I am a student at Imperial College London pursuing a PhD focused on developing machine learning techniques for the extraction of clinical insights from in-home monitoring data, focusing on people living with Dementia.
My interests are in theoretical machine learning as well as building new techniques that are applicable to healthcare.
I am a part of the Translational Machine Intelligence Lab, supervised by Payam Barnaghi.
I am also interested in photography, both film and digital.
Some of the publications I have worked on are listed below:
Developing machine learning techniques to improve Dementia care through the use of in-home sensors.
Supervised by Rahul Krishnan: Studying the use of language models in building probabilistic predictive models.
This was funded through the Turing Global Fellows Fund.
Machine Learning, Data Science, Computational Mathematics, Dynamic Systems, Asymptotic Analysis, General Relativity, Vortex Dynamics, Applied Complex Analysis.
Numerical Methods, Probability, Statistics, Mathematical Methods, Financial Mathematics, Algebra, Partial Differential Equations General Relativity, Measure Theory, Complex Analysis, Real Analysis, Fluid Mechanics.
Prepare and present materials, instruct in labs, give tutorials on Pytorch, and mark coursework for the Machine Learning for Neuroscience course.
Prepare materials and tutor in GCSE and A Level Mathematics.
Part of the data and analytics graduate program, I gained experience in the data science, analytics, machine learning, and risk management teams.
This is a python module that was developed based on the work "AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling".
The module provides a framework for using large language models (LLMs) to elicit expert knowledge in predictive modeling tasks. It includes tools for generating prior distributions.
This is a module I am a assistant teacher for at Imperial College London for the MSc Computational Neuroscience.
This module introduces students to the application of machine learning techniques in neuroscience. It covers topics such as neural data analysis, predictive modeling, and the use of deep learning frameworks like PyTorch. The course website provides additional resources and materials for students.
Some of my graphing code, inspired by seaborn's API.
Other projects can be found on my GitHub.