I research ML at Rice University in the department of Computer Science, where I work with Prof. Anshumali Shrivastava and Prof. Anastasios Kyrillidis. I am interested in Machine Learning, and more recently semisupervised learning, optimization, and GANs.

In 2018, I interned at 3Red Trading, where I applied Machine Learning to trade equities. Since 2017, I’ve been working on Valhalla Healthcare, a startup aiming to reduce physician burnout by automating documentation, augmented with Machine Learning.

You can contact me at johnchen@rice.edu

Publications

ImCLR: Implicit Contrastive Learning for Image Classification. Chen, J. and Sinha, S. and Kyrillidis, A. Preprint. [arxiv]

STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge. Coleman, B. and Gupta, G. and Chen, J. and Shrivastava, A. Preprint. [arxiv]

Revisiting Consistent Hashing with Bounded Loads. Chen, J. and Coleman, B. and Shrivastava, A. In AAAI, 2021. [arxiv]

Negative sampling in semi-supervised learning. Chen, J. and Shah, V. and Kyrillidis, A. In ICML, 2020. [arxiv]

Decaying Momentum Helps Neural Network Training. Chen, J. and Kyrillidis, A. In NeurIPS opt-ml workshop, 2019. [arxiv]