I am an applied researcher with Linkedin's Data & Artificial Intelligence Foundations (DAIF) team. I earned my PhD in Statistics at Stanford in 2021, advised by Rob Tibshirani. I am interested in all things statistics, but especially in developing statistical software, understanding how statistical analyses can impact decision-making more effectively, as well as how statistics is communicated to the public and in the classroom. More recently, I've become interested in optimization at scale, both for classical algorithms (e.g. linear programming) and for deep learning.

I have prior internship experience as a data scientist with the payments organization in Google, and as an applied scientist with and experimentation team in Amazon Search. Before that, I worked in the Singapore Government for 5 years in various roles: a data scientist at IDA Singapore's Data Science Division (now under GovTech), and public policy roles in Singapore's (then) Ministry of the Environment and Water Resources and Ministry of Defence.

I am also an avid blogger! Here are my blogs, in decreasing posting frequency:

• Statistical Odds & Ends: Short posts about concepts and ideas in statistics and related fields which interest me. This is also a place for me to clarify some of my thoughts and pen down notes for future reference.
• Mathematical Odds & Ends: As above, but for math that is non-stats.
• Beyond Solutions: Solutions to math olympiad problems and brainteasers, written with the intent of illuminating the problem-solving process.