“Music, with its complex hierarchical and sequential structure and its inherent emotional and aesthetic subjectivity, is an intriguing research subject at the core of human creativity,” said Chen. “And because of rapid advances in data-driven algorithms such as deep learning, exploring computational creativity via machine learning approaches is increasingly popular.”
While this exploration has included some work on generative models for music, research that investigates the capabilities of deep learning for creative applications such as style transfer on images and video is limited, he said.
The paper, “Learning to Fuse Music Genres with Generative Adversarial Dual Learning,” aims to fill this space by exploring the idea of style fusion in music with generative adversarial dual learning. Chen presented the paper at the 2017 IEEE International Conference on Data Mining (ICDM) in New Orleans, Louisiana.
In November, Chen will attend ICDM 2018 in Singapore, where he will present “Rational Neural Networks for Approximating Jump Discontinuities of the Graph Convolution Operator,” a study on deep graph learning.
“Effective information analysis generally boils down to the geometry of the data represented by a graph,” said Chen. Typical applications include social networks, transportation networks, spread of epidemic diseases, neuronal networks, biological regulatory networks, telecommunication networks, and knowledge graphs, defined over non-Euclidean graph domains.
Chen’s research in this area focuses on modeling graphs in spectral domains for deriving representations for node level embeddings. He uses graph notions such as adjacency matrices or graph Laplacians to describe geometric structures and reveal latent patterns.
He holds a bachelor’s degree in software engineering and Japanese from Huazhong University of Science and Technology, China, and a master’s degree in software engineering from Peking University, China.
Advised by Chang-Tien Lu, Chen is projected to graduate with a Ph.D. in computer science in Fall 2019.