I’m a data scientist in the Ecology and Evolutionary Biology (EEB) department at Princeton University. While my interests and expertise are in software engineering, statistics, machine learning, and analyzing large datasets, I’m most excited to apply these skills to EEB questions that help us understand the origins and diversity of life.
As a departmental data scientist, I
- analyze data and do research directly with faculty
- develop computational workflows that enable or accelerate several projects
- teach workshops
- mentor individual students and postdocs
- serve as consultant
I am also affiliated with the Center for Statistics and Machine Learning, which creates inter-departmental connections between scientists at Princeton.
Highlights
- As a former data scientist in Princeton’s Computer Science department, I discuss my role in the larger academic community.
- Here is a talk of my work on “Learning mixtures and DNA copy-numbers from bulk sequencing of tumor samples”
- I was an invited guest on the Nice Genes! podcast for our work on tuskless African elephants.
Background
For the past 5 years, I have worked as a staff scientist in academia.
Two of those years I spent at Harvard Informatics, creating workflows for analyzing genomic datasets, researching with faculty, and teaching introductory workshops on bioinformatics.
I then moved to Princeton University where I spent 3 years as a biomedical data scientist as part of the DataX initiative. At Princeton, I worked in the Computer Science department doing research on cancer genomics and teaching workshops on software engineering and machine learning. Through these experiences, I learned to think about data in more general ways beyond my specialization in genomics. I now have a broad interest in algorithms, statistics, and machine learning, and I’m interested in applying these skills to any kind of data that could be useful to address important questions. However, while data science skills are incredibly transferable, it’s always best to work with someone who has domain expertise in the data you’re analyzing!