Projects


Gender Bias in the Art World


I’m currently working on a project Gender Bias in the Art World in collaboration with Alex Gates and Prof Albert-László Barabási. We are interested in measuring gender bias systmatically and find out possible reasons contribute to that. The paper is currently under preparation.

Fairness in Machine Learning


Fairness in Machine Learning has become an arising topic recently. With increasing application of Machine Learning algorithms in daily lives, people have found out those algorithms can be “unfair” since the data is biased. We recently started a project “Just ML” with Prof. Tina Eliassi-Rad and Dr. Onur Varol, trying to quantify two potential sources of “unfairness”: in-group favortism” and “out-group prejudice” using the COMPAS data. The project is still in its early stage.

Success of Books and Authors


I worked on a project Success of Books and Authors in collaboration with Burcu Yucesoy, Onur Varol, Prof Tina Eliassi-Rad and Prof Albert-László Barabási. We are interested in why some books and authors become successful.

Our first paper in this project in EPJ Data Science. We analzed the New York Times Bestseller data and found a lot of interesting pattern in it. We also have an interactive visualization website and it’s fun to play with!

Our second paper in this project is on analyzing the contribution factors that make a book successful. We build a machine learning model to predict book sales from various features. This task is challenging since book sales is heavy-tailed distributed, and traditional machine learning models are prune to underpredict books with high sales. To tackle this, we build an algorithm called Learning to Place. We also analyzed the feature importance that contribute to book’s success.

Understanding Music using Networks


Started as a course project, I initially collaborated with Syed Haque trying to understand music from the sheet music purely. We built the one-step note transition matrix for music pieces and use these matrices to cluster music; we found the matrices themselves are very distinct for Bach’s Fugue and the clusters we found aligns with music era. Check our paper!

During the Santa Fe Summer School, I pitched this music related project and collaborated with Josefine Brask, Ricky Laishram and Carlos Marcelo, trying to understand music by building higher-order networks. We came up with several metrics to quantify different charateristics of music from those higher-order networks such as “branchingness”, “repetitiveness”, etc., and try to connect them with different music genres. Here are the related slides!