AI in healthcare,
from scientific depth to real-world systems.

During the rapid expansion of single-cell technologies, analytical complexity outpaced the capabilities of existing tools. Researchers relied heavily on manual gating and ad hoc workflows, limiting reproducibility and scalability. There was a clear gap between what the data contained and what the software ecosystem could extract in practice.

Over the past decade, I have maintained a personal blog focused on machine learning, statistical analysis, and scientific programming. The goal was not content production, but to externalize thinking, working through real problems, documenting approaches, and making complex analytical concepts accessible through concrete examples. The work spans early-stage data exploration, statistical testing, and machine learning workflows.

LLM-based knowledge systems often underperform in real-world environments. Not primarily due to model limitations, but because of a persistent gap between what the system returns and what users actually expect. In enterprise contexts, this gap is rarely measured. Feedback is unstructured, inconsistently captured, and not integrated into system improvement. As a result, systems stagnate despite continuous model iteration.