This is the part of my work that maps most directly to research-engineer and applied-scientist roles. It combines current NLP work with graph-aware representations and a practical emphasis on experiment design, comparative baselines, and measurable iteration.

In practice, I take open-ended modelling questions, turn them into tractable workflows, and produce evidence that other researchers or technical teams can build on.

This work takes spectral methods into a more engineering-facing direction by paying close attention to runtime, large-network behaviour, and the practical cost of experimentation.

I approach these graph problems as modelling tasks that need tractable algorithms, careful comparisons, and clear reasoning about trade-offs.

This work matters because the datasets are not clean benchmarks. They are clinically difficult, high-dimensional, and noisy, which makes the experimental discipline itself a transferable skill.

It also reflects how I work on applied research problems: when standard tools do not match the structure of the data, I adapt methods carefully and validate them through domain-grounded experiments.

This work gave me experience turning national-scale data into analysis that policy teams could use directly. It required modelling judgement, reproducibility, and communication with non-technical audiences.

The scale mattered as well: I worked with nationwide assessment and census datasets, built dashboard workflows for policy teams, and helped shorten analysis-to-decision cycles from weeks to hours.

This mathematical work is the foundation behind much of my later graph-learning and graph-signal research.

For research-heavy teams, this work reflects the way I reason about structure, assumptions, and algorithmic trade-offs instead of treating graph methods as black boxes.