Projects

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.

Impact — Enabled researchers to move from manual, heuristic-driven analysis to more reproducible and scalable workflows. The plugin remains in use and has supported multiple published studies, contributing to broader adoption of more rigorous analytical approaches in single-cell research.

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.

Impact — Built a body of work over 10+ years covering applied machine learning and data analysis, with articles used by practitioners in research and applied settings. More importantly, this work established a strong foundation in translating complex analytical concepts into usable frameworks, a capability that later extended into product, consulting, and AI system design.

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.

Impact — Initial iterations resulted in a consistent 10–15% improvement in response relevance across multiple knowledge managers. More importantly, the system introduced a scalable mechanism for continuous alignment between AI output and user expectations, addressing a core failure mode of enterprise AI systems.