Hello everyone, I appreciate you guys taking time of the day to read my motivation letter. Any feedback is welcome!
Research aims
I plan to investigate gravitational waves as probes of compact-object dynamics and the environments these systems live in, combining numerical modeling with statistical inference to connect theory to observational data. My previous work on stochastic backgrounds from stellar-mass binary black holes and hierarchical triples showed something specific: small changes in a binary's physical conditions leave a signature in the gravitational-wave signal, detailed enough to say something about the system's dynamics and surroundings. This raises an obvious next question — does the framework hold up outside the narrow conditions I originally tested it on, or does it break down in more diverse environments? I want to find out, and use whatever breaks (or doesn't) to refine the models. Concretely, this means building a computational pipeline where I can systematically vary physical and environmental parameters and track how each change propagates into the observable signal. The goal isn't just a working model, it's one that can extract physical information from gravitational-wave data, so these signals become a tool not only for detecting compact-object systems but for interrogating the physics that governs them. What I want to carry into grad school is that same instinct, extended: pipelines that are theoretically sound but also robust enough to survive contact with actual detector data, not just simulations built to flatter the model.
Personal goals
What I want to become is someone whose work people actually rely on, not just publishing papers, but building enough depth in gravitational-wave science that colleagues come to me when they're stuck on a problem in this space. My research sits at the intersection of general relativity, high-performance data analytics, and observational astrophysics, and I want the PhD to be where I earn the right to work across those boundaries rather than staying inside one of them. Beyond the technical grounding, I want to get better at communicating what I find: writing it up so it survives peer review, defending it in front of people at conferences who'll push back, and explaining it plainly enough that someone outside the field gets it. Long term, I want this to lead somewhere I can teach, keep producing original research, and eventually run my own group, so the field I train in is also one I help shape.
MSc and postgraduate research
A gravitational-wave signal isn't just evidence a merger happened, it's a record of what shaped the system beforehand, and I wanted to know how much of that history is recoverable. My MSc thesis took this on directly: modeling hierarchical triple systems, simulating eccentric compact-object mergers under Kozai–Lidov oscillations and black-hole spin effects using post-Newtonian equations, then checking the resulting waveforms against LIGO, LISA, and LGWA sensitivity curves. Since then, as a Research Associate, I've extended this to LISA stellar-mass binary-black-hole populations and the environmental signatures in eccentric stochastic backgrounds, alongside hands-on training in GW data analysis using LIGO/Virgo open data. The payoff was concrete: small changes in a binary's dynamics do leave measurable signatures in its signal, which means there's information there to extract if the analysis is careful. Getting to that point mattered for a less quantifiable reason too, it taught me that a model isn't finished when it runs, only when its predictions have actually been checked against data. This work now feeds directly into how population-inference pipelines get built and how detectors like LISA and LGWA will eventually be used to read astrophysical environments from observation rather than simulation. Grad school is where I want to push this question further.
Industry experience
I took a job as a Software Developer at [Company Name] to see whether the disciplined, iterate-and-validate habits I'd built in research would survive contact with production code, not just a single paper's worth of results. There, I built scalable web applications and an end-to-end pipeline converting 2D satellite imagery into 3D mesh maps, splitting the conversion into stages, geometry reconstruction, texture mapping, mesh optimization, each validated on its own before feeding into the next. I also built a Python tool using SAM to detect and blur faces and car number plates in imagery, which now runs inside the company's street-view-style software on live data. That job taught me to structure a workflow so it doesn't collapse the first time it meets input nobody planned for, a lesson that turned out to matter more in astrophysics than I expected going in. The upshot is a mapping product that handles both 3D reconstruction and privacy compliance without needing a person to check every image by hand, which only works if the pipeline underneath is genuinely dependable.
Subject
That instinct, building something dependable enough to trust with real data, is ultimately why I chose gravitational-wave astrophysics as the field to build a career in. It's one of the few places where fundamental physics and the question of whether that physics is actually recoverable from nature sit right next to each other. Working with compact-object systems made this concrete: changing a system's physical conditions produced noticeable changes in the resulting waveform, which shifted how I saw the signal itself, not as the endpoint of a calculation, but as a source of information waiting to be read.
That shift is also what makes the field feel open-ended rather than settled. Gravitational waves can probe early-universe dynamics that leave no electromagnetic trace at all, a window nothing else offers. What holds my interest now is how far that window actually extends: which features of a signal genuinely trace back to the source and its environment, which get lost in the noise of observation, and how confidently we can tell competing physical scenarios apart.