About me

I graduated with a B.S. in applied mathematics from UCLA in 2020, and I am now a PhD student and presidential scholar in the computational social science program at George Mason University (Computational and Data Sciences department). My interests are in using data, networks, GIS, and computational approaches to study urban systems as well as social problems pertaining to cities. I am also keen on developing computational tools to help other scientists do their work.

If inclined, you can read more about some of the things I have done and what I am currently doing below.

Connect with me:


Research

Currently, I am working on a few research projects. However, my mind has most been occupied with urban systems. In particular, with my PhD advisor, Dr. Eduardo Lopez, I am studying the estimation of flows on urban transit networks on the basis of an discrete algebraic topology technique called Hodge decomposition. Moreover, I am also spending a lot of time thinking about mobility, social structures, and spatial distributions of economic resources in cities.

In the past, I worked on networks-related project such as a co-evolving bounded confidence model for opinion dynamics on networks with a focus on homophilic rewiring. I am grateful to have received guidance from Professor Mason Porter and Dr. Michelle Feng for this project. I also worked with collaborators on the mathematics of diversity and intersectionality at the Institute for the Quantitative Study of Inclusion, Diversity, and Equity (QSIDE).

Academics

My research interests draw from many fields, so I take courses from many disciplines at GMU ranging from computational social science and complexity theory, geography, economics, networks, to urban planning. I am very fortunate to be taking some of these courses with professors who are pushing the field of computational social science, e.g. Dr. Rob Axtell and my advisor.

At UCLA, I tried to take as many courses as I can in data science and modeling, and get involved in a reading program or independent research. During my undergrad I gave three machine learning presentations on my independent studies. If this sounds like it might be of interest to you, you can see them here.

I also received sponsorship from UCLA to participate in COMAP’s Mathematical/Interdisciplinary Contest in Modeling in March 2020.

Industrial experience

Outside of the academic settings, I have had some related engineering experience. In particular, I have had two internships in data analysis and data science at one of the biggest FinTech companies in the US. I dealt directly with real data using Python, SQL, ML models, and other data tools. During these internships, I gained experience with data visualization, text analysis, and building classification models. I also built some in-house data tools for the company, notably a Python library on top of sklearn for speeding up ML model building and evaluation. I used SQL heavily during these internships. (See my professional resume for more info.)

I have an associate’s degree in computer science and in the past I consistently participated in hackathons where I worked on data science projects to develop my software engineering and data handling skills.

Other fun stuff

In my free time, I like to read about cities, economics, and politics. I also enjoy popular math books. I occasionally write about math and data science topics on this blog and some of them on Medium.com. I plan to write more articles in 2022 on urban things I find interesting.

At UCLA, I was part of the competitive women’s ultimate frisbee team (our program has returned to the D-I College National Championships 11 times out of the 19 years we’ve existed, go BLU!).  In addition, I was a practice player with the Los Angeles professional women’s ultimate team Astra.

I was involved in the Women in Math group, as well as the Networks Journal Club at UCLA. I am a supporter of women in academia, tech, and sports.