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I am very excited to be a PhD student at the Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø DIS CDT and to be a part of the deep learning revolution that is driving advancements in astrophysics.

Project title: Connecting Galaxies to Large Scale Structure Environment with Minimum Spanning Trees

Research Group:ÌýAstrophysics

Supervisor(s): Prof Ofer Lahav

Introduction:Ìý

I received my MSci in Physics from Imperial College London in 2021, where I developed a passion for astrophysics and computational physics through a summer internship with the Imperial UROP programme. After graduating, I worked for a year as a Civil Service Direct Appointee at the UK Home Office in various roles, including developing a deep learning approach to process unowned and unorganised data, and serving as a Serious Violent Crime Analyst to test the effectiveness of hotspot policing.

Subsequently, I completed an MSc by Research at Durham University, focusing on Reverberation Mapping of AGN using machine learning methods. This experience enhanced my programming skills and deepened my interest in applying machine learning to astrophysical problems.

I am very excited to be a PhD student at the Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø DIS CDT and to be part of the deep learning revolution that is driving advancements in astrophysics.

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Project description:ÌýÌý

I am seeking to connect the spectral properties of galaxies in the DESI survey with their large-scale structure (LSS) environments using graph-based methods. This connection will help unveil the assembly history of the universe and how galaxies have evolved to their present morphology. The enormous scale of the DESI (~30 million galaxies) survey enables me to use big data and machine learning methods to explore the galaxy-LSS connection in ways not previously possible.

I am also part of a DESI project that uses spectral data to detect the gravitational redshift effect from galaxy clusters to test general relativity and alternative theories of gravity.

First year group project:ÌýAdvances in proton beam therapy have enabled the precise targeting of brain tissue which could mitigate the severe neurocognitive side-effects that radiotherapy has on brain tumors, especially during brain development. In this vein, I worked with the Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø Medical Physics and Biomedical Engineering department to successfully develop a biologically informed deep neural network to establish the connection between gene expression data and radiation sensitivity, which would help facilitate optimal targeting during radiotherapy

Placement:Ìý

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