent Univerity College at the AI for Messenger Hackathon where we created a chatbot that returned the translated text from an image containing text in a different language. Used
Node.jsfor the messenger front end, with
Flaskconnecting to the
Pytorchmodels, which comprised of a CNN to determine the locations of the words, an OCR CNN to recognise the text, and a translation neural network.
- Image 1; Image 2; Image 3; Image 4; Image 5.
Providing insight from credit card customer datasets Hackathon - Winning Hackathon Team - University College London Data Science Society Hackathon, hosted by Microsoft and American Express
- I was part of the winning team, where we produced a solution concluding that that product personalisation for customersubsets could increase credit card growth while assessing potential credit default and delinquency risk. We conducted exploratory analysis through k-means clustering and build decision tree and random forest models using
Azure API in
- Image 1; Image 2; Image 3.
Adaptive Image Filter Challange - Winning Hackathon Team - Applied Machine Learning Insight Challange at Arm Holdings
- I was part of the winning team that completed a
Pythondebugging challenge applying an adaptive image filter to a webcamimage using a CNN during an insight into the research being conducted by ARM in the fields of computer vision and natural language processing for mobile devices.
Image Matching Game - Microsoft AI Mini Hackathon - Microsoft Reactor
Supervisions, University of Cambrdige
2021 - Part II Computer Science - Deep Neural Networks.
Other Relevant Experiance
Pembroke College, University of Cambrdige
June 2022 - Preseent, Housing Officer, Graduate Parlour.
- Elected to represent the gradute student body of the college on all matters relating to housing and accomodation.
June 2021 - June 2022, President, Graduate Parlour.
- Elected to represent the gradute student body of the college, as well as lead the Graduate Parlour committee.
Oct 2020 - June 2021 - Events Officer, Graduate Parlour.
- Elected to organise events, large and small, that will appeal to all aspects of the college community.
- This included online events as well as following, and adapting to, Covid protocols to ensure all in-person events are run safely and within guidelines.