July 2023. Kenai Fjords National Park, Alaska.
About
I am a PhD candidate in the Computer Science Department at Purdue University. I am fortunate to be co-advised by Elena Grigorescu and Paul Valiant.
My research interests lie in theoretical computer science, across subdisciplines including approximation algorithms, online algorithms, complexity theory, and possibly learning theory. More specifically, recent projects concern learning-augmented algorithms and statistical mean estimation. I like to describe my field of interest as “classical algorithms that can facilitate or be facilitated by machine learning”.
Prior to joining Purdue, I received my master’s degree in computer science at Carnegie Mellon University in May 2020, advised by Carl Kingsford. I also received my bachelor’s degree in computer science at Carnegie Mellon University in May 2019, with a minor in discrete mathematics.
I find algorithmic science a fascinating subject, and often analogize it to architecture. Both involves constructing complex structure with simple building blocks, giving them utility and meaning in whole. I aspire to showcase this beauty and elegance of algorithmic science to others.
I am also passionate about education. I worked as a teaching assistant for multiple courses, at both Purdue and Carnegie Mellon. I was an early team member and content manager of CMU Computer Science Academy, a project aimed at developing a free online interactive K-12 level computer science curriculum.
In my free time, I take interest in game design and storytelling. I am in particular interested in exploring and experiencing video game as a media of art, and various narrative techniques of independent games. Currently, I am designing a rogue-like deck-building dungeon crawler game with a friend. As a hobbyist writer and storyteller, I am writing a homebrew world setting and campaigns for Dungeons and Dragons (D&D), and am hosting campaigns for my friends.
Last updated at 22:48 07/09/2024.