My technical research (and teaching) interests are three distinct yet unified areas:
- Applied Statistics/ML: How can we design better experiments, build models, and formulate hypotheses while accurately interpreting results in science? My work focuses on structured data and leveraging the probabilistic intuition behind many statistical concepts to address these challenges. I’m developing a series titled “The Eightfold Path to Understanding Statistics in Scientific Research.” Check it out here
- Computational Research Stack: What are the essential computational tools that enable scientists to quickly prototype models, create impactful results, ensure reproducibility, facilitate knowledge transfer, and minimize duplicated efforts in team environments? Details coming soon.
- Biomarker-to-Drug: Can we build a map that traces backward from recently approved drugs to understand the “Bench to Bedside” pipeline in reverse? The goal of this approach is to help scientists working with biomarker data (DNA, RNA, proteins, etc.) tailor their hypotheses and analyses to maximize the likelihood of reaching the bedside with meaningful clinical applications. Details coming soon.
Check out my previous Teaching Reviews here