About
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 member of Pembroke College.
I am supervised by Professors Peter Haynes FRS and Rich Kerswell FRS.
My research interests lie in:
- Deep Learning
- Atmospheric and Oceanic Dynamics
- Nonlinear and Chaotic Systems
My research is currently focused on using machine learning to determine the predictive nature of fluid flows within the beta-plane jet system - a model analogous to the Tropospheric Mid-Latitude regions of our atmosphere. Understanding how predictable the system is will inform how a machine learning emulation of the model parameterisations could be built- which could lead to forecast models being developed at higher spatial resolutions or with the addition of greater model complexity, both while reducing the assocaited computational cost. More information can be found here.
My faculty homepage can be found here.
A copy of my CV can be found here.
Projects
Academic Research
Data-Driven Exploration of Mid-Latitude Weather - Report
2021
- 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
2021
- 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).
Python- Repository
Quantifying the effecitiveness of natural hazard preventions by using an LSTM to predict rainfall runoff in flood risk mitigation - Group Project
2020
- 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.
Python- Repository
Deep Learning Robustness for Neutrino Event Detection using Adversarial Neural Networks - Bachelor's Thesis - Report
2020
- 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
2020
- Supervisor- Professor Ruben Saakyan.
C++: GEANT4; Cent OS; LaTeX- Repository
Cellular Automata Model to Simulate Traffic Flow's Similarities to Granular Flow - Report
2019
- 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.
Python- Repository
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Hackathons
Chatbot to translate text in Facebook Messanger Hackathon - Developer Circles from Facebook AI Messanger Hack
2019
- I was selected to represent UCL 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, withFlaskconnecting to thePytorchmodels, 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.
- Repository.
Providing insight from credit card customer datasets Hackathon - Winning Hackathon Team - UCL Data Science Society Hackathon, hosted by Microsoft and American Express
2019
- 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
Scikit-Learnand theAzure API inPython. - Image 1; Image 2; Image 3.
Adaptive Image Filter Challange - Winning Hackathon Team - Applied Machine Learning Insight Challange at Arm Holdings
2019
- 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.
