Profile
What I work on
I work across AI, data science, and experimental research settings where the difficult part is often not just modeling, but making the data trustworthy enough to support modeling in the first place.
That usually means dealing with synchronization, quality control, metadata, documentation, and the practical decisions that turn a study into something collaborators can actually reuse. I am most useful in projects where physiological, behavioural, and environmental data need to be brought into a clear and defensible workflow.
Multimodal data
Research engineering
Reproducible workflows
Scientific software
Approach
How I work
I prefer systems that are understandable, auditable, and durable. In practice, that means getting the protocol, data structure, and validation layer right before treating the model as the main story.
I care about outputs that remain useful after a project ends: clean datasets, documented methods, stable analysis code, and visual or written material that explains what was done and why it should be trusted.
Direction
What I want to keep building
I want to keep working on scientific data engineering, multimodal sensing, and AI-adjacent research systems that help serious experimental work move faster without losing rigor.
The kind of environment that fits me best is one where technical depth matters, but usefulness matters just as much. I am interested in work that connects careful data practice with publication-ready outputs, reusable tools, and clearer research operations.
Working Stack
How the work gets built