Research Blog

The bioinformatics community faces a sustainability crisis: graduate students must publish novel tools to graduate, but no one funds long-term maintenance. This creates an ecosystem of abandonware that breaks microservices architectures and wastes researcher time. Without wholesale funding reform, we need to raise our engineering standards and lower the bar for what counts as 'publishable.' This post examines what bioinformatics can learn from mature open source communities and offers practical steps toward more sustainable software development.

In fields like microbial ecology, agriculture, and molecular biology, the practice of 'waiting to see what the data say' is often justified as embracing discovery. But without rigorous upfront design—particularly computational modeling to guide measurement strategies and statistical approaches—this flexibility masks p-hacking and arbitrary choices. This post argues that modeling, preregistration, and honest distinction between exploratory and confirmatory work aren't constraints on discovery; they're prerequisites for credible science. Targeted at computational professionals and data scientists, it offers practical guidance for designing rigorous studies, collaborating with wet-lab teams, and building institutional cultures around methodological transparency.

An exploration of language maximalism in scientific computing and the benefits of strategic polyglot programming approaches

This inaugural post introduces my research focus areas and the types of content you can expect from this blog, covering bioinformatics methodologies, data science writ large, and computational methods in biotechnology.